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AWS Lambda vs Google App Engine: What are the differences?

Introduction

In this article, we will explore the key differences between AWS Lambda and Google App Engine. Both AWS Lambda and Google App Engine are popular cloud computing platforms that allow developers to build and deploy applications without having to worry about managing the underlying infrastructure. However, there are several differences between the two platforms that are important to consider when choosing the right platform for your application.

  1. Pricing Model: AWS Lambda pricing is based on the number of requests and the amount of compute time consumed by those requests, while Google App Engine pricing is based on the number of instances and the amount of network traffic used by the application. This means that the pricing structure of these two platforms can vary significantly depending on the nature of the application and its usage patterns.

  2. Programming Languages: AWS Lambda supports a wide range of programming languages including JavaScript (Node.js), Python, Java, C#, and more. On the other hand, Google App Engine primarily supports programming languages like Python, Java, PHP, and Go.

  3. Scaling: AWS Lambda automatically scales your application based on the incoming request volume, without requiring any configuration from the developer. Google App Engine also provides automatic scaling, but it allows developers to configure the scaling behavior based on parameters such as CPU usage, network traffic, and request latency.

  4. Integration with Other Services: AWS Lambda can easily integrate with other AWS services such as Amazon S3, Amazon DynamoDB, and Amazon API Gateway. Google App Engine provides seamless integration with other Google Cloud Platform services like Google Cloud Storage, Google Cloud Datastore, and Google Cloud Pub/Sub.

  5. Deployment Options: AWS Lambda allows developers to deploy their functions independently without worrying about the underlying platform. Google App Engine also offers similar flexibility in deploying applications, but it provides additional options for managing versions, traffic splitting, and rollbacks.

  6. Community Support: AWS Lambda has a larger community of developers and a more extensive ecosystem of third-party tools and libraries. Google App Engine also has a strong community, but it may not be as vast as the AWS Lambda community. The availability of community support and resources can greatly impact development speed and troubleshooting capabilities.

In Summary, AWS Lambda and Google App Engine differ in their pricing models, supported programming languages, scaling behavior, integration with other services, deployment options, and community support. These differences should be carefully considered when choosing the right platform for your application.

Decisions about AWS Lambda and Google App Engine

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

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Pros of AWS Lambda
Pros of Google App Engine
  • 129
    No infrastructure
  • 83
    Cheap
  • 70
    Quick
  • 59
    Stateless
  • 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
  • 145
    Easy to deploy
  • 106
    Auto scaling
  • 80
    Good free plan
  • 62
    Easy management
  • 56
    Scalability
  • 35
    Low cost
  • 32
    Comprehensive set of features
  • 28
    All services in one place
  • 22
    Simple scaling
  • 19
    Quick and reliable cloud servers
  • 6
    Granular Billing
  • 5
    Easy to develop and unit test
  • 5
    Monitoring gives comprehensive set of key indicators
  • 3
    Really easy to quickly bring up a full stack
  • 3
    Create APIs quickly with cloud endpoints
  • 2
    No Ops
  • 2
    Mostly up

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Cons of AWS Lambda
Cons of Google App Engine
  • 7
    Cant execute ruby or go
  • 3
    Compute time limited
  • 1
    Can't execute PHP w/o significant effort
    Be the first to leave a con

    Sign up to add or upvote consMake informed product decisions

    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 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.

    Need advice about which tool to choose?Ask the StackShare community!

    What companies use AWS Lambda?
    What companies use Google App Engine?
    Manage your open source components, licenses, and vulnerabilities
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    Sign up to get full access to all the companiesMake informed product decisions

    What tools integrate with AWS Lambda?
    What tools integrate with Google App Engine?

    Sign up to get full access to all the tool integrationsMake informed product decisions

    Blog Posts

    GitHubPythonNode.js+47
    55
    72781
    GitHubDockerAmazon EC2+23
    12
    6611
    JavaScriptGitHubPython+42
    53
    22159
    What are some alternatives to AWS Lambda and Google App Engine?
    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.
    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.
    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.
    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