Building IoT TCO calculators on AWS

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Building IoT TCO calculators on AWS

G P S T E C 4 1 4

Craig Williams

Principal Solutions Architect, IoT

Amazon Web Service

Agenda

Understanding cost components of an IoT solution

Modeling your solution

Building your pricing spreadsheet

Considerations for SaaS & multi-tenant providers

Hardware considerations


Components of an IoT solution

Components of an IoT solution

• Hardware costs

• Fixed asset AWS costs

• Amazon EC2

• Always model out a highly available solution

• Databases

• Amazon Aurora, Amazon Redshift

• Analytics

• Amazon SageMaker

• Amazon Kinesis

• Serverless components

• AWS IoT Core, all platform services

• AWS Lambda, Amazon DynamoDB, etc.

How to think about calculating your TCO

• Normalize service pricing

• Break down any service cost to daily cost, per message, per Gb

• Storage costs, Amazon S3 aggregate cost over time

• Don’t include tiering as a buffer for mistakes

• Fixed asset costs

• It’s not linear but try swag a # of devices per allocated instances to calculate cost at scale

• Device modeling vs. AWS IoT Greengrass/Gateway modeling

• Gateways reduces connectivity costs

• Reduce the amount of data sent to the cloud

• Architecture model

• Define how services are used across the solution

• Ultimate goal

• What does it cost per day for a device?

• What does it cost for each message a device sends or receives?


Building a TCO calculator

TCO spreadsheet

• Summary page

• Not 100% of your data is always processed

• Gateways can process x amount of data locally

• Service costing page

• Pricing normalization

• Architecture page

• How does our solution use services

Amazon S3 Storage

AWS IoT Core

AWS IoT Core

AWS IoT Analytics

AWS IoT Analytics

Summary pageSolution overview – Quick changes

o At-a-glance view of the solution cost

o Allow editing of critical solution

components

o Display serverless, fixed, and aggregate

costs

o Remember to calculate the cost of data storage

over time

o You can model device usage on this

page

o Different from the architecture, this can be how

a device itself is used such as 3 hours per day

Amazon S3 Storage

Service cost pageCost normalization

o Simplify to a daily cost, per message

cost or GB cost

o Cost units

o IoT Example

o $1 / 1,000,000 = $0.0000001 per message

o $0.15 / 1,000,000 = $0.00000015 per rule

(Action cost is the same)

o Amazon S3

o GB cost per day, assuming we store our data

o Fixed cost resources

o Always on monthly cost

o # of devices this can support

Elasticsearch=(0.151*24*2)/E3

Example, cost per device per day …

(Fixed resource)

AWS IoT Analytics

Core

Amazon S3 Storage

Architecture pageDefine service usage in your solution

o Costs are still per message per device

o For each rule there is a rule + action

cost

o Lambda

o Work out average compute time

o Work out memory requirements

Note: Remember to consider batching to optimize your Lambda usage and payload

sizes.

AWS IoT Analytics==((E5*'Summary Page'!B5/1024/1024/1024)*'Service Costs'!B25)*E25

Example, cost per message per device …

(serverless)

AWS IoT Analytics

AWS IoT Core

Example solution

AWS

IoT CoreAWS Lambda

Data source

Data source

AWS Lambda

AWS Lambda Amazon

DynamoDB

AWS IoT

AnalyticsAmazon S3

Amazon S3 Storage

Example solution with gateway

AWS

IoT CoreAWS Lambda

Data source

Data source

AWS Lambda

AWS Lambda Amazon

DynamoDB

AWS IoT

AnalyticsAmazon S3

AWS

IoT Greengrass

Filtering, ML, batching, lower

connectivity costsAmazon S3 Storage


Optimizing costs

Design considerations

• Batching

• Device to cloud

• AWS IoT Core to other AWS services

• Basic ingest

• Messages are routed directly to rule targets

• Payload sizes

• Billing is in 5k blocks, optimize for this size

• Shadows can be up to 8k and billed in 1k changes

• Use the correct service for ingest

• Not all data belongs on a broker

• Some services are better suited for constant queries

• Amazon DynamoDB vs registry calls vs shadow calls

Optimize payload sizes, downstream

processing

o Batching from device to cloud

o Optimizes the AWS IoT Core

message size

o Optimize the downstream

processing

o Services like Amazon Kinesis will

batch for you

o Execution improvements

o AWS Lambda can work more

efficiently

Buffering/Batching Data

AWS

IoT Core

Amazon

KinesisAWS Lambda

Buffering your data to optimize cost

Data source

Data source

Batch on device

Unpack in Lambda

Use the correct service for ingest and

cost optimization

o Not all data belongs on a broker

o Control is over the broker

o Expensive data such as logs, video,

or images can be sent directly to

streaming services

o Can be more cost effective

o Use credentials provider or TES for

security

Ingest options

AWS

IoT Core

Amazon

KinesisAWS Lambda

High volume telemetry, video, etc.

Two-way control channel, MQTT

Amazon S3

Data source

Data source

Using the best service for your type of data

AWS IoT

Analytics


Hardware costs

Elements of hardware costs

• Cost of development

• Team, balance of experience and new ideas, training, finding the right skills, turnover, recruiting

• Maintenance

• Firmware costs

• OTA vs. Truck roll

• Cloud costs

• Fixed and on-demand

• Customer service

• Optimize by

• Portability across families of product

• Adopt existing platforms

• Partner with Silicon, SI, and OS partners


Calculator links

Calculator links

• AWS IoT Core calculator – https://aws.amazon.com/iot-core/pricing/

• This is an adjustable spreadsheet you can download

• AWS Lambda calculator – https://s3.amazonaws.com/lambda-tools/pricing-calculator.html

• AWS Pricing calculator – https://calculator.s3.amazonaws.com/index.html

• AWS calculator – https://calculator.aws

• TCO calculator template – https://iotcalculator.s3.amazonaws.com/iottco.xlsx

https://calculator.aws/

Thank you!


Craig Williams

[email protected]://www.linkedin.com/in/typemismatch/@manicasteroid

https://www.linkedin.com/in/typemismatch/

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Building IoT TCO calculators on AWS

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