When we talk about prices, we often only talk about Lambda costs. But we rarey use only Lambda in our applications. Usually, we have other building blocks like API Gateway, data sources like SNS, SQS or Kinesis and Log service (Cloud Watch). Also, we store our data either in S3 or in serverless databases like DynamoDB or recently in Aurora Serverless. All these services have their own price models which we have to pay attention to. Moreover, we have to consider application data transfer costs. In this talk, we will draw the complete picture about the costs in the serverless applications, look at the Total Cost of Ownership and make some recommendations about when it’s worth using serverless and when the traditional approach (EC2)

1. It‘s not only Lamda
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

3. Ip.labs GmbH

4. Money for
nothing
Developers are good guys
They can ignore money...
Image: https://www.flickr.com/photos/pictures-of-money/17121925920/

5. Cloud changes things

6. Lambda settings

7. It‘s not only
Lamda…
...also consider 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 really have
to consider:
Total Cost of
Ownership

11. Lambda pricing model

12. Cost for lambda
REQUEST DURATION

13. Request Tier
$ 0.20
Per 1 Mio Requests

14. Duration Tier
$ 0.00001667
Per GB-Second

15. GB-Second
ONE SECOND ONE GB

16. Example
• 1 Mio requests
• Lambda with 512MB
• Each lambda takes 200ms
• 0.5GB * 0.2sec * 1Mio
= 100 000 GB-Seconds
Requests:
$0.20
GB-Seconds:
$1.67

17. Cost scales
linearly with
memory

18. How does this compare to
VM pricing model?

19. Lambda performance
compares to
• M4 (general purpose)
• M5 (general purpose)
EC2 Instance
Families

20. M5 General Purpose Instance
Cost scales linearly
with memory

21. Lamdba vs VM
https://www.youtube.com/watch?v=b1ebYEfQX1s

22. Lambda compute is
5x more expensive than M5

23. Threading Model
Idle Idle
Pay per wall clock time Pay per use

24. AWS monetizes the datacenter…
… by selling time on their servers
Image: https://aws.amazon.com

25. Common pitfalls when
working with lambda

26. More memory = more expensive?

27. Power tuning
lambda
• Executes different
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…
Lambda is often just a
small percentage
of your total cost

30. $ 3.50
Per million API calls
Api Gateway

31. Event Sources
• SQS and SNS are
charged for requests
• Kinesis charges for
shard hours & PUT
requests
Image: https://blog.binaris.com/lambda-pricing-pitfalls/

32. Event Sources
• Cost for Kinesis grows
with slower rate
• Attractive at to
operate at scale
Image: https://blog.binaris.com/lambda-pricing-pitfalls/

33. DynamoDB On-Demand
Image: https://aws.amazon.com/blogs/aws/amazon-dynamodb-on-demand-no-capacity-planning-and-pay-per-request-pricing/

34. Provisioned vs
On-Demand
• Use On-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 really have
to consider:
Total Cost of
Ownership

39. Ajay Nair (https://twitter.com/ajaynairthinks/status/1057696133076541441)

40. TCO Full Picture
Forrest Brazeal „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

42. FaaS...
…or not to FaaS

43. Image: https://stock.adobe.com

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 „Be as serverless as you can, but not more than that”
https://blog.christianposta.com/serverless/its-not-about-microservices-vs-serverless/

46. Explore phase
• Quickly validate
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
• Built something 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
• How much 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’t magically move
that all off to service
providers
• You can try to modernize
parts of them

50. FinDev Concept
Activity-based costing on 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/reactive
• 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?
• 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 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
• Cold start
• Lambda with and without VPC for
each runtime
• Invocation duration/ Timeouts
• Lambda 15min
• API Gateway integration 29sec
• Max Memory
• Lambda 3GB

57. Ajay Nair „Become a Serverless Black Belt” https://www.youtube.com/watch?v=oQFORsso2go

58. Ajay Nair „Become a Serverless Black Belt” https://www.youtube.com/watch?v=oQFORsso2go

59. Understand platform
limitations
• Cold start
• Lambda with and without VPC for
each runtime
• Invocation duration/timeouts
• Lambda 15min
• API Gateway integration 29sec
• Max Memory
• Lambda 3GB

60. Understand platform
limitations
• Max concurrent invocations
• Lambda 3000 per account
• Scaling thresholds
• Dynamo DB provisioned and on-
demand capacities

61. 1. Application lifecycle
2. Workloads
3. Platform limitations
4. Cost at scale
5. Operational constraints imposed by other dependencies
6. Organizational knowledge

62. Understand your cost at
scale
• Lambda
• API Gateway
• Dynamo DB capacity choices
• Remote API calls
• Logging costs
• Monitoring costs

63. 1. Application lifecycle
2. Workloads
3. Platform limitations
4. Cost at scale
5. Operational constraints imposed by other dependencies
6. Organizational knowledge

64. Understand the operational constraints
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/

65. 1. Application lifecycle
2. Workloads
3. Platform limitations
4. Cost at scale
5. Operational constraints imposed by other dependencies
6. Organizational knowledge

66. Understand organizational
knowledge
• Do you already have
DevOps knowledge?
• Are the developers willing to
learn new languages?

67. Future of Serverless

68. Simon Wardley https://twitter.com/swardley/status/1017074876887322627

69. Berkeley View on
Serverless Computing
“A Berkeley View on Serverless Computing” https://www2.eecs.berkeley.edu/Pubs/TechRpts/2019/EECS-2019-3.html

70. Berkeley View on
Serverless Computing
• Provide low latency and high IOPS
Serverless Ephemeral Storage
• Provide Serverless Durable Storage
• Improve Networking
• Improve security
• Accommodate cost-performance
“A Berkeley View on Serverless Computing” https://www2.eecs.berkeley.edu/Pubs/TechRpts/2019/EECS-2019-3.html

71. Predictions
• More available runtimes (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/

72. 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

74. Thank You!