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  1. Stackups
  2. Application & Data
  3. Serverless
  4. Serverless Task Processing
  5. AWS Lambda vs Google Cloud Run

AWS Lambda vs Google Cloud Run

OverviewDecisionsComparisonAlternatives

Overview

AWS Lambda
AWS Lambda
Stacks26.0K
Followers18.8K
Votes432
Google Cloud Run
Google Cloud Run
Stacks290
Followers243
Votes62

AWS Lambda vs Google Cloud Run: What are the differences?

Introduction

AWS Lambda and Google Cloud Run are both serverless computing platforms that allow developers to build and deploy applications without managing infrastructure. However, there are several key differences between the two platforms that developers should be aware of before choosing one for their projects.

  1. Performance: AWS Lambda is known for its fast start times and low latency, making it a great choice for applications that require quick response times. On the other hand, Google Cloud Run containers have a longer startup time, but once running, they can handle a high number of concurrent requests, making them more suitable for applications that require high scalability.

  2. Pricing Model: AWS Lambda follows a pay-as-you-go model, where you are billed based on the number of requests and the duration of execution. On the other hand, Google Cloud Run offers a differentiated pricing model, where you are billed based on the CPU allocation and memory usage, allowing you to optimize costs based on your specific application requirements.

  3. Environment Flexibility: AWS Lambda supports a wide range of programming languages, including JavaScript, Python, Java, and C#, providing developers with more flexibility in their choice of language. Google Cloud Run, on the other hand, currently only supports containers, which means you can use any language that can be packaged into a container image.

  4. Integration with Ecosystem: AWS Lambda has a rich ecosystem of services and integrations, including the AWS Serverless Application Model (SAM), AWS Step Functions, and AWS API Gateway, making it easier to build and deploy complex serverless applications. Google Cloud Run integrates well with other Google Cloud services, such as Cloud Build for CI/CD, Cloud Pub/Sub for messaging, and Cloud Logging for monitoring.

  5. Automatic Scaling: Both AWS Lambda and Google Cloud Run offer automatic scaling based on request traffic, but they have different scaling mechanisms. AWS Lambda uses a combination of requests per second and concurrent executions to scale, while Google Cloud Run scales based on the number of concurrent requests.

  6. Deployment Options: AWS Lambda provides a native deployment mechanism through its console, CLI, or AWS CloudFormation, making it easy to manage and deploy functions. Google Cloud Run can be deployed using either the Google Cloud Console or the gcloud command-line tool, and it also integrates well with other deployment tools like Cloud Build.

Summary

In summary, AWS Lambda and Google Cloud Run have key differences in terms of performance, pricing model, environment flexibility, integration with ecosystem, scaling mechanisms, and deployment options. Developers should consider these differences when choosing a serverless platform for their applications.

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Advice on AWS Lambda, Google Cloud Run

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
Clifford
Clifford

Software Engineer at Bidvest Advisory Services

Mar 28, 2020

Decided

Run cloud service containers instead of cloud-native services

  • Running containers means that your microservices are not "cooked" into a cloud provider's architecture.
  • Moving from one cloud to the next means that you simply spin up new instances of your containers in the new cloud using that cloud's container service.
  • Start redirecting your traffic to the new resources.
  • Turn off the containers in the cloud you migrated from.
71.3k views71.3k
Comments

Detailed Comparison

AWS Lambda
AWS Lambda
Google Cloud Run
Google Cloud Run

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.

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.

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
Simple developer experience; Fast autoscaling; Managed; Any language, any library, any binary; Leverage container workflows and standards; Redundancy; Integrated logging and monitoring; Built on Knative; Custom domains
Statistics
Stacks
26.0K
Stacks
290
Followers
18.8K
Followers
243
Votes
432
Votes
62
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
  • 11
    HTTPS endpoints
  • 10
    Fully managed
  • 10
    Pay per use
  • 7
    Deploy containers
  • 7
    Concurrency: multiple requests sent to each container
Integrations
No integrations available
Google Kubernetes Engine
Google Kubernetes Engine
Google Cloud Build
Google Cloud Build
Docker
Docker
Knative
Knative

What are some alternatives to AWS Lambda, Google Cloud Run?

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.

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.

Fission

Fission

Write short-lived functions in any language, and map them to HTTP requests (or other event triggers). Deploy functions instantly with one command. There are no containers to build, and no Docker registries to manage.

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