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  1. Stackups
  2. Application & Data
  3. Serverless
  4. Serverless Task Processing
  5. AWS Lambda vs AWS Step Functions

AWS Lambda vs AWS Step Functions

OverviewDecisionsComparisonAlternatives

Overview

AWS Lambda
AWS Lambda
Stacks26.0K
Followers18.8K
Votes432
AWS Step Functions
AWS Step Functions
Stacks237
Followers391
Votes31

AWS Lambda vs AWS Step Functions: What are the differences?

# AWS Lambda vs AWS Step Functions

In this comparison, we will highlight key differences between AWS Lambda and AWS Step Functions.

1. **Execution Model**: AWS Lambda is a serverless computing service that allows you to run code without provisioning or managing servers. It is event-driven and operates on a per-request basis, executing functions in response to events. On the other hand, AWS Step Functions are a serverless orchestrator that enables you to coordinate multiple AWS services into serverless workflows. Step Functions allow you to define complex state machines with different states and transitions between them.

2. **Scalability**: AWS Lambda automatically scales based on the number of incoming requests, meaning it can handle a large volume of requests simultaneously without manual intervention. In contrast, AWS Step Functions are designed for orchestration and coordination of AWS services rather than handling high request volumes directly. Step Functions focus on managing the flow of execution between different service integrations.

3. **Use Cases**: AWS Lambda is commonly used for event-driven applications, real-time file processing, and background processing tasks. It is ideal for running small, individual functions in response to triggers. AWS Step Functions, on the other hand, are best suited for coordinating multiple AWS services in a workflow, handling long-running processes, state management, and error handling within complex applications.

4. **State Management**: AWS Lambda functions are stateless, meaning they do not maintain any state between invocations unless external services like databases are used to store state. In contrast, AWS Step Functions provide built-in state management capabilities, allowing you to track the state of a workflow, handle retries, and manage checkpoints within the workflow execution.

5. **Error Handling**: AWS Lambda provides basic error handling through retries and logging, requiring you to implement custom error handling logic within your function code. On the other hand, AWS Step Functions offer built-in error handling features, such as catching and retrying failed steps, defining catch handlers for different error types, and transitioning to specific states based on errors.

6. **Cost Structure**: AWS Lambda operates on a pay-per-use pricing model where you are charged based on the number of requests and the duration of code execution. In comparison, AWS Step Functions have a different pricing structure based on state transitions and state machine executions, with additional costs for API calls and data processing within workflows.

In Summary, AWS Lambda is ideal for executing individual functions in response to events, while AWS Step Functions excel at orchestrating complex workflows involving multiple AWS services and managing state transitions efficiently.

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Advice on AWS Lambda, AWS Step Functions

Tim
Tim

CTO at Checkly Inc.

Sep 18, 2019

Needs adviceonHerokuHerokuAWS LambdaAWS Lambda

When adding a new feature to Checkly rearchitecting some older piece, I tend to pick Heroku for rolling it out. But not always, because sometimes I pick AWS Lambda . The short story:

  • Developer Experience trumps everything.
  • AWS Lambda is cheap. Up to a limit though. This impact not only your wallet.
  • If you need geographic spread, AWS is lonely at the top.

The setup

Recently, I was doing a brainstorm at a startup here in Berlin on the future of their infrastructure. They were ready to move on from their initial, almost 100% Ec2 + Chef based setup. Everything was on the table. But we crossed out a lot quite quickly:

  • Pure, uncut, self hosted Kubernetes — way too much complexity
  • Managed Kubernetes in various flavors — still too much complexity
  • Zeit — Maybe, but no Docker support
  • Elastic Beanstalk — Maybe, bit old but does the job
  • Heroku
  • Lambda

It became clear a mix of PaaS and FaaS was the way to go. What a surprise! That is exactly what I use for Checkly! But when do you pick which model?

I chopped that question up into the following categories:

  • Developer Experience / DX 🤓
  • Ops Experience / OX 🐂 (?)
  • Cost 💵
  • Lock in 🔐

Read the full post linked below for all details

357k views357k
Comments

Detailed Comparison

AWS Lambda
AWS Lambda
AWS Step Functions
AWS Step Functions

AWS Lambda is a compute service that runs your code in response to events and automatically manages the underlying compute resources for you. You can use AWS Lambda to extend other AWS services with custom logic, or create your own back-end services that operate at AWS scale, performance, and security.

AWS Step Functions makes it easy to coordinate the components of distributed applications and microservices using visual workflows. Building applications from individual components that each perform a discrete function lets you scale and change applications quickly.

Extend other AWS services with custom logic;Build custom back-end services;Completely Automated Administration;Built-in Fault Tolerance;Automatic Scaling;Integrated Security Model;Bring Your Own Code;Pay Per Use;Flexible Resource Model
-
Statistics
Stacks
26.0K
Stacks
237
Followers
18.8K
Followers
391
Votes
432
Votes
31
Pros & Cons
Pros
  • 129
    No infrastructure
  • 83
    Cheap
  • 70
    Quick
  • 59
    Stateless
  • 47
    No deploy, no server, great sleep
Cons
  • 7
    Cant execute ruby or go
  • 3
    Compute time limited
  • 1
    Can't execute PHP w/o significant effort
Pros
  • 7
    Integration with other services
  • 5
    Pricing
  • 5
    Complex workflows
  • 5
    Easily Accessible via AWS Console
  • 3
    Workflow Processing

What are some alternatives to AWS Lambda, AWS Step Functions?

Azure Functions

Azure Functions

Azure Functions is an event driven, compute-on-demand experience that extends the existing Azure application platform with capabilities to implement code triggered by events occurring in virtually any Azure or 3rd party service as well as on-premises systems.

Google Cloud Run

Google Cloud Run

A managed compute platform that enables you to run stateless containers that are invocable via HTTP requests. It's serverless by abstracting away all infrastructure management.

Serverless

Serverless

Build applications comprised of microservices that run in response to events, auto-scale for you, and only charge you when they run. This lowers the total cost of maintaining your apps, enabling you to build more logic, faster. The Framework uses new event-driven compute services, like AWS Lambda, Google CloudFunctions, and more.

Google Cloud Functions

Google Cloud Functions

Construct applications from bite-sized business logic billed to the nearest 100 milliseconds, only while your code is running

Knative

Knative

Knative provides a set of middleware components that are essential to build modern, source-centric, and container-based applications that can run anywhere: on premises, in the cloud, or even in a third-party data center

OpenFaaS

OpenFaaS

Serverless Functions Made Simple for Docker and Kubernetes

Nuclio

Nuclio

nuclio is portable across IoT devices, laptops, on-premises datacenters and cloud deployments, eliminating cloud lock-ins and enabling hybrid solutions.

Apache OpenWhisk

Apache OpenWhisk

OpenWhisk is an open source serverless platform. It is enterprise grade and accessible to all developers thanks to its superior programming model and tooling. It powers IBM Cloud Functions, Adobe I/O Runtime, Naver, Nimbella among others.

Cloud Functions for Firebase

Cloud Functions for Firebase

Cloud Functions for Firebase lets you create functions that are triggered by Firebase products, such as changes to data in the Realtime Database, uploads to Cloud Storage, new user sign ups via Authentication, and conversion events in Analytics.

AWS Batch

AWS Batch

It enables developers, scientists, and engineers to easily and efficiently run hundreds of thousands of batch computing jobs on AWS. It dynamically provisions the optimal quantity and type of compute resources (e.g., CPU or memory optimized instances) based on the volume and specific resource requirements of the batch jobs submitted.

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