Pricing machine: VM instances pricing  |  Compute Engine: Virtual Machines (VMs)

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VM instances pricing  |  Compute Engine: Virtual Machines (VMs)

This page describes the cost of running a Compute Engine VM instance
with any of the following machine types, as well as other VM instance-related
pricing. To see the pricing for other Google Cloud Platform products, see
the GCP pricing list.

Note: This page covers the cost of running a VM instance. It does not cover
pricing for any disk and images,
networking costs, or the cost of any
sole-tenant or GPUs
used by the VM instance.

Compute Engine charges for usage based on the following price sheet. A bill
is sent out at the end of each billing cycle, listing previous usage and
charges. Prices on this page are listed in U.S. dollars (USD).

For Compute Engine, disk size, machine type memory, and network usage
are calculated in gigabytes (GB), where 1 GB is 230 bytes. This unit of measurement is also known as a gibibyte (GiB).

If you pay in a currency other than USD, the prices listed in your currency on
Cloud Platform SKUs apply.

You can also find pricing information with the following options:

Billing model

The following billing model applies to all vCPUs, GPUs, and memory resources. The
billing model also applies to several premium images that
you run on Compute Engine instances.

  1. All vCPUs, GPUs, and GB of memory are charged a minimum of 1 minute.
    For example, if you run your virtual machine for 30 seconds, you will be
    billed for 1 minute of usage.

  2. After 1 minute, instances are charged in 1 second increments.

Instance uptime

Instance uptime is measured as the number of seconds between when you start an
instance and when you stop an instance, the latter being when the instance
state is TERMINATED. In some cases, your instance can suffer from
a failure and be marked as TERMINATED by the system; in these
cases, you will not be charged for usage after the instance reaches the
TERMINATED state. If an instance is idle, but still has a state
of RUNNING, it will be charged for instance uptime. The easiest
way to determine the status of an instance is to use
gcloud compute with the
gcloud compute instances list command or to visit the Google Cloud Console.

In the case of reservations,
instance uptime is measured as the number of seconds between when you create a
reservation and when you delete that reservation. Reserved resources are billed at
standard rates, whether they are started or not.

Note that Compute Engine bills for a minimum of 1 minute of usage, so if
you use an instance for 30 seconds of uptime, you are billed for 1 minute.
After 1 minute, your instance is billed on a per-second basis. For more
information, see the billing model.

Note: If you are a Microsoft licensee with a contract that includes Software
Assurance, you might be able to move your existing SQL Server licenses to
Compute Engine, instead of paying a per-hour license fee. To
find out more information about License Mobility, see the documentation for
Using Existing Microsoft Licenses.

Resource-based pricing

Each vCPU and each GB of memory on Compute Engine is billed
separately rather than as part of a single machine type.
You still create instances using
predefined machine types, but your bill reports
them as individual vCPUs and memory used per hour.

Resource-based pricing allows Compute Engine to apply
sustained use discounts to
all of your predefined machine type usage in a region collectively rather than
to individual machine types.

vCPU and memory usage for each machine type can receive one of the
following discounts:

Discount types cannot be combined. Preemptible VM instances cannot receive
sustained use discounts or committed use discounts.

The following sections describe prices for machine types based on vCPU and
memory resources, but also include the calculated cost for each machine type.
You can also use the
Google Cloud Pricing Calculator to better understand
prices for different configurations.

General-purpose machine type family

General-purpose machine-types offer predefined and custom machine
types in each region. Predefined machine types have a preset number of vCPUs and
amount of memory, but are billed using the resource-based pricing
model. Custom machine types are billed according to the resource-based pricing.

For N1, N2, and C2 machine types, Compute Engine provides automatic
sustained use discounts for all of the predefined vCPU and memory resources that
you use in a region. Sustained use discounts for predefined machine types are
calculated separately from custom, memory-optimized, compute-optimized, and
shared-core machine types. Depending on the machine type, sustained use
discounts differ between N1 and N2 machine types:

  • N1 machine types can receive a sustained use discount up to 30%.
  • N2 machine types can receive a sustained use discount up to 20%.

E2 machine types do not offer sustained use discounts but provide
larger savings directly through the on-demand and committed-use prices. E2
machine types provide consistently predictable pricing without the requirement
to run a VM for a specific portion of the month.

For more information, see Sustained use discounts.

Not all machine types are guaranteed to be available in all zones all the time.
To ensure that a machine type is available when you need it, you can preemptively
reserve the machine type in a certain zone. For information about reserving
predefined machine types in a specific zone, see
Reserving zonal resources.

E2 machine types

E2 machine types do not offer sustained use discounts but provide
larger savings directly through the on-demand and committed-use prices. E2
machine types provide consistently predictable pricing without the requirement
to run a VM for a specific portion of the month.

E2 standard machine types

The following table shows the calculated cost for standard predefined machine
types in the E2 machine family. The vCPUs and memory from each of these
machine types are billed by their individual
predefined vCPU and memory prices, but these tables provide
the cost that you can expect using a specific machine type.

Standard machine types have 4 GB of memory per vCPU.

E2 high-memory machine types

The following table shows the calculated cost for the E2 high-memory predefined
machine types. The vCPUs and memory from each of these
machine types are billed by their individual predefined vCPU and memory
prices, but these tables provide the cost that you can expect
using a specific machine type.

High-memory machine types have 8 GB of memory per vCPU. High-memory instances
are ideal for tasks that require more memory relative to virtual CPUs.

E2 high-CPU machine types

The following table shows the calculated cost for E2 high-CPU predefined machine
types. The vCPUs and memory from each of these
machine types are billed by their individual predefined vCPU and memory
prices, but these tables provide the cost that you can expect
using a specific machine type.

High-CPU machine types have one vCPU for every 1 GB of memory. High-CPU
machine types are ideal for tasks that require moderate memory configurations
for the needed vCPU count.

E2 custom vCPUs and memory

Custom machine types let you set a specific number of vCPUs and GB of
memory for your instances to match the needs of your workload.
Custom machine types save you the cost of running on a larger and more
expensive machine type if your application does not require all of the resources
provided by that machine type.

E2 shared-core custom machine types are subject to the same pricing rate as E2
custom machines. These instances have fractional vCPUs with a custom memory
range.

  • 0.25 vCPU for micro machines
  • 0.50 vCPU for small machines
  • 1 vCPU for medium machines

Read
Creating a VM instance with a custom machine type
to learn how to use these machine types.

Not all machine types are guaranteed to be available in all zones all the time.
To ensure that a machine type is available when you need it, you can preemptively
reserve the machine type in a certain zone. For information about reserving
predefined machine types in a specific zone, see
Reserving zonal resources.

For an accurate estimate of your billing with custom machine types, use the
Google Cloud Platform Pricing Calculator.

N2 machine types

N2 standard machine types

The following table shows the calculated costs for standard predefined machine
types in the N2 machine family. The vCPUs and memory from each of these
machine types are billed by their individual
predefined vCPU and memory prices, but these tables provide
the cost that you can expect using a specific machine type.

Standard machine types have 4 GB of memory per vCPU.

N2 high-memory machine types

The following table shows the calculated cost for the N2 high-memory predefined
machine types. The vCPUs and memory from each of these
machine types are billed by their individual predefined vCPU and memory
prices, but these tables provide the cost that you can expect
using a specific machine type.

High-memory machine types have 8 GB of memory per vCPU. High-memory instances
are ideal for tasks that require more memory relative to virtual CPUs.

N2 high-CPU machine types

The following table shows the calculated cost for N2 high-CPU predefined machine
types. The vCPUs and memory from each of these
machine types are billed by their individual predefined vCPU and memory
prices, but these tables provide the cost that you can expect
using a specific machine type.

High-CPU machine types have one vCPU for every 1 GB of memory. High-CPU
machine types are ideal for tasks that require moderate memory configurations
for the needed vCPU count.

N2 custom vCPUs and memory

Custom machine types let you set a specific number of vCPUs and GB of
memory for your instances to match the needs of your workload.
Custom machine types save you the cost of running on a larger and more
expensive machine type if your application does not require all of the resources
provided by that machine type.

Read
Creating a VM instance with a custom machine type
to learn how to use these machine types.

Sustained use discounts for custom machine types are calculated
separately from predefined machine types, memory-optimized types, and
shared-core machine types.

Not all machine types are guaranteed to be available in all zones all the time.
To ensure that a machine type is available when you need it, you can preemptively
reserve the machine type in a certain zone. For information about reserving
predefined machine types in a specific zone, see
Reserving zonal resources.

For an accurate estimate of your billing with custom machine types, use the
Google Cloud Platform Pricing Calculator.

N2 extended custom memory

For custom machine types, any memory up to and including
8 GB of memory per vCPU is charged at the standard
custom vCPU and memory pricing rate. Any memory
above 8 GB per vCPU is charged according to the following extended memory prices.
To learn how to create instances with custom machine types and extended memory,
see Adding extended memory to a machine type.

N2D machine types

N2D standard machine types

The following table shows the calculated costs for standard predefined machine
types in the N2D machine family. The vCPUs and memory from each of these
machine types are billed by their individual
predefined vCPU and memory prices, but these tables provide
the cost that you can expect using a specific machine type.

Standard machine types have 4 GB of memory per vCPU.

N2D high-memory machine types

The following table shows the calculated cost for the N2D high-memory predefined
machine types. The vCPUs and memory from each of these
machine types are billed by their individual predefined vCPU and memory
prices, but these tables provide the cost that you can expect
using a specific machine type.

High-memory machine types have 8 GB of memory per vCPU. High-memory instances
are ideal for tasks that require more memory relative to virtual CPUs.

N2D high-CPU machine types

The following table shows the calculated cost for the N2D high-cpu predefined
machine types. The vCPUs and memory from each of these
machine types are billed by their individual predefined vCPU and memory
prices, but these tables provide the cost that you can expect
using a specific machine type.

High-cpu machine types have 1 GB of memory per vCPU. High-cpu instances
are ideal for tasks that require more virtual CPUs relative to memory.

N2D custom vCPUs and memory

Custom machine types let you set a specific number of vCPUs and GB of
memory for your instances to match the needs of your workload.
Custom machine types save you the cost of running on a larger and more
expensive machine type if your application does not require all of the resources
provided by that machine type.

Read
Creating a VM instance with a custom machine type
to learn how to use these machine types.

Sustained use discounts for custom machine types are calculated
separately from predefined machine types, memory-optimized types, and
shared-core machine types.

Not all machine types are guaranteed to be available in all zones all the time.
To ensure that a machine type is available when you need it, you can preemptively
reserve the machine type in a certain zone. For information about reserving
predefined machine types in a specific zone, see
Reserving zonal resources.

For an accurate estimate of your billing with custom machine types, use the
Google Cloud Platform Pricing Calculator.

N2D extended custom memory

For custom machine types, any memory up to and including
8 GB of memory per vCPU is charged at the standard
custom vCPU and memory pricing rate. Any memory
above 8 GB per vCPU is charged according to the following extended memory prices.
To learn how to create instances with custom machine types and extended memory,
see Adding extended memory to a machine type.

N1 machine types

N1 standard machine types

The following table shows the calculated cost for standard predefined machine
types in the N1 machine family. The vCPUs and memory from each of these
machine types are billed by their individual
predefined vCPU and memory prices, but these tables provide
the cost that you can expect using a specific machine type.

Standard machine types have 3.75 GB of memory per vCPU.

N1 high-memory machine types

The following table shows the calculated cost for the N1 high-memory predefined
machine types. The vCPUs and memory from each of these
machine types are billed by their individual predefined vCPU and memory
prices, but these tables provide the cost that you can expect
using a specific machine type.

High-memory machine types have 6.5 GB of memory per vCPU. High-memory instances
are ideal for tasks that require more memory relative to virtual CPUs.

N1 high-CPU machine types

The following table shows the calculated cost for N1 high-CPU predefined machine
types. The vCPUs and memory from each of these
machine types are billed by their individual predefined vCPU and memory
prices, but these tables provide the cost that you can expect
using a specific machine type.

High-CPU machine types have one vCPU for every 0.90 GB of memory. High-CPU
machine types are ideal for tasks that require moderate memory configurations
for the needed vCPU count.

N1 custom vCPUs and memory

Custom machine types let you set a specific number of vCPUs and GB of
memory for your instances to match the needs of your workload.
Custom machine types save you the cost of running on a larger and more
expensive machine type if your application does not require all of the resources
provided by that machine type.

For example, instead of using an n1-standard-8 machine type when you
need a machine type with 6 vCPUs, you can create an instance with a custom
machine type that has 6 vCPUs and 22.5 GB of memory. Creating a custom
machine type can save you up to 40% compared to selecting a larger machine type.
Custom machine types are billed according to the number of vCPUs and the amount
of memory used.

Read the
Creating instances with custom machine types
to learn how to use these machine types.

Sustained use discounts for custom machine types are calculated
separately from predefined machine types, memory-optimized types, and
shared-core machine types.

Not all machine types are guaranteed to be available in all zones all the time.
To ensure that a machine type is available when you need it, you can preemptively
reserve the machine type in a certain zone. For information about reserving
predefined machine types in a specific zone, see
Reserving zonal resources.

For an accurate estimate of your billing with custom machine types, use the
Google Cloud Platform Pricing Calculator.

N1 extended custom memory

For custom machine types, any memory up to and including
6.5 GB of memory per vCPU is charged at the standard
custom vCPU and memory pricing rate. Any memory
above the 6.5 GB per vCPU is charged according to the extended memory prices
that are described in detail below. See the
Extended Memory
page to learn how to create instances with custom machine types and extended
memory.

Compute-optimized machine type family

Compute-optimized machine types are ideal for compute-intensive workloads. These
machine types offer the highest performance per core on Google Compute Engine.

C2 machine types

C2 machine types offer Intel Scalable Processors (Cascade Lake) and up to
3.8Ghz sustained all-core-turbo. Currently, C2 machine types are only available
in certain regions and zones. The
following table describes the pricing per vCPU and GB of memory for C2 machine
types.

The following table shows the calculated cost for c2-standard machine types,
which are C2 predefined machine types. The vCPUs and memory from each of these
machine types are billed by their individual compute-optimized vCPUs and memory
prices but these tables provide the cost that you can expect using a specific
machine type.

Memory-optimized machine type family

Memory-optimized machine types are ideal for tasks that require intensive use of
memory with higher memory to vCPU ratios than the general-purpose n1-highmem
machine types. Memory-optimized machine types are available in certain regions
only. See the Machine types page to learn
more about memory-optimized machine types.

Sustained use discounts for memory-optimized machine types are calculated
separately from general-purpose machine types, custom machine types, and
shared-core machine types.

Not all machine types are guaranteed to be available in all zones all the time.
To ensure that a machine type is available when you need it, you can preemptively
reserve the machine type in a certain zone. For information about reserving
predefined machine types in a specific zone, see
Reserving zonal resources.

M2 machine types

If the machine types above don’t match your workloads, you can choose from
the following list of machine types that have larger amounts of memory per vCPU.
To use these machine types, you must request quota using one of the following
options:

  • Request access to evaluation quota so
    that you can test the performance of these machine types. Any VMs you create
    with these machine types count against the evaluation quota and are billed
    using the evaluative prices listed below. Evaluation quota persists only for
    a limited amount of time on your project.
  • Purchase a 1 year or 3 year commitment
    for sustained usage. Commitments are not billed incrementally. Commitments
    bill you a monthly fee for the duration of your commitment term even if you do
    not use any of the committed resources.

These machine types are only available in select zones.

M1 machine types

m1-ultramem and m1-megamem machine types have greater than 14 GB of memory
per vCPU. The following describes the pricing per vCPU and GB of memory of these
machine types.

The following table shows the calculated cost for m1-megamem and m1-ultramem
machine types. The vCPUs and memory from each of these machine types are billed
by their individual memory-optimized vCPUs and memory prices but these tables
provide the cost that you can expect using a specific machine type.

These machine types are only available in
select zones.

Accelerator-optimized machine type family

Accelerator-optimized (A2) VMs
are optimized for
massively parallelized CUDA compute workloads,
such as machine learning (ML) and high performance computing (HPC).

A2 VMs are preconfigured with a set number of
NVIDIA® A100 GPUs.

The A2 machine types are billed for their attached A100 GPUs, predefined vCPU,
and memory.

A2 standard machine types (base vCPU and memory prices only)

The following table shows only the base vCPUs and memory prices for A2 VMs.

A2 standard machine types (total cost)

The following table shows the total calculated cost that you can expect
for these predefined A2 machine types. This total cost includes the cost for
the GPUs, vCPU, and memory.

E2 shared-core machine types

Unlike predefined machine types and custom machine types, shared-core machine
types are not billed on their individual
resources. Each machine type has a defined price for both vCPUs and memory.
E2 shared-core machines with committed use discount contracts consume cores
in the following manner:

  • e2-micro: 0.25 cores
  • e2-small: 0.50 cores
  • e2-medium: 1.0 cores

Learn more about
E2 shared core machine types.

Compute Engine offers shared-core machine types, which
are more cost-effective for running smaller applications that don’t require
as many resources as provided by the other machine types.

E2 shared-core machine types don’t offer a sustained-use discount.

Note: e2-micro instances get 12.5% of 2 vCPUs and are allowed to burst up to 2
full vCPUs for short periods. e2-small instances get 25% of 2 vCPUs and are
allowed to burst up to 2 full vCPUs for short periods. e2-medium instances get
50% of 2 vCPUs and are allowed to burst up to 2 full vCPUs for short periods.

N1 shared-core machine types

Compute Engine offers shared-core machine types, which
are more cost-effective for running smaller applications that don’t require
as many resources as provided by the other machine types.

Unlike predefined machine types, custom machine types, and memory-optimized
machine types, shared-core machine types are not billed on their individual
resources. Each machine type has a defined price for both vCPUs and memory.

Sustained use discounts for shared-core machine types are calculated
separately from predefined machine types, custom machine types, and
memory-optimized machine types.

CPU bursting

Shared-core VMs offer bursting capabilities that allow instances
to use additional physical CPU for short periods of time. Bursting happens automatically when your
instance requires more physical CPU than originally allocated. During these
spikes, your instance will opportunistically take advantage of available
physical CPU in bursts. Note that bursts are not permanent and are only possible
periodically. Bursting doesn’t incur any additional charges. You are charged the listed on demand
price for e2 shared-core, f1-micro, and g1-small VMs.

Note: f1-micro instances get 20% of 1 vCPU and are allowed to burst up to
a full vCPU for short periods. g1-small instances get 50% of 1 vCPU and are
allowed to burst up to a full vCPU for short periods.

Note: Listed monthly pricing includes applicable, automatic
sustained use discounts, assuming that your
instance or node runs for a 730 hour month.

N2, N2D, and C2 higher bandwidth configuration

You can configure your N2, N2D, and C2 machine types to use TIER_1
higher bandwidth. This option is only available to N2, N2D, and C2 machine types
with greater than 30 vCPUs, and is dependent
upon machine type availability in
regions and zones. There is no charge
for this feature during Preview.

Sustained use discounts

Sustained use discounts are calculated for each individual vCPU and GB of
memory that you use. When you use a vCPU or a GB of memory for more
than 25% of a month, Compute Engine automatically gives you a
discount for every incremental second that you continue to use those resources.
The discount increases with usage and you can get up to a 30%
net discount off of the vCPU and memory cost for instances that run the
entire month.

Additionally, vCPU and memory usage in each region is calculated separately
for each of the following categories:

For example, if you use predefined machine types, custom machine types, and
memory-optimized machine types in us-west1, Compute Engine
calculates the sustained use discount for each of those categories separately.
Other regions are also calculated separately from us-west1 resources.

Sustained use discounts are applied automatically and will be calculated and
added to your bill as your project earns them. There is no action needed on your
part to enable sustained use discounts.

To learn more about sustained use discounts, see the
Sustained Use Discounts
documentation.

Committed use discounts

Compute Engine offers the ability to purchase a
committed use contract
in return for heavily discounted prices for VM usage. These discounts are known
as committed use discounts. Read the
purchasing a commitment
page to learn how to create a commitment.

Committed use discounts are available in the following categories:

  • General purpose: Committed use discounts for all general purpose machine
    types, general purpose sole-tenant nodes with or without GPUs, and local SSDs.
    N1 shared-core machine types are not eligible for committed use discounts.
  • Memory-optimized: Committed use discounts for memory-optimized machine
    types.
  • Compute-optimized: Committed use discounts for compute-optimized machine
    types.

Commitments are appropriate for predictable and steady state usage where you
will use a specific amount of vCPUs and memory for future workloads.
Commitments let you purchase a specific number of vCPUs and amount of
memory at up to a 57% discount over full prices for
most machine types. The discount is up to 70% for
memory-optimized machine types. You commit to the entire
usage term and are billed for each month regardless of whether usage has
occurred.

To see vCPU and memory pricing for 1 and 3 year commitments compared with
other Compute Engine pricing options, see the
resource pricing tables.

Combining commitments with reservations

A committed use discount provides a 1- or 3-year discounted price agreement, but
it does not reserve capacity in a specific zone. A reservation ensures that
capacity is held in a specific zone even if the reserved VMs are not running. By
combining a reservation with a commitment, you get discounted, reserved
resources.

Note: For GPUs and local SSDs, in order to purchase a commitment and get
discounted prices, you must define a reservation when purchasing the commitment.
The zone and amount of GPUs and local SSDs in the reservation cannot be
changed for the duration of the commitment.

See Purchasing a commitment with an attached reservation.

Simulated maintenance event pricing

Starting February 10, 2020, there is no cost to running
simulated maintenance events.

Note: Normal 1-minute-minimum usage charges for machine types and premium
images still apply to instances that you stop or preempt during a simulated
maintenance event. See the machine type billing model and
premium image pricing for details.

Prior to this date, the following charges apply:

  • Simulated maintenance on instances configured for
    live migration
    incur costs for each of the following instance resources:

    • Price per vCPU on the instance, where f1-micro and g1-small are each
      equivalent to 1 vCPU: $0.040
    • Price per GB of memory: $0.010
    • Price per GB of local SSD space: $0.001
  • Simulated maintenance on
    preemptible VM instances: Free
  • Simulated maintenance on instances configured to
    stop and restart: Free

Suspended VM instances

When you suspend an instance,
Compute Engine preserves the memory and device state. While you
are not charged for the VM instance as if it were running, suspended instances
still incur charges for the following:

Note: All preserved state charges in this section are prorated based on a
granularity of seconds. For example, if you suspended 1 GB of space for half
the month, then you are charged for only half of the month.

Viewing usage

The Google Cloud Console provides a transaction history for each of your
projects. This history describes your current balance and estimated resource
usage for that particular project.

To view a project’s transaction history, go to the
estimated billing invoice page.

What’s next

Disks and images pricing  |  Compute Engine: Virtual Machines (VMs)

This page describes the pricing information for Compute Engine disks and
images. This page does not cover pricing for
VM instances,
networking,
sole-tenant nodes, or
GPUs.

Compute Engine charges for usage based on the following price sheet. A bill
is sent out at the end of each billing cycle, listing previous usage and
charges. Prices on this page are listed in U.S. dollars (USD).

For Compute Engine, disk size, machine type memory, and network usage
are calculated in gigabytes (GB), where 1 GB is 230 bytes. This unit of measurement is also known as a gibibyte (GiB).

If you pay in a currency other than USD, the prices listed in your currency on
Cloud Platform SKUs apply.

You can also find pricing information with the following options:

Premium images

Certain images available on Compute Engine are considered
premium images and incur charges to use. These images include:

  • Red Hat Enterprise Linux (RHEL and RHEL for SAP)
  • SUSE Linux Enterprise Server (SLES and SLES for SAP)
  • Windows Server
  • SQL Server

Note: If you are a Microsoft licensee with a contract that includes Software
Assurance, you might be able to move your existing SQL Server licenses to
Compute Engine. For more information about License Mobility, see
Using existing Microsoft licenses.

The price for a premium image depends on which machine type
you use. For example, a standard SLES image costs $0.02 per hour to run on an
f1-micro instance, but the same image costs $0.11 per hour to run on an
n1-standard-8 instance. The prices for premium images are the same worldwide
and do not differ based on zones or regions.

All prices for premium images are in addition to charges for
using a machine type. For example, the total price for using an
n1-standard-8 instance with an SLES image is the sum of the machine
type cost and the image cost:

n1-standard-8 cost + SLES image cost =
$0.379998 + $0.11 = $0.49 per hour

Preemptible instances do not
reduce the costs of premium images and do not change the way that you
are billed for the use of those images. If Compute Engine
terminates a preemptible instance that runs a premium image, you are
billed for that image as if you terminated the instance yourself. The
charges for minimum usage still apply, and bills for premium images
are still calculated by rounding up to the nearest usage increment.

Note: When you use premium images, Google is required to report the appropriate
licensing details to the image provider(s). This information might include
information about your Google account (such as the person or entity being
billed, and the region or country registered to the account), details of the
transaction (such as what product or service you have used, the
corresponding Google SKU, or the date when you first used the product or service),
and usage information (such as total hours of usage).

Red Hat Enterprise Linux (RHEL) and RHEL for SAP images

RHEL images:

  • $0.06 USD/hour for instances with 4 or fewer vCPUs
  • $0.13 USD/hour for instances with more than 4 vCPUs

RHEL 6 ELS images:

  • $0.02 USD/hour for instances with 4 or fewer vCPUs
  • $0.05 USD/hour for instances with more than 4 vCPUs

RHEL for SAP with HA and Update Services images:

  • $0.10 USD/hour for instances with 4 or fewer vCPUs
  • $0.225 USD/hour for instances with more than 4 vCPUs

All RHEL and RHEL for SAP images are charged a 1 minute
minimum. After 1 minute, RHEL images are charged in 1 second
increments
.

SLES and SLES for SAP images

SLES images:

  • $0.02 USD/hour for f1-micro and g1-small machine types
  • $0.11 USD/hour for all other machine types

SLES for SAP images:

  • $0.17 USD/hour for instances with 1 – 2 vCPUs
  • $0.34 USD/hour for instances with 3 – 4 vCPUs
  • $0.41 USD/hour for instances with 5 or more vCPUs

All SLES images are charged a 1 minute minimum. After 1
minute, SLES images are charged in 1 second increments.

Committed use discounts are now available for SUSE Linux Enterprise Server (SLES) for SAP
licenses. To purchase a commitment, see
Purchasing commitments for SAP premium SLES images.

Committed use discounts for SLES SAP images

By purchasing a 1- or 3-year committed use discount contract for SLES SAP images you can save
between 59% and 63% over the on-demand image price.

SKU name On-demand hourly 1-year commitment/month 3-year commitment/month
SLES for SAP, 1-2 virtual cores $0.17/hour $50.01 $44.72
SLES for SAP, 3-4 virtual cores $0.34/hour $100.01 $89.43
SLES for SAP, 5+ virtual cores $0.41/hour $119.94 $107.75

Ubuntu Pro

The following sections outline the license cost for using Ubuntu Pro images
on Compute Engine. When running VMs that use the premium Ubuntu Pro images,
you incur license cost in addition to the regular cost of running the VM. For VM
pricing, see VM instances pricing.

The license cost for running Ubuntu Pro VMs, per hour, on Compute Engine
is calculated as follows:

(license cost for RAM per GB per hour) + (license cost for vCPU per hour)

Memory

License cost for memory is charged at one flat rate of $0.000127 per GB/hour in USD.

vCPU

License cost for vCPU varies by the number of vCPUs that the Ubuntu Pro VM has.
The following table summarizes the license cost per hour in USD.

Number of vCPUs License cost (USD)/hour
1 $0.001660
2 $0.002971
4 $0.005545
6 – 8 $0.009970
10 – 16 $0.018063
18 – 48 $0.033378
50 – 78 $0.060548
80 – 96 $0.077871
98 – 222 $0.102401
>222 $0.122063

Example

For example, if your Ubuntu Pro VM has 64 GB RAM and 16 vCPUs, the
license cost is calculated as follows:

Hourly license cost per VM = (0.000127 * 64) + (0.018063) = $0.026191
Monthly license cost (31 day month) per VM = 0.026191 * 744 = $19.486104
Ubuntu pro with attached GPUs license costs

The following sections outline only the license cost for using Ubuntu Pro images
with attached GPUs on Compute Engine. When running VMs that use the premium
Ubuntu Pro images with attached GPUs, you incur license cost for the premium image
and a GPU license in addition to the regular cost of running the VM and the cost of
the attached GPU.

The license cost of running Ubuntu VMs with attached GPU, per hour, is
calculated as follows:

(license cost for RAM per GB per hour) + (license cost for vCPU per hour) + (license cost for GPU per hour)

The following table summarizes the per GPU license rates per month in USD for
Ubuntu Pro VMs. The license fee varies based on the number of GPUs attached to
the VM but is the same for all GPU models that are available on Compute Engine.

Number of GPUs License cost (USD)
1 $0.035
2 $0.066
4 $0.120
8 $0.208
>8 $0.300

Example

For example if your Ubuntu Pro VM has 64 GB RAM, 16 vCPUs and 4 GPUs attached,
the per hour license cost is calculated as follows:

Hourly license cost per VM  = (0.000127 * 64) + (0.018063) + (0.120) = $0.146191
Monthly license cost (31 day month) per VM = 0.146191 * 744 = 108.766104

Windows Server images

Public images for several versions of Windows Server are available in either
the Server Core configuration or the Server with Desktop Experience
configuration. Both configurations are available at the following prices:

  • $0.023 USD/hour for f1-micro and g1-small machine types
  • $0.046 USD per core/hour for all other machine types

Standard machine types, high-CPU machine types, and high-memory machine types
are charged based on the number of CPUs. For example, n2-standard-4,
n2-highcpu-4, and n2-highmem-4 are
machine-types with 4 vCPUs, and are charged at $0.184 USD/hour (4 x $0.046
USD/hour).

Windows Server images are charged a 1 minute minimum. After
1 minute, Windows images are charged in 1 second increments.

For information about licensing for Windows Server images, see Microsoft licenses.

SQL Server images

SQL Server images incur costs in addition to the cost for
Windows Server images and the cost for the selected
machine type.

  • $0.399 USD per core/hour for
    SQL Server Enterprise
  • $0.1645 USD per core/hour for
    SQL Server Standard
  • $0.011 USD per core/hour for
    SQL Server Web
  • No additional charge for SQL Server Express

Microsoft SQL Server licensing requires a core license to be assigned to each
virtual CPU on your virtual machine instance, with a four core minimum for each
instance. Instances with fewer than 4 vCPUs will be charged for SQL Server at 4
x $0.1645 USD/hour ($0.658 USD/hour) to comply with these requirements. For
instances with 4 or more vCPUs, Compute Engine charges you for
Microsoft SQL Server licenses in increments of 2. However, instances with
a custom machine type
will be charged for the number of SQL Server licenses that is equal to the
number of vCPUs.

Google recommends that you not use SQL Server images on f1-micro or
g1-small machine types based on Microsoft’s minimum hardware and software recommendations.

Unlike other premium images, SQL Server images are charged a 10
minute minimum
. After 10 minutes, SQL Server images are charged in
1 minute increments.

For information about licensing for SQL Server OS images, see Microsoft licenses.

Disk pricing

Each VM instance has at least one disk attached to it. Each disk incurs a cost,
described in this section. In addition, if you use snapshots,
there are separate snapshot charges.

All disk-related charges in this section are prorated based on a granularity of
seconds. This includes all persistent disk types, snapshot storage,
and local SSD pricing.

For example, based off US pricing, a 200 GB standard persistent disk volume
would cost $8.00 for the whole month. If you only provisioned a 200 GB volume
for half a month, it would cost $4.00. Likewise, a 200 GB SSD persistent disk
volume would cost $34.00 for the whole month. If you only provisioned a 200 GB
volume for half a month, it would cost $17.00.

Provisioned disk space includes all used and unused space. If you provision a
200 GB disk, you are billed for that entire disk space, regardless of how you
use it, until you relinquish it.

Persistent disk pricing

Standard, SSD, and balanced persistent disks are priced by the amount of
provisioned space per disk. For these disk types, I/O operations are included
in the price for provisioned space. Since disk performance grows linearly with
the size of your disk, consider your I/O needs when choosing the size of your
disk.

Extreme persistent disks are priced by the amount of provisioned space per disk
and the number of provisioned IOPS per disk.

For more information, see the
persistent disk specifications.

After you successfully delete a persistent disk, you are no longer charged
for that disk.

Persistent disk snapshots

All snapshots that exist in your project incur monthly storage fees. Whenever
you create or restore a snapshot, you might also incur network fees based on the
storage location of the snapshot.

Storage charges for snapshots

A snapshot incurs monthly storage charges as long as it exists in your
project. Persistent disk snapshots only incur charges for the total size of the
snapshot. For example, if you only used 2 TB of disk space on a 5 TB
persistent
disk, your snapshot size is charged for 2 TB, rather than the full
5 TB of
provisioned disk space. Compute Engine also provides
incremental snapshots, which
contain only the data
that has changed since the previous snapshot, providing for a generally lower
cost for snapshot storage.
When you delete a complete or incremental snapshot, some of its data may move
to the next incremental snapshot in the snapshot chain. This additional data
increases the storage cost because you are using more space in the storage
system.

Note that snapshot storage charges, like disk-related charges, are prorated
based on a granularity of seconds.

Network charges for snapshot creation and restoration

Network charges for snapshot creation and restoration follow standard
Cloud Storage network pricing,
but are billed under Compute Engine.

A persistent disk can be stored in a
Compute Engine zone or region, but a
snapshot is stored in either a
Cloud Storage region or multi-region.
Note that Compute Engine regions and Cloud Storage regions
have similar names. Each multi-region contains multiple regions, and each
region contains multiple zones. For example, the zone europe-north2-a is part
of the europe-north2 region, and europe-north2 is part of the eu
multi-region.

If you create or restore a snapshot that is stored in a location that is
different from the location of your disk, the data travels over the network
between those locations and may incur network fees. Snapshots
incur the same fees as Cloud Storage egress.

For example, if your disk is located in the us-central1 region, and you want
to create a snapshot in europe-west1, you will incur a cross-regional network
charge.

Local SSD pricing

Local SSD devices are charged for the amount of provisioned space per device
for the lifetime of the instance it is attached to. The prices for local SSDs
differ depending on the region. For example, in the Iowa, Oregon, Taiwan, and
Belgium regions, local SSDs cost $0.080 per GB per
month. As mentioned earlier, local SSD charges are prorated to a granularity of
seconds.

Because local SSDs can only be purchased in 375 GB increments, the
cost-per-month
for a single device is the monthly rate multiplied by 375 GB. For example, at a
monthly rate of $0.080, the cost would be
$30.00 per device per month. Actual data storage and
usage are included in that price and there is no additional charge for local
traffic between the virtual machine and the local SSD device.

You can reserve local SSDs in a specific zone, with or without a commitment.
Without a commitment, you pay normal on-demand prices.
For committed-use discounted pricing for local SSDs, a reservation
must be created when purchasing the commitment. For more information, see
Reserving zonal resources.

Custom image storage

If you import or create custom images in
Compute Engine, these images incur a storage cost. The cost of these
custom images depends on the location where you store the image. There are no
network transfer fees for creating images, nor for creating disks from images.

Machine image

The cost of using a machine image depends on the size of the machine image, and
the location where it is stored.

There is also a one time network transfer fee if you are storing a machine image
in a different location than the source instance, or if you create an instance
from a machine image that is stored in a different location from the instance.

What’s next

How Machine Learning is reshaping Price Optimization

The challenge of setting the right price

Setting the right price for a good or service is an old problem in economic theory. There are a vast amount of pricing strategies that depend on the objective sought. One company may seek to maximize profitability on each unit sold or on the overall market share, while another company needs to access a new market or to protect an existing one. Moreover, different scenarios can coexist in the same company for different goods or customer segments.

In this blog post, we’ll present the problem of price optimization for retail – which has its own particularities – and how retailers can take advantage of the tremendous power of Machine Learning technology to build effective pricing solutions.

These are some of the crucial questions that retailers recurrently face:

  • What price should we set if we want to make the sale in less than a week?
  • What is the fair price of this product, given the current state of the market, the period of the year, the competition, or the fact that it is a rare product?

Given that in these days it is very easy for a customer to compare prices thanks to online catalogs, specialized search tools or collaborative platforms, retailers must pay close attention to several parameters when setting prices. Factors such as competition, market positioning, production costs, and distribution costs, play a key role for retailers in order to make the right move. Check this example for a deep dive into real-life sales data analysis for an online retailer.

Machine Learning can be of great help in this case and have an enormous impact on KPIs. Its power lies in the fact that the developed algorithms can learn patterns from data, instead of being explicitly programmed. Machine Learning models can continuously integrate new information and detect emerging trends or a new demands.

The use of Machine Learning is a very attractive approach for retailers. Instead of using, for example, aggressive general markdowns (which is often a bad strategy), they can benefit from predictive models that allow them to determine the best price for each product or service.

What is price optimization?

Briefly, price optimization uses data analysis techniques to pursue two main objectives:

  1. Understanding how customers will react to different pricing strategies for products and services, i.e., understanding the elasticity of the demand.

  2. Finding the best prices for a given company, considering its goals.

Pricing systems have evolved since the early 1970s until now, from applying very simple strategies, such as a standard markup to base cost, to being capable of predicting the demand of products or services and finding the best price to achieve the set KPI.

Price optimization techniques can help retailers evaluate the potential impact of sales promotions or estimate the right price for each product if they want to sell it in a certain period of time.

Current state-of-the-art techniques in price optimization allow retailers to consider factors such as:

  • Competition
  • Weather
  • Season
  • Special events / holidays
  • Macroeconomic variables
  • Operating costs
  • Warehouse information

to determine:

  • The initial price
  • The best price
  • The discount price
  • The promotional price
Using different kind and sources of data to find the prices that improve profits.

Price optimization vs dynamic pricing

Even though sometimes these two concepts are used as synonyms, they represent different concepts. The main difference is that dynamic pricing is a particular pricing strategy, while price optimization can use any kind of pricing strategy to reach its goals.

For example, using a dynamic pricing strategy, retailers can dynamically alter the prices of their products in order to match their competitor’s price. This strategy would imply changing prices very frequently but not necessarily being this the best strategy possible. Price optimization techniques focus on finding the price that maximizes a defined cost function (e.g., the company’s margin), considering many different factors to suggest such price or price range for different scenarios. Depending on the particular use case, this can indeed be performed in a dynamic way, and thus combining dynamic pricing + optimization is the go-to option for many scenarios.

Price optimization vs automatic pricing

Moreover, it is important to differentiate price optimization from automatic pricing as they primarily solve two different pain points: sub-optimal pricing strategy vs. excessive cost of pricing.

The main difference is that we focus on a price automation solution when pricing is a pain point for the company in terms of costs. By automatically pricing the items we are not changing the pricing strategy itself but we are changing the pricing process making it cheaper and faster. On the other hand, when we think of a price optimization solution we change the pricing strategy in order to maximize an objective function, subject to different business constraints.

This is to say that by implementing a price optimization solution we are automating our pricing process but not vice versa; not necessarily all price automation solutions optimize the pricing strategy. Both price automation and price optimization solutions could be understood as dynamic pricing if the frequency of price changes is high.

Price automation with and without Machine Learning

Finally, price automation can be developed with or without Machine Learning. The difference between these two approaches is that without Machine Learning the pricing rules are pre-defined while with Machine Learning rules are obtained in a data-driven way.

For example, a price automation system without using Machine Learning would take the form of a pre-defined set of rules such as:

  • Mark up all products in FOOD CATEGORY by 15%
  • If BRAND is in MARKETING CAMPAIGN add a 10% discount
  • If KEY VALUE ITEM set price equal to competition and lower it by 5%
  • Adjust all prices to end with “.99”

On the other hand, a price automation solution with Machine Learning implies training a model capable of automatically price items the way they would be priced by a human expert at scale. The model could take in historical data and different characteristics of the product as well as unstructured data such as images and text and would learn the pricing rules with no explicit coding, adapting to changes in the environment in a much richer and dynamic way.

Key differences between different pricing concepts

Feature Dynamic Pricing Price Optimization Price Automation with Machine Learning
Prices change frequently Yes Yes Yes
Prices are set automatically Yes Yes Yes
Main goal is to reduce pricing process cost Yes
Main goal is to optimize pricing strategy Yes
Demand Forcasting Yes
Estimate Store/SKU price elasticity of demand Yes
“What if” Pricing Scenario Capability Yes

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What Machine Learning can do for retail price optimization

The pricing strategies used in the retail world have some peculiarities. For example, retailers can determine the prices of their items by accepting the price suggested by the manufacturer (commonly known as MSRP). This is particularly true in the case of mainstream products. Another simple strategy is keystone, which consists in defining the sale price as the double of the wholesale price or cost of the product.

While these and other strategies are widely used, Machine Learning enables retailers to develop more complex strategies that work far better to achieve their KPIs. Machine Learning techniques can be used in many ways to optimize prices. Let’s have a look at a typical scenario.

For example, a widely adopted pricing strategy technique that enhances this technology is dynamic pricing. Using this strategy, retailers can dynamically alter the prices of their products based on current market demand. However, changing the prices dynamically with no objective function in mind may lead to suboptimal results. This is why we suggest using dynamic pricing jointly with price optimization techniques.

A typical scenario

Imagine an e-commerce or brick-and-mortar retailer who wants to estimate the best prices for new products for the next season. The competition is hard, so their prices and promotions need to be taken into consideration. Therefore, the retailer adopts a widely used strategy: competitive pricing. Simply put, this strategy defines the price of a product or service based on the prices of the competition.

Let’s see the steps needed to develop a Machine Learning solution for this use case.

Process of defining prices in retail with price optimization using Machine Learning.
1. Gather input data

First of all, we need data. To train Machine Learning models, it is necessary to have different types of information (structure or unstructured data):

  • Transactional: a sales history that includes the list of the products purchased and, eventually, the customers who purchased them.
  • Description of the products: a catalog with relevant information about each product such as category, size, brand, style, color, photos and manufacturing or purchase cost.
  • Data on past promotions and past marketing campaigns.
  • Customer Reviews: reviews and feedback given by customers about the products.
  • Data on the competition: prices applied to identical or similar products.
  • Inventory and supply data.
  • In the case of physical stores: information about their geographical location and that of the competitors.

For further reference take a look at this post that highlights actionable steps to get your data ready for price optimization.

Depending on the set KPIs and the way of modeling the solution, some of this data may not be necessary. For example, if there is little or no information about customers, which is sometimes the case for brick-and-mortar retailers, the model can nonetheless be trained.

In contrast, information about the competition is crucial for a competitive pricing strategy. In many cases, it is even possible to connect via APIs to this information or monitor it online. Checkout a real-life data collection example here.

2. Define goals and constraints

The next step is to define the strategic goals and constraints.

Retailers may pursue a unique, clear objective of profit maximization. However, they may also be interested in customer loyalty (e.g. increasing the net promoter score or the conversion rate) or in attracting a new segment (e.g. young people).

Restrictions may be of legal nature (e.g. if some type of control of sale prices is carried out), they may have to do with the reputation of the company (e.g. fearing a bad image for applying favorable prices only to a certain segment of customers) or be related to physical aspects such as the capacity of a store or the average time of supply.

Each particular scenario will impact the way the problem is modeled. It is possible, and usually very interesting, to test different scenarios for the same retailer, which implies using different models.

3. Modeling and training

In this step, the data previously gathered is used to train the Machine Learning models. There is a wide variety of models that can be used in price optimization. Historically, Generalized Linear Models (GLMs) have been used (in particular, logistic regression). However, for a few years, more complex and powerful methods have been developed. For instance, depending on the volume of data available, it could be possible to use Deep Learning methods or even reinforcement learning techniques.

In this case, in which we are dealing with new products for the next season, there is an additional difficulty since there is no previous product data. The interesting thing is that the Machine Learning models will know how to find similar products and be effective despite not having specific prior data. The same happens in the case of retailers that sell rare or exotic products.

You can take a look at a real-life example of demand forecasting modeling here.

4. Execute and adjust prices

Once the model is trained, prices can be estimated for the new products and tested. Depending on the modeling, the estimate may be an exact price or a range. The prices obtained by the model can be subsequently adjusted manually by the retailer and optimized regularly.

More opportunities of using Machine Learning for price optimization

Machine Learning can be used for other tasks related to pricing in retail. For example, given a new product, a clustering algorithm can quickly associate it with similar products to obtain a probable price segment. Another compelling possibility is to jointly predict prices and demands for items that were never sold.

More generally, Machine Learning can be a tremendous tool for insights:

  • In what way is the sale of pants impacted when shirts’ prices are drastically cut?

  • When efforts are made to sell more pens, are the related products, such as ink, notebooks or work agendas, impacted?

  • Are customers who buy a certain computer more or less likely to buy monitors the following month?

  • Are inactive clients in the last year sensitive to a promotion campaign?

These are just some examples of the questions that Machine Learning models can help answer.

Advantages of price optimization with Machine Learning

In addition to automation and speed, there are several advantages to using Machine Learning to optimize prices.

First, Machine Learning models can consider a huge number of products and optimize prices globally. The number and nature of parameters and their multiple sources and channels allow them to make decisions using fine criteria. This is a daunting task if retailers try to do it manually, or even using basic software.

For example, it is known that changing the price of a product often impacts the sales of other products in ways that are very hard to predict for a human. In most cases, the accuracy of a Machine Learning solution will be significantly higher than that of a human. In addition, retailers can modify the KPI and immediately see how the models recalculate prices for the new goals.

Second, by analyzing a large amount of past and current data, a Machine Learning can anticipate trends early enough. This is a key issue that allows retailers to make appropriate decisions to adjust prices.

Finally, in the case of a competitive pricing strategy, Machine Learning solutions can continuously crawl the web and social media to gather valuable information about prices of competitors for the same or similar products, what customers say about products and competitors, considering hot deals, as well as the price history over the last number of days or weeks.

A system that can learn most of what is happening in the market allows retailers to have more information than their competitors in order to make better decisions.

Price optimization for brick-and-mortar and e-commerce retailers

While it may seem more natural to apply Machine Learning in the case of e-commerce retailers, brick-and-mortar retailers can perfectly take advantage from this technology.

In fact, price changes are less often performed in brick-and-mortar retailers and thus, having more room to improve and adjust to current demand.

Digital price tags are enabling brick-and-mortar retailers to do as many price changes as e-commerce sites. However, even without digital price tags, weekly or monthly price changes can be performed in order to match the current demand and maximize profit.

We have proven our approach with one of the largest travel retailers in the world with over 400 stores across the globe and over 160 million clients per year. We helped them boost gross margin by 28% performing weekly price changes in-store.

Price optimization and demand forecasting with Machine Learning during a crisis

At the date of publishing this post, we are in the middle of a global economic slowdown due to the COVID-19 outbreak. As in all recessions, there’s a direct impact on consumer spending, which directly hits on sales for multiple industries. To illustrate this, Revenue Collective reports that 95.6% of the business surveyed as of April 9th has already seen an impact on business, while 72% of them have already revised their annual sales forecasts.
It is no news, then, that most businesses are not operating in business as usual (BAU) fashion, which sparks the following question: are we still able to use Machine Learning to predict demand in this scenario?

Our answer is “yes, but with new things to consider”. In a BAU scenario, Machine Learning models are likely to leverage historical sales and correlated external data to bring insights such as seasonality, relevant sales dates, and competitors’ reactions. During a crisis, as the market is not behaving as usual, the historical insights are likely to fall short to predict future sales. To fight back, we’d need to increase the importance of shorter-term information (e.g. daily sales), in the understanding that the recent past is much more suitable to predict the future. Practically, this means adjusting the feature engineering process to weigh the shorter term sales lags rather than the historical ones.

Additionally, the demand forecasting problem will also require the incorporation of more real-time market data than before as well as external macroeconomic and social data. On the real-time data, this means regularly updating available market data such as sales data, customer churn, sales intent (e.g. added to cart items, traffic to competitors site), competitors’ prices, among others. On the macroeconomic level, data such as consumer spending, unemployment, GDP and even community mobility segmented by cities/regions could also be considered, although these are mostly reported on a monthly basis. Stock market indicators (S&P 500, Dow Jones) could potentially be considered too, as a proxy of real-time macroeconomic trends. Finally, there might also be positive results by incorporating social data, such as reported COVID cases or government policies (i.e. lockdown duration), to generate scenario forecasting and consider them for modeling future demand.

To summarize, in this new economic scenario, our analysis is still that Machine Learning could be greatly leveraged to build accurate demand forecasts and optimize the pricing strategy. The key adaptations to a BAU scenario would be to incorporate more real-time data (market and macroeconomic data) + adapt the models to consider nearer-term lags vs. historical data. It’s also worth noting that business understanding and human judgment will still play a key role in the creation of this solution.

Companies using Machine Learning for price optimization

Price optimization has been used, with significant success, in industries such as hospitality, airline, car rental, and e-commerce retail.

One of the first success stories occurred in the early 2000s, when Hilton Hotels Corp and InterContinental Hotels Group decided to eliminate fixed rates in favor of a fluid scheme, including dynamic pricing strategies. In those years the prices of the rooms were modified once or twice a day. The current computational power allows prices to change practically in real time.

The hotel industry continues to employ dynamic pricing strategies, based entirely on Machine Learning. Currently, Airbnb proposes a dynamic price tool that recommends prices to its hosts, considering parameters such as seasonality, the day of the week or special events, and also more sophisticated factors such as photos of the property to be rented or the prices applied in the neighborhood. Other companies such as eBay and Uber have adopted similar approaches.

Amazon is another of the big players when talking about dynamic pricing strategies. To give an idea, in 2012 Amazon was changing prices much more often than its competitors and in 2013, they were performing as much as 2.5 million price changes per day. Changing prices in such a dynamic way is informally known as the Amazon effect.

In the retail world, the most popular examples have been in e-commerce, but brick-and-mortar retailers have not been left behind. Although it is difficult to know precisely all the retail companies using Machine Learning to optimize their prices and operating processes, there are nevertheless some known success stories.

Companies like Ralph Lauren and Michael Kors use Machine Learning to offer fewer markdowns and better manage their inventory, seeking to increase profit margins, even at the risk of losing a little revenue. Another well-known case is that of Zara, which uses Machine Learning to minimize promotions and adapt quickly to the changing trends. There are many other success stories, such as Morrisons –one of the largest supermarket chains in the United Kingdom–, bonprix –an international fashion company based in Germany– or Monoprice –an American B2B and B2C electronics retailer–, among others. While there is no information available on the exact modeling of the problems, it is known that these companies are taking advantage of the power of Machine Learning to increase their revenues and improve operations.

Final thoughts

Nowadays the world is moving towards changing prices more often and using state-of-the-art data driven pricing strategies is a must. In a study performed by Bain & Company they show that top performers across industries are nearly twice as likely to price dynamically.

Whether it’s about an e-commerce marketplace or a brick-and-mortar retail store, both are embracing the benefits of dynamic pricing and price optimization.

Price optimization helps retailers understand how customers will react to different price strategies for products and services, and set the best prices. Machine Learning models can take key pricing variables into account (e.g. purchase histories, season, inventory, competitors’ pricing), to find the best prices, even for vast catalogs of products or services, that can achieve the set KPIs.

These models don’t have to be programmed. They learn patterns from data and are capable of adapting themselves to new data. They allow retailers to quickly test different hypotheses and make the best decision.

What is probably most important to keep in mind is that the use of Machine Learning in the retail world keeps widening, and all signs point to the fact that this trend will continue in the coming years.

The question is no longer whether to apply dynamic pricing or not. But the question is how to do so in order to remain profitable.

Here at Tryolabs, we specialize in Machine Learning solutions for retail companies. Since 2010, we have been working with several retailers, which let us better understand the opportunities, challenges and available solutions within the industry.

Interested in how you could leverage price optimization at your company? Drop us a line.

This blog post has been updated with the collaboration of Maia Brenner, Gonzalo Marín, Braulio Ríos, Marcos Toscano and Martín Fagioli.

Compute Pricing | Oracle

To make it easier to compare pricing across cloud service providers, Oracle web pages show both vCPU (virtual CPUs) prices and OCPU (Oracle CPU) prices for products with compute-based pricing. The products themselves, provisioning in the portal, billing, etc. continue to use OCPU (Oracle CPU) units. OCPUs represent physical CPU cores. Most CPU architectures, including x86, execute two threads per physical core, so 1 OCPU is the equivalent of 2 vCPUs for x86-based compute. The per-hour OCPU rate customers are billed at is therefore twice the vCPU price since they receive two vCPUs of compute power for each OCPU, unless it’s a sub-core instance such as preemptible instances. Additional details supporting the difference between OCPU vs. vCPU can be accessed here.



AUD – Australian Dollar ($)CAD – Canadian Dollar ($)EUR – Euro (€)GBP – British Pound (£)USD – US Dollar ($)────────────────────BGN – Bulgarian Lev (лв.)BRL – Brazilian Real (R$)CHF – Swiss Franc (CHF)CZK – Czech Koruna (Kč)DKK – Danish Krone (kr)HKD – Hong Kong Dollar (HK$)HUF – Hungarian Forint (Ft)JPY – Japanese Yen (¥)NOK – Norwegian Krone (kr)PLN – Polish Zloty (zł)RON – Romanian Leu (lei)SEK – Swedish Krona (kr)SGD – Singapore Dollar ($)


Compute – Virtual Machine Instances*















Product

Comparison Price (/vCPU) *

Unit price

Unit

Compute – Ampere A1 – OCPU

OCPU per hour

Compute – Ampere A1 – Memory

Gigabyte per hour

Compute – Virtual Machine Optimized – X9

OCPU per hour

Compute – Virtual Machine Optimized – X9 – Memory

Gigabyte per hour

Compute – Virtual Machine Standard – X7

OCPU per hour

Compute – Virtual Machine Dense I/O – X7

OCPU per hour

Compute – Standard – E4 – OCPU

OCPU per hour

Compute – Standard – E4 – Memory

Gigabyte per hour

Compute – Standard – E3 – OCPU

OCPU per hour

Compute – Standard – E3 – Memory

Gigabyte per hour

Compute – Virtual Machine Standard – E2 Micro – Free

Free

Free

OCPU per hour

Database – Marketplace Compute Image – Microsoft SQL Enterprise

OCPU per hour

Database – Marketplace Compute Image – Microsoft SQL Standard

OCPU per hour

*Notes:

  • 1 OCPU on x86 CPU Architecture (AMD and Intel) = 2 vCPUs
  • 1 OCPU on Arm CPU Architecture (Ampere) = 1 vCPU
  • The minimum unit of provisioning starts from 1 OCPU on both X86 (Intel and AMD) and Arm (Ampere) processors.
  • All compute instances use boot volumes as their system disk. Additional storage and boot volumes are billed at the standard block volumes service pricing. Note, that this is in addition to the price of the compute instance.
  • E3, E4, X9, A1 SKUs will support resource-based pricing. The OCPU and the memory resources are priced separately and customers will need to purchase the Memory SKU along with the OCPU SKU. The memory to OCPU ratio for VM Instances can range from 1GB/OCPU upto 64 GB/OCPU.
  • Unused Capacity reservations are priced at 85% of the price of regular instances. This discount is applied in addition to other discounts, such as UCM negotiated rates.
  • Preemptible instances are priced at 50% of the price of regular instances. This discount is applied in addition to other discounts, such as UCM negotiated rates. See the product documentation for supported shapes.

Compute – Bare Metal Instances*













Product

Comparison Price (/vCPU) *

Unit price

Unit

Compute – Ampere A1 – OCPU

OCPU per hour

Compute – Ampere A1 – Memory

Gigabyte per hour

Compute – Optimized – X9

OCPU per hour

Compute – Optimized – X9 – Memory

Gigabyte per hour

Compute – Bare Metal Standard – X7

OCPU per hour

Compute – Bare Metal Dense I/O – X7

OCPU per hour

Compute – Bare Metal Standard – HPC – X7

OCPU per hour

Compute – Standard – E4 – OCPU

OCPU per hour

Compute – Standard – E4 – Memory

Gigabyte per hour

Compute – Standard – E3 – OCPU

OCPU per hour

Compute – Standard – E3 – Memory

Gigabyte per hour

*Notes:

  • You can find upgrade options for previous shapes including BM.HighIO on the Compute FAQ page.
  • The first 3K OCPU-hrs and the first 18K GB-hrs each month is free for Ampere A1 shape.
  • All compute instances use boot volumes as their system disk. Additional storage and boot volumes are billed at the standard block volumes service pricing. Note, that this is in addition to the price of the compute instance.
  • E3, E4, X9, A1 SKUs will support resource-based pricing. The OCPU and the memory resources are priced separately and customers will need to purchase the Memory SKU along with the OCPU SKU. The OCPU and memory resources are fixed for Bare Metal Instances.
  • Unused Capacity reservations are priced at 85% of the price of regular instances. This discount is applied in addition to other discounts, such as UCM negotiated rates.


Compute – Oracle Cloud VMware Solution*







Product

Comparison Price (/vCPU) *

Unit price

Unit

Additional Details

Oracle Cloud VMware Solution – HCX Enterprise – 1 Month Commit

OCPU per hour

Additional capacity can be added in quantities of 104 vCPUs (52 OCPUs)

Oracle Cloud VMware Solution – BM.DenseIO2.52 – 1 Month Commit

OCPU per hour

Additional capacity can be added in quantities of 104 vCPUs (52 OCPUs)

Oracle Cloud VMware Solution – BM.DenseIO2.52 – Hourly Commit

OCPU per hour

Additional capacity can be added in quantities of 104 vCPUs (52 OCPUs)

Oracle Cloud VMware Solution – BM.DenseIO2.52 – 1 Year Commit

OCPU per hour

Additional capacity can be added in quantities of 104 vCPUs (52 OCPUs)

Oracle Cloud VMware Solution – BM.DenseIO2.52 – 3 Year Commit

OCPU per hour

Additional capacity can be added in quantities of 104 vCPUs (52 OCPUs)

*Notes:

  • Minimum of 312 vCPUs (156 OCPUs) each month.
  • Hourly commit requires a minimum of 8 hours
  • Cancelling service prior to the end of the commitment interval does not stop metering.

Compute – GPU Instances

Instances are available as both virtual machines and bare metal, providing flexibility and performance at the fraction of the cost of other public cloud providers.

*Notes:

  • Available soon
  • Unused Capacity reservations are priced at 85% of the price of regular instances. This discount is applied in addition to other discounts, such as UCM negotiated rates.


Compute – Image and Artifact Storage





Product

Unit price

Unit

Custom Image Storage

Same as Object Storage – Standard

Gigabyte storage capacity per month

Container Image Storage

Same as Object Storage – Standard

Gigabyte storage capacity per month

Generic Artifact Storage

Same as Object Storage – Standard

Gigabyte storage capacity per month

Note: Images and Artifacts in existence prior to May 26th, 2021 are exempt from charging.


Compute – Operating Systems






Operating Systems

Unit Price (OCPU per hour)

Oracle Linux 6/7*

Free

CentOS 6.x/7.x

Free

Ubuntu 14.x/16.x

Free

Compute – Windows OS**

  • Server 2016 Standard and Datacenter
  • Server 2012 R2 Standard and Datacenter
  • Server 2012 Standard and Datacenter
  • Server 2008 R2 Standard, Enterprise and Datacenter

*Oracle Linux Premier Support included. With the Unbreakable Enterprise Kernel (UEK), part of Oracle Linux, customers can take advantage of Ksplice zero-downtime updates.

**Windows Server license cost is an add-on to the underlying compute instance price. You will pay for the compute instance cost and Windows license cost separately. You can get more information on how Microsoft Windows Server charges apply from the Compute FAQ page.


Compute – Previous Generation Instances*





Product

Comparison Price (/vCPU) *

Unit price

Unit

Compute – Virtual Machine Standard – X5

OCPU per hour

Compute – Bare Metal Standard – X5

OCPU per hour

Compute – Standard – E2

OCPU per hour


*Notes:

  • Previous generation instances are in use by some current customers but are no longer available for new deployments.
  • Unused Capacity reservations are priced at 85% of the price of regular instances. This discount is applied in addition to other discounts, such as UCM negotiated rates.

Machine Learning for B2B Pricing

Data & modelling challenges in the B2B space and their solutions

Reluctance to adopt ML-based pricing originates from two main sources i.e. data related challenges and the complexity surrounding the pricing as a business process (See Figure: 2). In this section, we have tried to address some of these.

  • ML models need huge volume of data and in B2B, the volume of data is less to build an effective model.         

Solution – This stems from the common perception is that ML models need a large data set.  While it is true that some of the more sophisticated models like deep learning cannot be trained without large volumes of data, there are multiple algorithms in ML e.g. Decision Tree or Generalized Linear Models (GLM) that do not really need huge data sets. In fact, the first few areas of applications of statistical modelling were clinical trial and agriculture where data volume is even less compared to some of the B2B organizations.

  • High cost of an error: A typical B2B deal size runs into millions of dollars and losing a deal can have a significant impact on the company’s revenue.  Therefore, B2B deal pricing is a much riskier endeavor to be left to algorithms. 

Solution – We advocate ML models to be a supplement to human decisioning and not a substitution of the same. The human amendments to the model’s recommendation should be sent as a feedback to the model. It should be captured as an insight that can be leveraged in subsequent pricing decisions.

  • Data quality issues:  Poor data quality is a reality and good models cannot be built on these data.       

Solution – Over the years, most organizations have made significant investments in their applications like ERP, CRM as well as enterprise data warehouse. Additional checks like multi-level reconciliations across systems, investing in a master data management systems will just not help pricing but the overall organization. Ultimately a good model needs good data. It is strongly recommended to treat data enrichment as a continuous process to reap benefits from any analytics initiative.  

  • Sparse data: Some stakeholders in B2B organizations feel that most of the deals are unique and an ML model can hardly learn from the past data during model training. 

Solution – Techniques like Bayesian Hierarchical Models or Decisions Trees can be leveraged to model such scenarios.  Let’s say, you are selling a product in a territory and you don’t have any past history of selling the same product in the same territory, hierarchical models intelligently roll up the data to the next level in hierarchy where you have available historical data and generalize those insights.

  • Complexity in B2B sales process: B2B buying decisions are complex and often the price may not just be a function of quantity but the terms and conditions of contract as well. This makes the calculation of price elasticity extremely difficult. 

Solution – The focus of the modelling should be to compute the Bid Price vs. Win Probability and not estimation of price elasticity i.e. demand as a function of price. The outcome in a B2B sales cycle consists of multiple phases but the advantage here is the seller has the option to revise the quote according to the response from the buyer. The different stages in the B2B sales life cycle can be modelled as state transitions that can be factored as an input to the model so that accurate prices can be determined earlier in the flow. One advantage while modelling pricing in B2B is the buyers and sellers in a B2B environment are expected to behave rationally compared to their consumer counterparts, and models need not really factor in the behavioral pricing that are frequent in the consumer space.

Pricing and Plan Information – CircleCI


What do I get with 2,500 free credits / week?

Users on our Free plan can build up to 250 minutes per week using their 2,500 credits.

On the Free plan, users can build with the Medium compute option (2 vCPUs
with 4 GB of memory) on Linux machines, which uses 10 credits per minute. Users can also build on Windows with the Medium compute option (4 vCPUs with 15 GB of memory), which uses 40 credits per minute.


What if I am building open source?

CircleCI will be offering organizations on our free plan 400,000 credits per month
to use on medium Docker compute for open source repositories, but they can
only be spent on Linux compute. Orgs building OSS Windows projects can
still use the 2,500 free credits per week that all projects have access to
on those projects, and orgs building OSS macOS projects can request free
macOS access by contacting [email protected]

If you are building a bigger open source project and would like the
flexibility of our new plans,

let us know

how we can help you!


What are concurrent job runs?

Concurrent job runs refers to the number of jobs that can run simultaneously
without queueing.1 You pay for compute based on the total amount
of time you use compute resources, not the number of resources that you have
access to. This means you can choose the right plan for your team to maximize
concurrent jobs and parallelism for your jobs to minimize queuing.

For example, say you have a workflow with 10 jobs that each take 5 minutes to
run. If you are limited to 1x concurrency, each of these jobs runs subsequently,
and the workflow completes in 50 minutes. With scaling concurrency, you can
run all 10 jobs concurrently (at the same time), and the workflow completes
in 5 minutes. In both cases, your total usage time is 50 minutes since you
used 10 machines for 5 minutes each. With usage-based pricing, that means
you pay the same amount either way, but if you maximize your concurrency,
you save 45 minutes of time waiting for your workflow to complete.

1To ensure our system remains stable and responsive for all
users, we set a cap of 80x concurrency for some of our resource classes – most
organizations never hit this limit. If you are on a paid Performance
plan and you are hitting this limit, reach out to our Customer Success team through a support ticket to discuss adjusting your plan for higher concurrency.


How do I use credits?

Credits are used to pay for your active users and usage. Usage is based on run time, machine type and size, and premium features like Docker layer caching.


How do I buy credits?

Credits are purchased in blocks of 25,000. At the beginning of each
billing month (based on your purchase date), you are charged for your
credit package and those credits are added to your account.

If you reach 2% of your remaining credits during your billing month, you
will be refilled 25% of your credits. For example, if your monthly package
size is 25,000 credits, you will automatically be refilled 6,250 credits
when you reach 2,500 remaining credits.


Do credits expire?

Credits expire one year after purchase. Unused credits will be forfeited
when the account subscription is canceled.


How do I enable Docker layer caching for my builds?

To use Docker layer caching, your account must be on the Performance plan.
If your team is not yet on the Performance plan, the administrator on your
account can upgrade your team on the Plan Overview page within
the application. Then you can enable Docker layer caching at the job level
within your configuration file.
Learn how.

Docker layer caching uses 200 credits per job run in a pipeline. For
example, if your configuration specifies a workflow with three parallel
Docker build jobs, you will be charged 600 credits each time these jobs
are run in addition to the compute credits/minute usage.


Why does CircleCI charge for Docker layer caching?

Docker layer caching reduces build times on pipelines where Docker images are built by only rebuilding Docker layers that have changed (more in docs here). Docker layer caching (DLC) costs 200 credits per job run.

There are a few things that CircleCI does to ensure DLC is available to customers. We use solid-state drives and replicate the cache across zones to make sure DLC is available. We will also increase the cache as needed in order to manage concurrent requests and make DLC available for your jobs. All of these optimizations incur additional cost for CircleCI with our compute providers, which pass along to customers when they use DLC.

To estimate your DLC cost, look at the jobs in your config file with Docker layer caching enabled, and the number of Docker images you are building in those jobs. Docker layer caching costs 200 credits per job run. There are cases where a job can be written once in a config file but the job runs multiple times in a pipeline, for example, with parallelism.

Note that the benefits of Docker layer caching are only apparent on pipelines that are building Docker images, and reduces image build times by reusing the unchanged layers of the application image built during your job. If your pipeline does not include a job where Docker images are built, Docker layer caching will provide no benefit.

We are looking at ways to optimize Docker layer caching over time in order to improve the experience and reduce the cost.


How do I pay?

Once you have a CircleCI account, and if you are the admin on the account,
you can pay by logging into the CircleCI application and going to
Settings → Plan Overview. From there, you can pay via credit card.

Invoicing billing is available on custom annual plans and requires a spend
of $6,000 / year.


Why does CircleCI have per-active-user pricing?

Credit usage covers access to compute. We prefer to keep usage costs as low as possible to encourage frequent job runs, which is the foundation of a good CI practice. Per-active-user fees cover access to platform features and job orchestration. This includes features like dependency caching, artifact caching, and workspaces, all of which speed up build times without incurring additional compute cost. Our per-active-user charge also allows us to provide support to all customers and deliver additional features like insights and orbs.

Anyone who triggers a build on CircleCI is an active user, regardless of whether they have a CircleCI account. If a user without a CircleCI account triggers a build, for example via a pull request on a repo, they are counted as an active user. A minimum of 25,000 credits will be charged per billing period, which enables up to 3 users to build on your repository.


Automating freight procurement with machine learning

This is the second post in our Supply Chain AI series. For an introduction to AI and how ML can automate freight logistics, read our first blog: How machine learning can help move your freight

Chocolate and peanut butter. Lennon and McCartney. Machine learning and freight. A few things in this world were destined to be together.

Convoy uses machine learning throughout the lifecycle of a shipment to make operations more efficient and reliable. In fact, our use of ML starts in the procurement phase, when we price and tender freight. 

Pricing contract freight accurately with machine learning

Most truckload rates are established through an RFP process in which a carrier bids on freight that a company (a shipper) needs to transport. You might wonder, how do carriers establish their prices for contracts? 

Traditional carriers price their bids based on a rudimentary cost-plus model, according to MIT Supply Chain Management. Cost-plus is exactly what it sounds like: taking the cost to operate a truck and add a markup. Unfortunately, this pricing method is prone to error, as it may not factor in how fluctuations in operating costs and demand shifts impact transactional rates. 

Convoy uses artificial intelligence to take a more comprehensive approach to pricing. Our supply chain machine learning models analyze millions of data points, including historical shipment records, near-time volume, capacity indicators, seasonality shifts, shipment time of day, macroeconomic factors, regional carrier density, carrier quality, and more. 

We have seven distinct machine learning models focused on pricing alone. These models analyze this data to estimate truck prices over the full duration of the contract. Every bid that we submit is based on the output of these models. 

By using machine learning, Convoy can forecast more accurately and price appropriately so that we accept a higher proportion of the loads we agree to take. How does this compare to the cost-plus option? 

Convoy’s tender acceptance rate is higher than 95% for contract freight, even in tight markets. This is much higher than the rest of the industry: an MIT study found that the average tender acceptance across US truckload was under 75% for primary carriers. Another MIT report found 80% tender acceptance, and that each rejection increased shipment costs by an average of 14.8%.

This saves shippers overall transportation costs, and brings greater predictability and reliability to their supply chain planning. 

Driving higher tender acceptance with AI

In addition to developing accurate pricing, machine learning boosts Convoy’s tender acceptance rates by using AI and ML to calculate the likelihood that we can match a load to a carrier in our network. 

This calculation informs our pricing and tender acceptance for contract freight. It also applies to accepting tender for backup freight (loads allocated through a shipper’s routing guide) and spot market freight. Central to this is a machine learning model that calculates what we call the “supply availability score.” 

We have a supply chain machine learning model that actively calculates a supply availability score for each load a shipper tenders to Convoy. This score determines the likeliness of our ability to find a truck and service the freight.

This involves analyzing both historical data and real-time variables that are constantly moving. The supply availability score is based on tender lead time, capacity in the market, the number of carriers in our network in the facility’s region, the required truck type, whether a lane can be batched with other Convoy loads, and other factors. 

Because our machine learning supply chain models operate continuously, we establish a supply availability score immediately after a load is tendered. This has a few key benefits: 

  • We accept tender quickly for contract freight 
  • We guarantee coverage for backup and spot freight, so the price you see is the price you get
  • We assign loads to carriers fast, so you spend less time wondering if your load is covered

Pricing and accepting the tender from the shipper is still only the beginning of the process. The next phase in machine learning is matching the load with the best truck for the job.

Finding a quality carrier for every shipment

Central to Convoy’s digital freight network is the matchmaking process of connecting loads with carriers. This is another area in which machine learning excels. 

If you add up the potential combinations of trucks in Convoy’s network with the loads that need to be transported, you wind up with billions of possible outcomes. This calculation is further complicated when you account for: 

  • Identifying the highest-quality carrier for the load
  • Ensuring we can offer shippers the best price possible
  • Covering a load as quickly as possible

Of course, at some point a judgement needs to be made. The lowest cost carrier may not have the highest on-time performance record. Weighing these is a complex measurement that requires analyzing massive sets of data that are constantly changing. Said another way: it’s the perfect place where machine learning can optimize supply chains. 

Prioritizing on-time performance and safety

Convoy’s machine learning models offer loads to carriers who are most likely to make pickups and deliveries safely and on time. When we tender loads, we’re willing to pay more to carriers who are the best match. This helps us source drivers who are less likely to fall off, more likely to be on time, and whose records show fewer safety incidents and cargo claims. 

Each time a carrier hauls with us, we collect more data on quality and performance. This data informs our machine learning model. When carriers make successful pickups and deliveries, we reward them by offering access to desirable loads. This motivates better performance throughout our network, translating to more reliable service for customers who ship with us.

Using AI to assign better loads, faster

One of the pain points that truckload carriers face is securing backhauls for their trips back home. Once making a drop, carriers may spend hours sifting through load boards for the shipment that takes them in the direction they want to travel. 

This is another pain point we addressed with machine learning. When a carrier uses Convoy’s app, our ML models curate the most relevant loads to them. We detect the driver’s current location, the type of trailer they’re hauling, and highlight nearby loads that will route carriers in the same direction as their homes.

What this means for shippers? Faster response times and less time wondering your load is covered.

Adding it up: Peace of mind with artificial intelligence

“If we plug it into Convoy, they’re doing the legwork for us and we know we can trust the system to get it taken care of. So we’ve been able to reduce the time we spend on a load almost by half.” – Encore Glass

By building our digital freight network on the foundation of machine learning technology, we’re able to rapidly advance supply chains by providing more accurate pricing, higher tender acceptance, and faster confirmed responses. This adds up to more certainty, stronger reliability, and more peace of mind for those who ship with Convoy — all before a load leaves their docks. 

For more information about machine learning and freight: 

Convoy Team

Convoy is the nation’s most efficient digital freight network. We move thousands of truckloads around the country each day through our optimized, connected network of carriers, saving money for shippers, increasing earnings for drivers, and eliminating carbon waste for our planet. We use technology and data to solve problems of waste and inefficiency in the $800B trucking industry, which generates over 87 million metric tons of wasted CO2 emissions from empty trucks. Fortune 500 shippers like Anheuser-Busch, P&G, Niagara, and Unilever trust Convoy to lower costs, increase logistics efficiency, and achieve environmental sustainability targets.
90,000 Car market experts named the reasons for the rise in car prices: Markets: Economy: Lenta.ru

Analysts of the Russian car market speculated about the situation in the industry, in which over the past six months, prices for new cars have increased by 5-10 percent, pulling the cost of cars from mileage, Izvestia writes.

Analysts of the Avilon Group of Companies argue that the rise in car prices has spurred currency fluctuations, inflation and a shortage of cars. Models of the Fiat brand became the leader in the rise in price, the maximum price level reached 13.2 percent.Hyundai, Kia, Volkswagen, Volvo increased their prices on average from 5 to 10 percent.

Car market analyst from JATO Dynamics Sergey Baranov believes that this year one can expect the same price increase as in the post-crisis 2016. And the dependence of production on foreign components only exacerbates the situation. Fewer and fewer car enthusiasts can buy a new car, so they go to the secondary market or extend the service life of an existing car. “As a result, the market is losing consumers.In total, by the end of 2021, growth will definitely exceed 10 percent, and we can see a repeat of the situation in 2016, when prices jumped by 15 percent, ”Baranov is sure.

Prices on the secondary car market follow the primary prices. “In general, all cars bought in 2019 and the first half of 2020 can definitely be sold for the same price. Due to the instability of the ruble exchange rate and the increase in the cost of a used car, Russians continue to regard the purchase of a car, even a supported one, as an investment, ”said Denis Migal, General Director of the Fresh Auto network of car dealerships.

At the same time, the cost of luxury segment cars remained almost unchanged, which is explained by the specifics of sales of such cars. They are supplied in small batches and are not sold according to the price list, since each model is equipped with additional options, after which the cost of the car increases by 10-15 percent. And after last year’s excitement for luxury cars, salons are experiencing a shortage of buyers and are attracting them, among other things, due to price containment, Sergey Baranov explained.

At the beginning of June, the Russians were warned of a new rise in the price of cars.Twenty-nine manufacturers changed prices for new cars in May, of which 27 brands rose in price. Prior to that, the rise in price was reported at the end of March. In the first quarter of 2021, auto prices went up by 2-8 percent.

Ubuntu Advantage Virtual Machine Pricing in the Advanced Tier | Microsoft Azure

OS or software:
CentOS or Ubuntu LinuxRed Hat Enterprise LinuxRed Hat Enterprise Linux with High AvailabilityRHEL for SAP with HARHEL for SAP Business ApplicationsSUSE Linux Enterprise only and patchesSUSE Linux Enterprise and 24/7 supportSUSE Linux Enterprise for HPC and 24/7 supportSUSE Linux Enterprise for SAP applications and 24/7 support Primary Service Type Ubuntu AdvantageUbuntu Advantage in the “Standard” pricing category Extended service type Ubuntu Advantage Machine Learning Server on Red Hat Enterprise Linux Machine Learning Server on Ubuntu or CentOS LinuxSQL Server Enterprise Ubuntu LinuxSQL Server Standard Ubuntu LinuxSQL Server Web Ubuntu LinuxSQL Server Enterprise Red Hat Enterprise LinuxSQL Server Standard Red Hat Enterprise LinuxSQL Server Web Red Hat Enterprise LinuxSQL Server Enterprise SUSE PrioritySQL Server Standard SUSE PrioritySQL Server Web SUSE Priority Windows OSBizTalk EnterpriseBizTalk StandardMachine Learning ServerSharePointSQL Server EnterpriseS QL Server StandardSQL Server Web

Category
AllUniversalOptimized computing powerOptimized memoryOptimized storageGPUHigh performance computing

Virtual Machine Series:
VseSeriya BsAv2 StandardSeriya Dav4Seriya Dasv4Seriya DCsv2Seriya Ddsv4Seriya Ddsv5Seriya Ddv4Seriya Ddv5Seriya Dv2Seriya Dsv2Seriya Dv3Seriya Dv4Seriya Dv5Seriya Dsv3Seriya Dsv4Seriya Dsv5Seriya Eav4Seriya Easv4Seriya Edsv4Seriya Edsv5Seriya Edv4Seriya Edv5Seriya Ev3Seriya Ev4Seriya Ev5Seriya Esv3Seriya Esv4Seriya Esv5Seriya FSeriya FsSeriya Fsv2Seriya FXSeriya GSeriya GsSeriya HReklamnaya action on a series of HSeriya HBSeriya HBv2Seriya HBv3Seriya HCSeriya LsSeriya Lsv2Seriya Mdsv2Seriya Msv2Seriya NCNCas_T4_v3 -series NC Series PromotionNCSv2 SeriesNCsv3 SeriesNDA100v4-seriesNDs SeriesNDv2 SeriesNP SeriesNV SeriesNVv3 SeriesM SeriesMv2

Series

Region:
Central USEast USEast US 2North Central USSouth Central USWest Central USWest USWest US 2West US 3UK SouthUK WestUAE CentralUAE NorthSwitzerland NorthSwitzerland WestNorway EastNorway WestKorea CentralKorea SouthJapan EastJapan WestCentral IndiaSouth IndiaWest IndiaGermany Central Germany (Germany) Northraignada EuropeCentral Europe South Brazil SoutheastUS Gov ArizonaUS Gov TexasUS Gov VirginiaAustralia CentralAustralia Central 2Australia EastAustralia SoutheastEast AsiaSoutheast AsiaSouth Africa NorthSouth Africa West

Currency:
US Dollar ($) Euro (€) British Pound (£) Australian Dollar ($) Indian Rupee (₹) Canadian Dollar ($) Australian Dollar ($) Argentine Peso ($) Brazilian Real (R $) British Pound (£) Hong Kong Dollar (HK $) Danish Krone (kr) US Dollar ($) Euro (€) Indian Rupee (₹) Indonesian Rupee (Rp) Canadian Dollar ($) Korean Won (₩) Malaysian Ringgit (RM $) Mexican Peso (MXN $) New Zealand dollar ($) Norwegian krone (kr) Russian ruble (rub) Saudi riyal (SR) Taiwan dollar (NT $) Turkish lira (TL) Swedish krona (kr) Swiss franc (chf) South African rand (R) Japanese yen (¥)

Show prices for:
HourMonth

90,000 What is the cost of a CASCO policy for a car in 2021

What determines the price of the CASCO policy

Compared to the compulsory type of insurance, the cost of CASCO is calculated by each company based on its own parameters.Moreover, there is the following list of standard criteria:

    1. Type of insurance by the number of possible cases, or the amount of payments. There is a choice between full and partial, aggregate and non-aggregate. The more possible insurance claims are indicated, the more voluntary car insurance costs.
    2. The age group the driver belongs to. For persons under 21 or over 65 years of age – a multiplying factor is applied.
    1. Driving experience.For more experienced drivers, the risk of accidents decreases, which also reduces the price range of Comprehensive Automobile Insurance Except Liability 2021.
    1. Type of the anti-theft system.
    1. Additional items covered by the insurance (audio system, tires, etc.).
    1. The number of drivers who are authorized to drive.
    1. The use or absence of the terms of the franchise in the contract.
    1. Parking place: guarded or regular.
    1. Vehicle scope: private or commercial.

The main parameter that is used to calculate the cost of this policy is the price category of the car at the time of contacting the insurance company.It is determined by the make, model of the car, the date of manufacture and the percentage of wear of the main units. Also, some car models are more often stolen, or appear in the statistics on road accidents.

Voluntary auto insurance cost in 2021

The pricing policy of companies for car insurance differs depending on the mileage of the car and the period of validity of the property insurance.

On a new car

Vehicles that have recently been assembled on a conveyor line and have not been used have a minimal likelihood of breakdown.There are more favorable tariffs for this type of transport. The average CASCO price range for a new car is in the range of 5-7% of the vehicle price.

On a credit car

A car that was purchased on credit is owned by a banking institution until the full amount is paid. Therefore, the credit institution proposes to issue a voluntary “Comprehensive Auto Insurance In addition to Liability” to reduce financial risks.Its price range depends on the technical condition of the car: with or without mileage.

How much does insurance cost for 3 months

A short-term policy, valid for approximately 90 days, is considered the most expensive. For its registration, similar operating costs for the payment of compensation are required, as for the annual, and the likelihood of an insured event is higher.

For half a year

The rates for voluntary car insurance for 6 months are cheaper than for 365 days.However, they exceed half the amount, and are equal to about 60-70% of the annual price. As a rule, some drivers try not to use a car in winter due to the increased likelihood of an accident and the difficulty of moving on snowy road surfaces.

For a year

The calculation of the annual CASCO tariff is also carried out taking into account many factors. At the same time, the maximum amount reaches 40% of the market price of a car. Standard car insurance conditions allow you to buy a policy for 4-12% of the cost of a car.

Is it possible to save money on voluntary auto insurance in 2021

All car owners can reduce the “Comprehensive Car Insurance” tariff for a new or used car. To do this, just use the following tips:

  1. Limit the list of cases for which compensation will be paid.
  1. For careful drivers, you can choose the aggregate type of policy, in which the sum insured is reduced after each event by the amount of payment.
  1. Install a modern anti-theft system. Typically, insurers vary their auto insurance rates for car models that are equipped with various anti-theft devices.
  1. Choose the best type of franchise – the amount of financial damage that is not reimbursed.
  1. Change of the organization-storager guarantees a discount.
  1. List the precautions that are taken to prevent theft.This allows you to find out the cost of car insurance online, taking into account the discount, when placed in a guarded parking lot.
  1. Report the absence of major or repeated accidents for all people who are entitled to drive your vehicle.
  1. Provide information on all previous policies to confirm the timeliness of their payment, compliance with traffic rules.
  1. If the car is used, then inspect it and, if necessary, repair it.The cost of CASCO in 2021 for non-new cars is determined after inspection at maintenance stations that cooperate with an insurance organization.
  1. Exclude from the indemnity contract all additional parts that are tuning or in the category of removable parts.

Answers to frequently asked questions

Is there insurance only against theft

Yes, it is called an “incomplete” complex format.It is almost impossible to buy such an option only from “hijacking” in Russia – organizations prefer not to provide services for this particular risk. Exceptions are several companies, which include: Intach-Insurance, Zetta-Insurance, Surgutneftegaz, RESO-Garantia. Other auto insurers offer an option with compensation from theft and with an optimized part for the risk of “damage” or with a large deductible (up to 70% of the cost of the car). Such conditions are indicated by Renaissance Insurance, Ingosstrakh, MAKS.

Conclusion

Regardless of the driving experience and driving style, it is advisable to periodically study the issue of insurance costs. This allows you to draw up a new contract on more favorable terms and get discounts. You can find out the cost of CASCO by model in 2021 for free using an online calculator.

VOLVO cars – high quality and reasonable price policy.

VOLVO cars are in rather active demand in our country.And not only new models. Car enthusiasts are increasingly attracted by the sale of used cars. Their quality is not satisfactory, and comprehensive pre-sale preparation allows you to completely protect yourself from the smallest problems. And the value of Volvo in trade is pleasing with its democratic character. You can choose a model from us on your own and with the help of our consultants. We never impose our opinion, but we are always ready to help in a difficult situation.

Range of VOLVO

models

In our showroom you can buy used cars of this brand on favorable terms for yourself.The following models are in the greatest demand among consumers:

The

  • S40 is a spacious and comfortable sedan that you will surely appreciate. The optimal balance of quality and affordable prices;
  • The

  • XC90 is a roomy crossover for city and off-road driving. Such a used car will not leave anyone indifferent;
  • The

  • S60 is a sedan that optimally combines comfort, reliability and safety. Such a purchase will be inexpensive for any budget;
  • The

  • XC70 is a practical all-rounder with a good technical base and well-thought-out ergonomics.The affordable cost did not affect the overall quality of this model;
  • The

  • XC60 is a powerful foreign crossover with a stylish exterior and roomy interior. You will definitely be delighted with such a machine.

Benefits of buying from us

The trade in Volvo offered by us has the following advantages for you:

  • Various exchange programs of your choice;
  • Fast sale of VOLVO without overpayments and unnecessary expenses;
  • Qualified help and technical support with every call;
  • Close interaction with official dealers in the capital and the region;
  • The best trade-in in the region and the most favorable conditions for regular partners.

Shipping Information

But that’s not all. We offer not only the purchase of Volvo in Moscow, but also its shipment to one of our regional centers. You yourself choose the place of arrival of the car, and we will help you deliver it within the agreed time frame. For more information on these issues and trade-in conditions, you can check on our page. The consultants will provide you with comprehensive answers as soon as possible.

Are you interested in our proposed Volvo trade in Moscow and the region? Fine.Then we are waiting for you in one of our showrooms. Do not hesitate to visit. Come. We are always glad to see you.

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AVTOVAZ Pricing Policy

The automotive industry in all countries is developing through the introduction of new models into production, containing the advanced achievements of science and technology.At the same time, the requirements for the safety of cars and their environmental friendliness are constantly being tightened. That is, the development of the automotive industries takes place in two main directions – the development of new models and the presentation by the state and consumers of more and more new requirements. Russia is no exception.

That is why, being the leading domestic car manufacturer, JSC AVTOVAZ is constantly working to improve the quality and consumer properties of LADA cars. It is no secret that a car of any brand can have defects that arise both during the warranty and post-warranty periods of operation.And the task of the automaker is to minimize their number, achieving the most optimal balance between price and quality.

Work to correct the existing deviations in the units and assemblies of the vehicle is a priority for JSC AVTOVAZ. In 2004, a lot of work was carried out in this direction by all divisions of JSC AVTOVAZ. A whole range of measures was implemented to improve the quality and consumer appeal of the car. These measures are carried out in two directions: firstly, the identified comments are eliminated during the warranty period, and, secondly, the reasons for the appearance of deviations directly in the production cycle are identified and eliminated.In the latter case, control over the quality of both its own components and the products of supplier enterprises is carried out. In connection with the growth of requirements for the quality of cars, the requirements for the supplied components and assemblies are constantly increasing. All these areas, of course, require additional costs and capital investments.

JSC AVTOVAZ does not stand still, and, for example, cars of the LADA 110 family, produced two or three years ago and they are also today, are quite different from the point of view of consumer properties.Changes have been introduced into the structure of the design that increase the safety of the car; additional options have been included in the complete sets that increase the level of comfort for both the driver and the passenger. The new engine, power steering – all this our consumers have already been able to evaluate on cars of the LADA 110 family.

It is clear that all these innovations require a lot of money. Therefore, one of the components of the efficiency of production and economic activities of an enterprise is its pricing policy.The peculiarities of its formation in JSC AVTOVAZ are commented by the vice-president of the enterprise for marketing, sales and technical maintenance of cars Vladimir Kuchay:

What influence does the market situation have on the formation of the selling price of a car?

The situation in the domestic automotive market in recent years has evolved according to fairly similar scenarios. The first half of each year is traditionally marked by an active growth in demand. The first half of 2004 was no exception, when the sales of our cars in the domestic market were noticeably higher than the rates of their production.But by the second half of the year, the demand had stabilized. On the whole, in 2004, the sales volumes of cars produced by AVTOVAZ were only slightly higher than the rates of their production, which influenced a slight decrease in car stocks at dealerships by the end of the year.

The growth in demand observed in the first half of each year, as a rule, also leads to an increase in retail prices for cars. This situation allows the manufacturer to consider the possibility of increasing the selling prices. In 2004, OJSC AVTOVAZ also increased its selling prices, mainly in the first half of the year.The last change in selling prices last year took place on 1 September.

What tasks of updating the model range did AVTOVAZ solve in 2004?

AVTOVAZ is actively involved in the technical re-equipment of production, updating its own model range, and modernizing already manufactured vehicles. So, in 2004, AVTOVAZ started mass production of classic-layout cars equipped with an electronic engine management system that meet the Euro-2 toxicity standards.The production of a new generation three-door hatchback LADA SAMARA has begun on the main conveyor. Since October last year, all cars of the LADA 110 family have been equipped with a new 1.6-liter engine. On November 18, a complex for the production of LADA KALINA cars was opened. This work requires significant investment. In the current situation, the main resource for obtaining such funds is our own profit, which is formed through the sale of cars.

Traditionally, one of the main competitive advantages of our cars is their price.Today there is an increase in the income of the population. For example, in 2003 the level of real money income of the population increased by 13.7%. In 2004, according to preliminary data, this figure was 10.8%. In these conditions, the price for the buyer is often no longer a decisive factor when choosing a car. The reliability of the car and the set of its consumer properties are becoming much more important. And an increase in the comfort and reliability of a car invariably leads to an increase in its value.

How does the change in selling prices by AVTOVAZ correlate with inflationary processes in the country’s economy in 2004?

In 2004, with a forecast of 10%, the level of consumer inflation in our country was approximately 11.5%.It should be borne in mind that inflation in industry is always higher than consumer inflation and, according to the Ministry of Economic Development, in 2004 amounted to about 29%. The presence of these processes in the economy also has a significant impact on the price of the car. It should be noted that with the mentioned inflation rates, the selling price of LADA cars in 2004 increased by 8.5%. Thus, the growth in selling prices for our cars lags significantly behind the inflation rate.

How will AVTOVAZ’s pricing policy be formed in 2005?

When considering the issue of changes in selling prices, we always begin with an analysis and assessment of the competitive situation in the market.The growth in sales of new foreign cars, especially in segments up to $ 15,000, clearly shows that consumer requirements for cars have changed significantly in recent years. Retail prices for some new foreign cars, especially those assembled in Russia, are at the turn of $ 10,000-12,000, while the price of LADA 110 family cars is $ 8,000. Naturally, we will approach the formation of pricing policy very carefully.

It is also worth noting that with any change in selling prices, AVTOVAZ focuses on the purchasing power of the population, which allows LADA cars to traditionally remain the most popular in the domestic market and available to many segments of consumers.

The expert has assessed the list of the most stolen cars in the Russian Federation

Hyundai Creta is one of the best-selling vehicles of the brand. This crossover is usually not sold in the secondary market and buyers usually insure it after purchase.

“The model is popular, and popular from all points of view. It is not surprising that the hijackers too. The car is bought new, so it can be resold profitably,” says Morjaretto.

The same theses can be applied to other cars of the brand – Tucson, Santa Fe and Solaris. All these are popular cars of the middle price segment, the owners of which prefer to insure them upon purchase, and the hijackers find it especially profitable to sell new models.

Separately, Morzaretto noted Kia Rio and Sportage – one of the most popular models of a passenger car in the Russian Federation. Its prevalence also causes a high number of thefts.

“There are a lot of such cars, they are easy to resell, disassemble, and then it is very difficult to find them,” he said.

Morzaretto associates the popularity of the premium Lexus lx with the hijackers with the fact that this model is now one of the most relevant in this price segment. And a large number of cars of a certain model always makes it easier for the hijackers to sell it.

“Models of premium brands are generally popular because you can get a lot of profit from one single sale. Lexus is always in the forefront here.”

sees reasons for changing this trend.

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