Windows pricing on AWS

There is also a version of this article on Strategic Blue blog

Intro

At Strategic Blue, we care about the money, and we’ve been collecting prices of AWS and other cloud vendors for a while. Another characteristic of Strategic Blue is that we are curious and love digging into the numbers, checking common assumptions, and find new ways to save money.

This article is one such analysis done to understand how much a Windows license costs on AWS and understand if Azure Windows Licenses are cheaper (as per common assumption).

Executive Summary

  • AWS Windows license pricing is very consistent, and only depends on the number of CPU for “current generation VM”.
  • AWS Windows license pricing is extremely inconsistent for older generations
  • Windows License price on AWS does not change between Regions
  • Surprise, Windows license on AWS and Azure is the same. i.e. Microsoft Windows license on Azure cost the same as AWS. (Tested on a subset of Azure pricing, but the numbers match for three decimals)
size winlicprice count
10xlarge $1.84/h 16
12xlarge $2.208/h 260
16xlarge $2.944/h 317
18xlarge $3.312/h 56
24xlarge $4.416/h 235
2xlarge $0.368/h 381
32xlarge $5.888/h 30
3xlarge $0.552/h 29
4xlarge $0.736/h 353
6xlarge $1.104/h 29
8xlarge $1.472/h 284
8xlarge $1.656/h 11
8xlarge $1.5/h 3
8xlarge $.523/h 1
8xlarge $1.543/h 1
9xlarge $1.656/h 56
large $0.092/h 333
xlarge $0.184/h 386

The price is per hour in US$ The c4.8xlarge have different prices per region, don’t ask me why

POMO

Purpose: to find if a good approximation of the cost of Windows licensing on AWS Objective: being able to separate infrastructure and licensing costs Method: SQL queries on AWS pricing table Outcome: A Windows license price list for AWS

Assumptions:

  • Work is done on shared instances
  • The licence cost is the difference between a windows instance price - Linux instance price
  • Based on march 2020 prices

Other possible experiments:

  • check RI pricing logic - are RI calculated on the infrastructure cost then a license is added
  • deduce RedHat/Suse license prices
  • deduce the SQL Server license prices
  • improve the queries
  • make this analysis automated

What can a Windows licensed instance run on AWS:

from https://aws.amazon.com/ec2/pricing/on-demand/ The pricing below includes the cost to run private and public AMIs on the specified operating system (“Windows Usage” prices apply to Windows Server 2003 R2, 2008, 2008 R2, 2012, 2012 R2, 2016, and 2019).

The dataset

The dataset is coming from Strategic Blue pricing data originated from the AWS price API. I did some changes to make it manageable in a PostgreSQL on my desktop.

The column used are instance, os, software, region, hourly_price, upfront_price, current_generation, term, tenancy, effective_month, license_model, purchase_option, offering_class, key.

Investigation

Regions

How many regions do we have in the table: 21

Asia Pacific (Tokyo), EU (London), US East (N. Virginia), Asia Pacific (Singapore), South America (Sao Paulo), Asia Pacific (Osaka-Local), US West (Oregon), Asia Pacific (Seoul), EU (Paris), Asia Pacific (Hong Kong), Canada (Central), AWS GovCloud (US-West), US East (Ohio), EU (Ireland), US West (N. California), Asia Pacific (Mumbai), Asia Pacific (Sydney), EU (Frankfurt), AWS GovCloud (US-East), Middle East (Bahrain), EU (Stockholm)

This information is essential as in most queries, the count column, represents the number of regions a specific price is valid. If count is 21, then all regions are charged the same.

queries

SELECT  COUNT(DISTINCT region)
from awspricing;
SELECT  DISTINCT region
from awspricing;

Getting the family, generation and size of instances

SELECT
  regexp_replace (t1.instance, '\..*$', '') gen,
  regexp_replace (t1.instance, '^[^\.]*.', '') size
FROM awspricing t1

Joining the rows of the window with the Linux rows

We assume that the Windows license price = Windows hourly price - Linux hourly price.

So in Database talk, we need to join the table awspricing with itself but connecting Windows rows with Linux rows and being able to get the Windows licence price.

To limit the quantity of information and focus on recent instances, we are going to only choose shared instances, of the current generation, without any additional software (e.g. SQL server). The pricing used is on-demand as it is the baseline for all other prices.

SELECT
  regexp_replace (t1.instance, '\..*$', '') gen,
  regexp_replace (t1.instance, '^[^\.]*.', '') size,
  round((t1.hourly_price - t2.hourly_price)::numeric, 3) AS WinLicPrice,
  count(*)
FROM awspricing AS t1 JOIN awspricing AS t2
  ON t1.instance = t2.instance
  AND t1.software = t2.software
  AND t1.region = t2.region
  AND t1.tenancy = t2.tenancy
  AND t1.license_model = t2.license_model
  AND t1.purchase_option = t2.purchase_option
  AND t1.offering_class = t2.offering_class
  AND t1.key = t2.key

WHERE t1.os = 'Windows'
  AND t2.os = 'Linux'
  AND t1.software = 'NA'
  AND t1.key = 'OD'
  AND t1.tenancy = 'Shared'
  AND t1.current_generation = 'Yes'
  AND regexp_replace (t1.instance, '^[^\.]*.', '') <> 'unit'

GROUP BY WinLicPrice, size, gen

ORDER BY size, count DESC, WinLicPrice, gen
;
gen size winlicprice count
m4 10xlarge 1.84 16
m5 12xlarge 2.208 21
m5d 12xlarge 2.208 21
r5 12xlarge 2.208 21
r5d 12xlarge 2.208 21
g4dn 12xlarge 2.208 19
c5 12xlarge 2.208 18
i3en 12xlarge 2.208 17
m5a 12xlarge 2.208 17
r5a 12xlarge 2.208 17
c5d 12xlarge 2.208 16
m5ad 12xlarge 2.208 16
r5ad 12xlarge 2.208 16
z1d 12xlarge 2.208 12
m5dn 12xlarge 2.208 7
m5n 12xlarge 2.208 7
r5dn 12xlarge 2.208 7
r5n 12xlarge 2.208 7
i3 16xlarge 2.944 21
m5 16xlarge 2.944 21
m5d 16xlarge 2.944 21
r5 16xlarge 2.944 21
r5d 16xlarge 2.944 21
g4dn 16xlarge 2.944 19
m5a 16xlarge 2.944 17
r4 16xlarge 2.944 17
r5a 16xlarge 2.944 17
x1 16xlarge 2.944 17
m4 16xlarge 2.944 16
m5ad 16xlarge 2.944 14
r5ad 16xlarge 2.944 14
g3 16xlarge 2.944 13
x1e 16xlarge 2.944 13
p3 16xlarge 2.944 12
p2 16xlarge 2.944 11
m5dn 16xlarge 2.944 7
m5n 16xlarge 2.944 7
r5dn 16xlarge 2.944 7
r5n 16xlarge 2.944 7
h1 16xlarge 2.944 4
c5 18xlarge 3.312 21
c5d 18xlarge 3.312 21
c5n 18xlarge 3.312 14
m5 24xlarge 4.416 21
m5d 24xlarge 4.416 21
r5 24xlarge 4.416 21
r5d 24xlarge 4.416 21
c5 24xlarge 4.416 18
i3en 24xlarge 4.416 17
m5a 24xlarge 4.416 17
r5a 24xlarge 4.416 17
c5d 24xlarge 4.416 16
m5ad 24xlarge 4.416 16
r5ad 24xlarge 4.416 16
m5dn 24xlarge 4.416 7
m5n 24xlarge 4.416 7
r5dn 24xlarge 4.416 7
r5n 24xlarge 4.416 7
p3dn 24xlarge 4.416 6
t3 2xlarge 0.147 21
c5 2xlarge 0.368 21
c5d 2xlarge 0.368 21
i3 2xlarge 0.368 21
m5 2xlarge 0.368 21
m5d 2xlarge 0.368 21
r5 2xlarge 0.368 21
r5d 2xlarge 0.368 21
g4dn 2xlarge 0.368 19
t2 2xlarge 0.062 17
t3a 2xlarge 0.147 17
i3en 2xlarge 0.368 17
m5a 2xlarge 0.368 17
r4 2xlarge 0.368 17
r5a 2xlarge 0.368 17
c4 2xlarge 0.368 16
m4 2xlarge 0.368 16
m5ad 2xlarge 0.368 16
r5ad 2xlarge 0.368 16
c5n 2xlarge 0.368 14
x1e 2xlarge 0.368 13
p3 2xlarge 0.368 12
z1d 2xlarge 0.368 12
d2 2xlarge 0.368 11
d2 2xlarge 0.221 8
m5dn 2xlarge 0.368 7
m5n 2xlarge 0.368 7
r5dn 2xlarge 0.368 7
r5n 2xlarge 0.368 7
h1 2xlarge 0.368 4
d2 2xlarge 0.296 1
x1 32xlarge 5.888 17
x1e 32xlarge 5.888 13
i3en 3xlarge 0.552 17
z1d 3xlarge 0.552 12
c5 4xlarge 0.736 21
c5d 4xlarge 0.736 21
i3 4xlarge 0.736 21
m5 4xlarge 0.736 21
m5d 4xlarge 0.736 21
r5 4xlarge 0.736 21
r5d 4xlarge 0.736 21
g4dn 4xlarge 0.736 19
m5a 4xlarge 0.736 17
r4 4xlarge 0.736 17
r5a 4xlarge 0.736 17
c4 4xlarge 0.736 16
m4 4xlarge 0.736 16
m5ad 4xlarge 0.736 16
r5ad 4xlarge 0.736 16
c5n 4xlarge 0.736 14
g3 4xlarge 0.736 13
x1e 4xlarge 0.736 13
d2 4xlarge 0.736 11
d2 4xlarge 0.302 8
m5dn 4xlarge 0.736 7
m5n 4xlarge 0.736 7
r5dn 4xlarge 0.736 7
r5n 4xlarge 0.736 7
h1 4xlarge 0.736 4
d2 4xlarge 0.592 1
i3en 6xlarge 1.104 17
z1d 6xlarge 1.104 12
i3 8xlarge 1.472 21
m5 8xlarge 1.472 21
m5d 8xlarge 1.472 21
r5 8xlarge 1.472 21
r5d 8xlarge 1.472 21
g4dn 8xlarge 1.472 19
m5a 8xlarge 1.472 17
r4 8xlarge 1.472 17
r5a 8xlarge 1.472 17
m5ad 8xlarge 1.472 14
r5ad 8xlarge 1.472 14
g3 8xlarge 1.472 13
x1e 8xlarge 1.472 13
p3 8xlarge 1.472 12
p2 8xlarge 1.472 11
c4 8xlarge 1.656 11
d2 8xlarge 1.656 11
d2 8xlarge 0.678 8
m5dn 8xlarge 1.472 7
m5n 8xlarge 1.472 7
r5dn 8xlarge 1.472 7
r5n 8xlarge 1.472 7
h1 8xlarge 1.472 4
c4 8xlarge 1.5 3
d2 8xlarge 1.332 1
c4 8xlarge 1.523 1
c4 8xlarge 1.543 1
c5 9xlarge 1.656 21
c5d 9xlarge 1.656 21
c5n 9xlarge 1.656 14
t3 large 0.028 21
c5 large 0.092 21
c5d large 0.092 21
i3 large 0.092 21
m5 large 0.092 21
m5d large 0.092 21
r5 large 0.092 21
r5d large 0.092 21
t2 large 0.028 17
t3a large 0.028 17
i3en large 0.092 17
m5a large 0.092 17
r4 large 0.092 17
r5a large 0.092 17
c4 large 0.092 16
m4 large 0.092 16
m5ad large 0.092 16
r5ad large 0.092 16
c5n large 0.092 14
z1d large 0.092 12
m5dn large 0.092 7
m5n large 0.092 7
r5dn large 0.092 7
r5n large 0.092 7
t3 medium 0.018 21
t2 medium 0.018 17
t3a medium 0.018 17
t3 micro 0.009 21
t2 micro 0.005 17
t3a micro 0.009 17
t3 nano 0.005 21
t2 nano 0.002 17
t3a nano 0.005 17
t3 small 0.018 21
t2 small 0.009 17
t3a small 0.018 17
t3 xlarge 0.074 21
c5 xlarge 0.184 21
c5d xlarge 0.184 21
i3 xlarge 0.184 21
m5 xlarge 0.184 21
m5d xlarge 0.184 21
r5 xlarge 0.184 21
r5d xlarge 0.184 21
g4dn xlarge 0.184 19
t2 xlarge 0.041 17
t3a xlarge 0.074 17
i3en xlarge 0.184 17
m5a xlarge 0.184 17
r4 xlarge 0.184 17
r5a xlarge 0.184 17
c4 xlarge 0.184 16
m4 xlarge 0.184 16
m5ad xlarge 0.184 16
r5ad xlarge 0.184 16
c5n xlarge 0.184 14
x1e xlarge 0.184 13
z1d xlarge 0.184 12
d2 xlarge 0.184 11
p2 xlarge 0.184 11
g3s xlarge 0.184 10
d2 xlarge 0.131 8
m5dn xlarge 0.184 7
m5n xlarge 0.184 7
r5dn xlarge 0.184 7
r5n xlarge 0.184 7
d2 xlarge 0.148 1

discoveries / anomalies

  • For non-current-generation, the numbers are all over the place
  • For current-generation, the numbers are consistent in all regions and size
  • Only size (i.e. vcpu) matters
  • c4 8xlarge is not consistent
  • d2 is the current generation, but should not be in my opinion
  • t2 and t3 and t3a have dedicated pricing, probably due to the nature of their CPU bursting capabilities

Query excluding the anomalies above

SELECT
  regexp_replace (t1.instance, '^[^\.]*.', '') size,
  round((t1.hourly_price - t2.hourly_price)::numeric, 3) AS WinLicPrice,
  count(*)
FROM awspricing AS t1 JOIN awspricing AS t2
  ON t1.instance = t2.instance
  AND t1.software = t2.software
  AND t1.region = t2.region
  AND t1.tenancy = t2.tenancy
  AND t1.license_model = t2.license_model
  AND t1.purchase_option = t2.purchase_option
  AND t1.offering_class = t2.offering_class
  AND t1.key = t2.key

WHERE t1.os = 'Windows'
  AND t2.os = 'Linux'
  AND t1.software = 'NA'
  AND t1.key = 'OD'
  AND t1.tenancy = 'Shared'
  AND t1.current_generation = 'Yes'
  AND regexp_replace (t1.instance, '\..*$', '') <> 't2'
  AND regexp_replace (t1.instance, '\..*$', '') <> 't3'
  AND regexp_replace (t1.instance, '\..*$', '') <> 't3a'
  AND regexp_replace (t1.instance, '\..*$', '') <> 'd2'
  AND regexp_replace (t1.instance, '^[^\.]*.', '') <> 'unit'

GROUP BY WinlicPrice, size --, gen

ORDER BY size, count DESC, winlicprice --, gen
;
size winlicprice count
10xlarge 1.84 16
12xlarge 2.208 260
16xlarge 2.944 317
18xlarge 3.312 56
24xlarge 4.416 235
2xlarge 0.368 381
32xlarge 5.888 30
3xlarge 0.552 29
4xlarge 0.736 353
6xlarge 1.104 29
8xlarge 1.472 284
8xlarge 1.656 11
8xlarge 1.5 3
8xlarge 1.523 1
8xlarge 1.543 1
9xlarge 1.656 56
large 0.092 333
xlarge 0.184 386

Again, the c4.8xlarge have different prices per region… don’t ask me why

t2 and t3 and t3a

SELECT
  regexp_replace (t1.instance, '\..*$', '') gen,
  regexp_replace (t1.instance, '^[^\.]*.', '') size,
  round((t1.hourly_price - t2.hourly_price)::numeric, 3) AS WinLicPrice,
  count(*)
FROM awspricing AS t1 JOIN awspricing AS t2
  ON t1.instance = t2.instance
  AND t1.software = t2.software
  AND t1.region = t2.region
  AND t1.tenancy = t2.tenancy
  AND t1.license_model = t2.license_model
  AND t1.purchase_option = t2.purchase_option
  AND t1.offering_class = t2.offering_class
  AND t1.key = t2.key

WHERE t1.os = 'Windows'
  AND t2.os = 'Linux'
  AND t1.software = 'NA'
  AND t1.key = 'OD'
  AND t1.tenancy = 'Shared'
  AND t1.current_generation = 'Yes'
  AND regexp_replace (t1.instance, '\..*$', '') IN ('t2', 't3', 't3a')
  AND regexp_replace (t1.instance, '^[^\.]*.', '') <> 'unit'

GROUP BY WinlicPrice, size ,gen

ORDER BY gen, size, count DESC, winlicprice
;
gen size winlicprice count
t2 2xlarge 0.062 17
t2 large 0.028 17
t2 medium 0.018 17
t2 micro 0.005 17
t2 nano 0.002 17
t2 small 0.009 17
t2 xlarge 0.041 17
t3 2xlarge 0.147 21
t3 large 0.028 21
t3 medium 0.018 21
t3 micro 0.009 21
t3 nano 0.005 21
t3 small 0.018 21
t3 xlarge 0.074 21
t3a 2xlarge 0.147 17
t3a large 0.028 17
t3a medium 0.018 17
t3a micro 0.009 17
t3a nano 0.005 17
t3a small 0.018 17
t3a xlarge 0.074 17

Monthly and yearly calculations

SELECT
  regexp_replace (t1.instance, '\..*$', '') gen,
  regexp_replace (t1.instance, '^[^\.]*.', '') size,
  round((t1.hourly_price - t2.hourly_price)::numeric, 3) AS WinLicPrice,
  round((t1.hourly_price - t2.hourly_price)::numeric, 3) * 730 AS WinLicPriceMonthly,
  round((t1.hourly_price - t2.hourly_price)::numeric, 3) * 730 * 12 AS WinLicPriceYearly,
  count(*)
FROM awspricing AS t1 JOIN awspricing AS t2
  ON t1.instance = t2.instance
  AND t1.software = t2.software
  AND t1.region = t2.region
  AND t1.tenancy = t2.tenancy
  AND t1.license_model = t2.license_model
  AND t1.purchase_option = t2.purchase_option
  AND t1.offering_class = t2.offering_class
  AND t1.key = t2.key

WHERE t1.os = 'Windows'
  AND t2.os = 'Linux'
  AND t1.software = 'NA'
  AND t1.key = 'OD'
  AND t1.tenancy = 'Shared'
  AND t1.current_generation = 'Yes'
  AND regexp_replace (t1.instance, '^[^\.]*.', '') <> 'unit'

GROUP BY WinLicPrice, size, gen

ORDER BY size, count DESC, WinLicPrice, gen
gen size winlicprice winlicpricemonthly winlicpriceyearly count
m4 10xlarge $1.84 $1,343.20 $16,118.40 16
m5 12xlarge $2.21 $1,611.84 $19,342.08 21
m5d 12xlarge $2.21 $1,611.84 $19,342.08 21
r5 12xlarge $2.21 $1,611.84 $19,342.08 21
r5d 12xlarge $2.21 $1,611.84 $19,342.08 21
g4dn 12xlarge $2.21 $1,611.84 $19,342.08 19
c5 12xlarge $2.21 $1,611.84 $19,342.08 18
i3en 12xlarge $2.21 $1,611.84 $19,342.08 17
m5a 12xlarge $2.21 $1,611.84 $19,342.08 17
r5a 12xlarge $2.21 $1,611.84 $19,342.08 17
c5d 12xlarge $2.21 $1,611.84 $19,342.08 16
m5ad 12xlarge $2.21 $1,611.84 $19,342.08 16
r5ad 12xlarge $2.21 $1,611.84 $19,342.08 16
z1d 12xlarge $2.21 $1,611.84 $19,342.08 12
m5dn 12xlarge $2.21 $1,611.84 $19,342.08 7
m5n 12xlarge $2.21 $1,611.84 $19,342.08 7
r5dn 12xlarge $2.21 $1,611.84 $19,342.08 7
r5n 12xlarge $2.21 $1,611.84 $19,342.08 7
i3 16xlarge $2.94 $2,149.12 $25,789.44 21
m5 16xlarge $2.94 $2,149.12 $25,789.44 21
m5d 16xlarge $2.94 $2,149.12 $25,789.44 21
r5 16xlarge $2.94 $2,149.12 $25,789.44 21
r5d 16xlarge $2.94 $2,149.12 $25,789.44 21
g4dn 16xlarge $2.94 $2,149.12 $25,789.44 19
m5a 16xlarge $2.94 $2,149.12 $25,789.44 17
r4 16xlarge $2.94 $2,149.12 $25,789.44 17
r5a 16xlarge $2.94 $2,149.12 $25,789.44 17
x1 16xlarge $2.94 $2,149.12 $25,789.44 17
m4 16xlarge $2.94 $2,149.12 $25,789.44 16
m5ad 16xlarge $2.94 $2,149.12 $25,789.44 14
r5ad 16xlarge $2.94 $2,149.12 $25,789.44 14
g3 16xlarge $2.94 $2,149.12 $25,789.44 13
x1e 16xlarge $2.94 $2,149.12 $25,789.44 13
p3 16xlarge $2.94 $2,149.12 $25,789.44 12
p2 16xlarge $2.94 $2,149.12 $25,789.44 11
m5dn 16xlarge $2.94 $2,149.12 $25,789.44 7
m5n 16xlarge $2.94 $2,149.12 $25,789.44 7
r5dn 16xlarge $2.94 $2,149.12 $25,789.44 7
r5n 16xlarge $2.94 $2,149.12 $25,789.44 7
h1 16xlarge $2.94 $2,149.12 $25,789.44 4
c5 18xlarge $3.31 $2,417.76 $29,013.12 21
c5d 18xlarge $3.31 $2,417.76 $29,013.12 21
c5n 18xlarge $3.31 $2,417.76 $29,013.12 14
m5 24xlarge $4.42 $3,223.68 $38,684.16 21
m5d 24xlarge $4.42 $3,223.68 $38,684.16 21
r5 24xlarge $4.42 $3,223.68 $38,684.16 21
r5d 24xlarge $4.42 $3,223.68 $38,684.16 21
c5 24xlarge $4.42 $3,223.68 $38,684.16 18
i3en 24xlarge $4.42 $3,223.68 $38,684.16 17
m5a 24xlarge $4.42 $3,223.68 $38,684.16 17
r5a 24xlarge $4.42 $3,223.68 $38,684.16 17
c5d 24xlarge $4.42 $3,223.68 $38,684.16 16
m5ad 24xlarge $4.42 $3,223.68 $38,684.16 16
r5ad 24xlarge $4.42 $3,223.68 $38,684.16 16
m5dn 24xlarge $4.42 $3,223.68 $38,684.16 7
m5n 24xlarge $4.42 $3,223.68 $38,684.16 7
r5dn 24xlarge $4.42 $3,223.68 $38,684.16 7
r5n 24xlarge $4.42 $3,223.68 $38,684.16 7
p3dn 24xlarge $4.42 $3,223.68 $38,684.16 6
t3 2xlarge $0.15 $107.31 $1,287.72 21
c5 2xlarge $0.37 $268.64 $3,223.68 21
c5d 2xlarge $0.37 $268.64 $3,223.68 21
i3 2xlarge $0.37 $268.64 $3,223.68 21
m5 2xlarge $0.37 $268.64 $3,223.68 21
m5d 2xlarge $0.37 $268.64 $3,223.68 21
r5 2xlarge $0.37 $268.64 $3,223.68 21
r5d 2xlarge $0.37 $268.64 $3,223.68 21
g4dn 2xlarge $0.37 $268.64 $3,223.68 19
t2 2xlarge $0.06 $45.26 $543.12 17
t3a 2xlarge $0.15 $107.31 $1,287.72 17
i3en 2xlarge $0.37 $268.64 $3,223.68 17
m5a 2xlarge $0.37 $268.64 $3,223.68 17
r4 2xlarge $0.37 $268.64 $3,223.68 17
r5a 2xlarge $0.37 $268.64 $3,223.68 17
c4 2xlarge $0.37 $268.64 $3,223.68 16
m4 2xlarge $0.37 $268.64 $3,223.68 16
m5ad 2xlarge $0.37 $268.64 $3,223.68 16
r5ad 2xlarge $0.37 $268.64 $3,223.68 16
c5n 2xlarge $0.37 $268.64 $3,223.68 14
x1e 2xlarge $0.37 $268.64 $3,223.68 13
p3 2xlarge $0.37 $268.64 $3,223.68 12
z1d 2xlarge $0.37 $268.64 $3,223.68 12
d2 2xlarge $0.37 $268.64 $3,223.68 11
d2 2xlarge $0.22 $161.33 $1,935.96 8
m5dn 2xlarge $0.37 $268.64 $3,223.68 7
m5n 2xlarge $0.37 $268.64 $3,223.68 7
r5dn 2xlarge $0.37 $268.64 $3,223.68 7
r5n 2xlarge $0.37 $268.64 $3,223.68 7
h1 2xlarge $0.37 $268.64 $3,223.68 4
d2 2xlarge $0.30 $216.08 $2,592.96 1
x1 32xlarge $5.89 $4,298.24 $51,578.88 17
x1e 32xlarge $5.89 $4,298.24 $51,578.88 13
i3en 3xlarge $0.55 $402.96 $4,835.52 17
z1d 3xlarge $0.55 $402.96 $4,835.52 12
c5 4xlarge $0.74 $537.28 $6,447.36 21
c5d 4xlarge $0.74 $537.28 $6,447.36 21
i3 4xlarge $0.74 $537.28 $6,447.36 21
m5 4xlarge $0.74 $537.28 $6,447.36 21
m5d 4xlarge $0.74 $537.28 $6,447.36 21
r5 4xlarge $0.74 $537.28 $6,447.36 21
r5d 4xlarge $0.74 $537.28 $6,447.36 21
g4dn 4xlarge $0.74 $537.28 $6,447.36 19
m5a 4xlarge $0.74 $537.28 $6,447.36 17
r4 4xlarge $0.74 $537.28 $6,447.36 17
r5a 4xlarge $0.74 $537.28 $6,447.36 17
c4 4xlarge $0.74 $537.28 $6,447.36 16
m4 4xlarge $0.74 $537.28 $6,447.36 16
m5ad 4xlarge $0.74 $537.28 $6,447.36 16
r5ad 4xlarge $0.74 $537.28 $6,447.36 16
c5n 4xlarge $0.74 $537.28 $6,447.36 14
g3 4xlarge $0.74 $537.28 $6,447.36 13
x1e 4xlarge $0.74 $537.28 $6,447.36 13
d2 4xlarge $0.74 $537.28 $6,447.36 11
d2 4xlarge $0.30 $220.46 $2,645.52 8
m5dn 4xlarge $0.74 $537.28 $6,447.36 7
m5n 4xlarge $0.74 $537.28 $6,447.36 7
r5dn 4xlarge $0.74 $537.28 $6,447.36 7
r5n 4xlarge $0.74 $537.28 $6,447.36 7
h1 4xlarge $0.74 $537.28 $6,447.36 4
d2 4xlarge $0.59 $432.16 $5,185.92 1
i3en 6xlarge $1.10 $805.92 $9,671.04 17
z1d 6xlarge $1.10 $805.92 $9,671.04 12
i3 8xlarge $1.47 $1,074.56 $12,894.72 21
m5 8xlarge $1.47 $1,074.56 $12,894.72 21
m5d 8xlarge $1.47 $1,074.56 $12,894.72 21
r5 8xlarge $1.47 $1,074.56 $12,894.72 21
r5d 8xlarge $1.47 $1,074.56 $12,894.72 21
g4dn 8xlarge $1.47 $1,074.56 $12,894.72 19
m5a 8xlarge $1.47 $1,074.56 $12,894.72 17
r4 8xlarge $1.47 $1,074.56 $12,894.72 17
r5a 8xlarge $1.47 $1,074.56 $12,894.72 17
m5ad 8xlarge $1.47 $1,074.56 $12,894.72 14
r5ad 8xlarge $1.47 $1,074.56 $12,894.72 14
g3 8xlarge $1.47 $1,074.56 $12,894.72 13
x1e 8xlarge $1.47 $1,074.56 $12,894.72 13
p3 8xlarge $1.47 $1,074.56 $12,894.72 12
p2 8xlarge $1.47 $1,074.56 $12,894.72 11
c4 8xlarge $1.66 $1,208.88 $14,506.56 11
d2 8xlarge $1.66 $1,208.88 $14,506.56 11
d2 8xlarge $0.68 $494.94 $5,939.28 8
m5dn 8xlarge $1.47 $1,074.56 $12,894.72 7
m5n 8xlarge $1.47 $1,074.56 $12,894.72 7
r5dn 8xlarge $1.47 $1,074.56 $12,894.72 7
r5n 8xlarge $1.47 $1,074.56 $12,894.72 7
h1 8xlarge $1.47 $1,074.56 $12,894.72 4
c4 8xlarge $1.50 $1,095.00 $13,140.00 3
d2 8xlarge $1.33 $972.36 $11,668.32 1
c4 8xlarge $1.52 $1,111.79 $13,341.48 1
c4 8xlarge $1.54 $1,126.39 $13,516.68 1
c5 9xlarge $1.66 $1,208.88 $14,506.56 21
c5d 9xlarge $1.66 $1,208.88 $14,506.56 21
c5n 9xlarge $1.66 $1,208.88 $14,506.56 14
t3 large $0.03 $20.44 $245.28 21
c5 large $0.09 $67.16 $805.92 21
c5d large $0.09 $67.16 $805.92 21
i3 large $0.09 $67.16 $805.92 21
m5 large $0.09 $67.16 $805.92 21
m5d large $0.09 $67.16 $805.92 21
r5 large $0.09 $67.16 $805.92 21
r5d large $0.09 $67.16 $805.92 21
t2 large $0.03 $20.44 $245.28 17
t3a large $0.03 $20.44 $245.28 17
i3en large $0.09 $67.16 $805.92 17
m5a large $0.09 $67.16 $805.92 17
r4 large $0.09 $67.16 $805.92 17
r5a large $0.09 $67.16 $805.92 17
c4 large $0.09 $67.16 $805.92 16
m4 large $0.09 $67.16 $805.92 16
m5ad large $0.09 $67.16 $805.92 16
r5ad large $0.09 $67.16 $805.92 16
c5n large $0.09 $67.16 $805.92 14
z1d large $0.09 $67.16 $805.92 12
m5dn large $0.09 $67.16 $805.92 7
m5n large $0.09 $67.16 $805.92 7
r5dn large $0.09 $67.16 $805.92 7
r5n large $0.09 $67.16 $805.92 7
t3 medium $0.02 $13.14 $157.68 21
t2 medium $0.02 $13.14 $157.68 17
t3a medium $0.02 $13.14 $157.68 17
t3 micro $0.01 $6.57 $78.84 21
t2 micro $0.01 $3.65 $43.80 17
t3a micro $0.01 $6.57 $78.84 17
t3 nano $0.01 $3.65 $43.80 21
t2 nano $0.00 $1.46 $17.52 17
t3a nano $0.01 $3.65 $43.80 17
t3 small $0.02 $13.14 $157.68 21
t2 small $0.01 $6.57 $78.84 17
t3a small $0.02 $13.14 $157.68 17
t3 xlarge $0.07 $54.02 $648.24 21
c5 xlarge $0.18 $134.32 $1,611.84 21
c5d xlarge $0.18 $134.32 $1,611.84 21
i3 xlarge $0.18 $134.32 $1,611.84 21
m5 xlarge $0.18 $134.32 $1,611.84 21
m5d xlarge $0.18 $134.32 $1,611.84 21
r5 xlarge $0.18 $134.32 $1,611.84 21
r5d xlarge $0.18 $134.32 $1,611.84 21
g4dn xlarge $0.18 $134.32 $1,611.84 19
t2 xlarge $0.04 $29.93 $359.16 17
t3a xlarge $0.07 $54.02 $648.24 17
i3en xlarge $0.18 $134.32 $1,611.84 17
m5a xlarge $0.18 $134.32 $1,611.84 17
r4 xlarge $0.18 $134.32 $1,611.84 17
r5a xlarge $0.18 $134.32 $1,611.84 17
c4 xlarge $0.18 $134.32 $1,611.84 16
m4 xlarge $0.18 $134.32 $1,611.84 16
m5ad xlarge $0.18 $134.32 $1,611.84 16
r5ad xlarge $0.18 $134.32 $1,611.84 16
c5n xlarge $0.18 $134.32 $1,611.84 14
x1e xlarge $0.18 $134.32 $1,611.84 13
z1d xlarge $0.18 $134.32 $1,611.84 12
d2 xlarge $0.18 $134.32 $1,611.84 11
p2 xlarge $0.18 $134.32 $1,611.84 11
g3s xlarge $0.18 $134.32 $1,611.84 10
d2 xlarge $0.13 $95.63 $1,147.56 8
m5dn xlarge $0.18 $134.32 $1,611.84 7
m5n xlarge $0.18 $134.32 $1,611.84 7
r5dn xlarge $0.18 $134.32 $1,611.84 7
r5n xlarge $0.18 $134.32 $1,611.84 7
d2 xlarge $0.15 $108.04 $1,296.48 1
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