AWS Lambda vs Azure Functions vs Google Cloud Functions

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AWS Lambda

+ 1
Azure Functions

+ 1
Google Cloud Functions

+ 1
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Pros of AWS Lambda
Pros of Azure Functions
Pros of Google Cloud Functions
  • 129
    No infrastructure
  • 83
  • 70
  • 59
  • 47
    No deploy, no server, great sleep
  • 12
    AWS Lambda went down taking many sites with it
  • 6
    Event Driven Governance
  • 6
    Extensive API
  • 6
    Auto scale and cost effective
  • 6
    Easy to deploy
  • 5
    VPC Support
  • 3
    Integrated with various AWS services
  • 14
    Pay only when invoked
  • 11
    Great developer experience for C#
  • 9
    Multiple languages supported
  • 7
    Great debugging support
  • 5
    Can be used as lightweight https service
  • 4
    Easy scalability
  • 3
  • 3
  • 2
    Event driven
  • 2
    Azure component events for Storage, services etc
  • 2
    Poor developer experience for C#
  • 7
    Serverless Applications
  • 5
    Its not AWS
  • 4
  • 3
    Free Tiers and Trainging
  • 2
    Simple config with GitLab CI/CD
  • 1
    Built-in Webhook trigger
  • 1
    Typescript Support
  • 1
    Blaze, pay as you go
  • 1
    Customer Support

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Cons of AWS Lambda
Cons of Azure Functions
Cons of Google Cloud Functions
  • 7
    Cant execute ruby or go
  • 3
    Compute time limited
  • 1
    Can't execute PHP w/o significant effort
  • 1
    No persistent (writable) file system available
  • 1
    Poor support for Linux environments
  • 1
    Sporadic server & language runtime issues
  • 1
    Not suited for long-running applications
  • 1
    Node.js only
  • 0
    Typescript Support
  • 0
    Blaze, pay as you go

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What is AWS Lambda?

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.

What is 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.

What is Google Cloud Functions?

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

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What companies use AWS Lambda?
What companies use Azure Functions?
What companies use Google Cloud Functions?

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What tools integrate with AWS Lambda?
What tools integrate with Azure Functions?
What tools integrate with Google Cloud Functions?

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What are some alternatives to AWS Lambda, Azure Functions, and Google Cloud Functions?
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.
AWS Elastic Beanstalk
Once you upload your application, Elastic Beanstalk automatically handles the deployment details of capacity provisioning, load balancing, auto-scaling, and application health monitoring.
AWS Step Functions
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.
Google App Engine
Google has a reputation for highly reliable, high performance infrastructure. With App Engine you can take advantage of the 10 years of knowledge Google has in running massively scalable, performance driven systems. App Engine applications are easy to build, easy to maintain, and easy to scale as your traffic and data storage needs grow.
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.
See all alternatives