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"It’s not only Lambda! Economics behind Serverless" at JAX Conference in Mai 2019 by Christian Bannes and Vadym Kazulkin (both ip.labs GmbH)
The document discusses the economics of serverless computing, particularly AWS Lambda, emphasizing the total cost of ownership which includes factors like memory usage, API calls, and other associated services. It contrasts serverless architectures with traditional VM setups, pointing out potential pitfalls and cost management strategies. Moreover, it highlights the importance of understanding application lifecycles, workloads, and platform limitations to fully leverage serverless benefits while mitigating costs.
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Similar to "It’s not only Lambda! Economics behind Serverless" at JAX Conference in Mai 2019 by Christian Bannes and Vadym Kazulkin (both ip.labs GmbH)
"It’s not only Lambda! Economics behind Serverless" at JAX Conference in Mai 2019 by Christian Bannes and Vadym Kazulkin (both ip.labs GmbH)
- 1. It‘s not onlyLamda Economics behind serverless by Christian Bannes and Vadym Kazulkin, ip.labs GmbH
- 2. Contact Vadym Kazulkin ip.labs GmbH v.kazulkin@iplabs.de xing.com/profile/Vadym_Kazulkin @VKazulkin @ServerlessBonn(Meetup) Christian Bannes ip.labs GmbH c.bannes@iplabs.de xing.com/profile/Christian_Bannes
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- 4. Money for nothing Developers aregood guys They can ignore money... Image: https://www.flickr.com/photos/pictures-of-money/17121925920/
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- 7. It‘s not only Lamda… ...alsoconsider the infrastructure Image: https://stocksnap.io
- 8. You also need… •Storage • Databases • Queues • Api Gateway • Logging • Data Transfer • And More ... Image: https://stocksnap.io
- 9. Most expensive… ...are people Image:https://burst.shopify.com
- 10. What you reallyhave to consider: Total Cost of Ownership
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- 12. Cost for lambda REQUESTDURATION
- 13. Request Tier $ 0.20 Per1 Mio Requests
- 14. Duration Tier $ 0.00001667 PerGB-Second
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- 16. Example • 1 Miorequests • Lambda with 512MB • Each lambda takes 200ms • 0.5GB * 0.2sec * 1Mio = 100 000 GB-Seconds Requests: $0.20 GB-Seconds: $1.67
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- 18. How does thiscompare to VM pricing model?
- 19. Lambda performance compares to •M4 (general purpose) • M5 (general purpose) EC2 Instance Families
- 20. M5 General PurposeInstance Cost scales linearly with memory
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- 22. Lambda compute is 5xmore expensive than M5
- 23. Threading Model Idle Idle Payper wall clock time Pay per use
- 24. AWS monetizes thedatacenter… … by selling time on their servers Image: https://aws.amazon.com
- 25. Common pitfalls when workingwith lambda
- 26. More memory =more expensive?
- 27. Power tuning lambda • Executesdifferent settings in parallel • Outputs the optimal setting Image: https://github.com/alexcasalboni/aws-lambda-power-tuning
- 28. Paying for wait time Image:https://blog.binaris.com/lambda-pricing-pitfalls/
- 29. The reality is… Lambdais often just a small percentage of your total cost
- 30. $ 3.50 Per millionAPI calls Api Gateway
- 31. Event Sources • SQSand SNS are charged for requests • Kinesis charges for shard hours & PUT requests Image: https://blog.binaris.com/lambda-pricing-pitfalls/
- 32. Event Sources • Costfor Kinesis grows with slower rate • Attractive at to operate at scale Image: https://blog.binaris.com/lambda-pricing-pitfalls/
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- 34. Provisioned vs On-Demand • UseOn-Demand for spiky workloads • Use Provisioned for constantly high workload
- 35. Cost saving with On-Demand Image:https://twitter.com Switch to "On-Demand"
- 36. Example $2400 / Month$520 / Month 100 API requests per second 24/7 • API Gateway • Lambda with 1GB and 300ms per request • Load balancer • 3 x M5.xlarge with 16GB each
- 37. Serverless vs VM •"Pay as you go" can significantly reduce server cost because you don’t pay for ide time • Cost for compute is always higher • For serverless you need right workload and architecture • For constantly high workloads it can quickly become expensive
- 38. What you reallyhave to consider: Total Cost of Ownership
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- 40. TCO Full Picture ForrestBrazeal „The Business Case For Serverless” https://www.trek10.com/blog/business-case-for-serverless/ No Infrastructure Maintanence Auto Scaling Built in Fewer Engineers Required Lower Technical Debt Focus on Business Value & Innovation Faster Time to Market
- 41. Do more with(Server-)less
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- 44. 1. Application lifecycle 2.Workloads 3. Platform limitations 4. Cost at scale 5. Operational constraints 6. Organizational knowledge Decision Checklist
- 45. Understand Application lifecycle Christian Posta „Beas serverless as you can, but not more than that” https://blog.christianposta.com/serverless/its-not-about-microservices-vs-serverless/
- 46. Explore phase • Quicklyvalidate hypotheses • Rapidly experiment • Run experiments as cheaply as possible Image: burst.shopify.com/photos/a-look-across-the-landscape-with-view-of-the-sea Serverless is a perfect fit
- 47. Exploit phase • Builtsomething that does provide customer value • Build it on scale • Build a profitable product around it Image: Robert Scoble via Flickr partly serverless and partly not serverless architecture
- 48. Application lifecycle • Howmuch of my stack should I own to be able to deliver business value? • Outsource SLA, regulatory compliance, price, and roadmap to my service provider?
- 49. Existing applications • You can’tmagically move that all off to service providers • You can try to modernize parts of them
- 50. FinDev Concept Activity-based costingon a digital operation-by-operation basis • Figure out features which deliver business value comparing to their cost Aleksander Simovic & Mark Schwarz „FinDev and Serverless Microeconomics: Part 1” https://aws.amazon.com/de/blogs/enterprise-strategy/findev-and-serverless-microeconomics-part-1/
- 51. 1. Application lifecycle 2.Workloads 3. Platform limitations 4. Cost at scale 5. Operational constraints imposed by other dependencies 6. Organizational knowledge
- 52. Understand your workloads • Event-driven •API-driven • Batch Job • ML/AI • Big Data Image: flickr.com/photos/everywhereatonce/294789504
- 53. Understand your workloads •Do we need to access specialized hardware ? • GPU access required? • Another RAM/CPU ratio? • Do we need constantly high performance? • Response time below 100 ms (e.g. bidding or gaming platforms) “A Berkeley View on Serverless Computing” https://www2.eecs.berkeley.edu/Pubs/TechRpts/2019/EECS-2019-3.html
- 54. Understand your workloads •Do we need high throughput ? • Lambda‘s network bandwidth is limited shared between all functions packed on the same VM • Do functions need to communicate with each other? • functions are not directly network accessible, they must communicate via an intermediary service “A Berkeley View on Serverless Computing” https://www2.eecs.berkeley.edu/Pubs/TechRpts/2019/EECS-2019-3.html
- 55. 1. Application lifecycle 2.Workloads 3. Platform limitations 4. Cost at scale 5. Operational constraints imposed by other dependencies 6. Organizational knowledge
- 56. Understand platform limitations • Coldstart • Lambda with and without VPC for each runtime • Invocation duration/ Timeouts • Lambda 15min • API Gateway integration 29sec • Max Memory • Lambda 3GB
- 57. Ajay Nair „Becomea Serverless Black Belt” https://www.youtube.com/watch?v=oQFORsso2go
- 58. Ajay Nair „Becomea Serverless Black Belt” https://www.youtube.com/watch?v=oQFORsso2go
- 59. Don‘t be scared ofcold starts
- 60. Understand platform limitations • Coldstart • Lambda with and without VPC for each runtime • Invocation duration/timeouts • Lambda 15min • API Gateway integration 29sec • Max Memory • Lambda 3GB
- 61. Understand platform limitations • Maxconcurrent invocations • Lambda 3000 per account • Scaling thresholds • Dynamo DB provisioned and on- demand capacities
- 62. 1. Application lifecycle 2.Workloads 3. Platform limitations 4. Cost at scale 5. Operational constraints imposed by other dependencies 6. Organizational knowledge
- 63. Understand your costat scale • Lambda • API Gateway • Dynamo DB capacity choices • Remote API calls • Logging costs • Monitoring costs
- 64. 1. Application lifecycle 2.Workloads 3. Platform limitations 4. Cost at scale 5. Operational constraints imposed by other dependencies 6. Organizational knowledge
- 65. Understand the operationalconstraints imposed by other dependencies • Language runtime choice • Can be forced by some dependencies to other libraries • Relation database choice • Aurora Serverless: either use VPC or use Data API Yan Cui „Not so FaaS” https://blog.binaris.com/not-so-faas/
- 66. 1. Application lifecycle 2.Workloads 3. Platform limitations 4. Cost at scale 5. Operational constraints imposed by other dependencies 6. Organizational knowledge
- 67. Understand organizational knowledge • Doyou already have DevOps knowledge? • Are the developers willing to learn new languages?
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- 70. Berkeley View on ServerlessComputing “A Berkeley View on Serverless Computing” https://www2.eecs.berkeley.edu/Pubs/TechRpts/2019/EECS-2019-3.html
- 71. Berkeley View on ServerlessComputing • Provide low latency and high IOPS Serverless Ephemeral Storage • Provide Serverless Durable Storage • Improve Networking and Performance • Improve Security • Accommodate cost-performance “A Berkeley View on Serverless Computing” https://www2.eecs.berkeley.edu/Pubs/TechRpts/2019/EECS-2019-3.html
- 72. Predictions • More availableruntimes (e.g. GraalVM) • Platform limitations will be weakened or disappear • New use cases of serverless (Big Data, ML/AI) • Access to specialized hardware • Companies gaining advantages from the FinDev approach Image: Marco Verch via Flickr (Creative Commons 2.0) https://www.flickr.com/photos/30478819@N08/26528539689/
- 73. Picture from SRV409session in AWS re:Invent 2018 https://www.youtube.com/watch?v=QdzV04T_kec
- 74. Picture from SRV409session in AWS re:Invent 2018 https://www.youtube.com/watch?v=QdzV04T_kec
- 75. Predictions • More availableruntimes (e.g. GraalVM) • Platform limitations will be weakened or disappear • New use cases of serverless (Big Data, ML/AI) • Access to specialized hardware • Companies gaining advantages from the FinDev approach Image: Marco Verch via Flickr (Creative Commons 2.0) https://www.flickr.com/photos/30478819@N08/26528539689/
- 76. Serverless vs VM •"Pay as you go" can significantly reduce server cost because you don’t pay for ide time • Cost for compute is always higher • For serverless you need right workload and architecture • For constantly high workloads it can quickly become expensive • Application lifecycle • Workloads • Platform limitations • Cost at scale • Operational constraints • Organizational knowledge FaaS or not to FaaS
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