Azure Functions vs Google Cloud Run

Overview

Azure Functions

Stacks785

Followers705

Votes62

Google Cloud Run

Stacks292

Followers243

Votes62

Azure Functions vs Google Cloud Run: What are the differences?

Introduction: Azure Functions and Google Cloud Run are two popular serverless compute platforms that allow developers to run and scale their applications without the need to manage infrastructure. While both platforms offer similar benefits, they have distinct differences that developers should consider when choosing between them.

Pricing model: Azure Functions follows a consumption-based pricing model where users pay for the actual execution time and resources used. On the other hand, Google Cloud Run uses a pay-as-you-go pricing model based on the number of requests and the resources consumed. Azure Functions' model is more granular and can be more cost-effective for applications with sporadic or unpredictable workloads. Google Cloud Run's model is ideal for applications with a steady or predictable traffic pattern.
Platform support: Azure Functions is part of the larger Azure ecosystem and tightly integrates with other Azure services such as Azure Logic Apps, Azure Event Grid, and Azure Storage. It also supports multiple programming languages, including C#, JavaScript, Java, Python, and PowerShell. Google Cloud Run, on the other hand, supports any language that can be containerized using Docker. This flexibility allows developers to use the language and tools of their choice, regardless of the underlying cloud platform.
Containerization: Azure Functions supports running code in a serverless environment without the need for containerization. It automatically manages the underlying infrastructure and allows developers to focus solely on writing code. Google Cloud Run, however, requires applications to be containerized using Docker. This provides developers with greater control over the runtime environment and enables them to leverage the rich ecosystem of Docker containers.
Scale and concurrency: Azure Functions automatically scales based on demand, allowing applications to handle high loads without manual intervention. It also provides concurrent execution and can process multiple requests simultaneously. Google Cloud Run also scales automatically, but it has a maximum limit on the number of concurrent requests that can be processed. This limit is configurable but may affect the performance of highly concurrent workloads.
Integration with CI/CD pipelines: Azure Functions integrates seamlessly with Azure DevOps and other popular CI/CD tools. This enables developers to easily deploy, test, and manage their functions as part of their continuous integration and deployment pipelines. Google Cloud Run integrates with Google Cloud Build, making it well-suited for organizations already using Google Cloud Platform in their development workflows.
Platform maturity and ecosystem: Azure Functions has been on the market for a longer period and has a well-established ecosystem with extensive documentation, community support, and third-party integrations. Google Cloud Run, although relatively new, benefits from the vast ecosystem of Google Cloud Platform services and the expertise of Google in managing distributed systems.

In summary, Azure Functions and Google Cloud Run differ in their pricing models, platform support, containerization requirements, scale and concurrency capabilities, integration with CI/CD pipelines, and platform maturity. Choosing between the two platforms depends on the specific requirements and preferences of the development team or organization.

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CLI (Node.js)

Manual

Advice on Azure Functions, Google Cloud Run

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.

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Detailed Comparison

Azure Functions	Google Cloud Run
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.	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.
Easily schedule event-driven tasks across services;Expose Functions as HTTP API endpoints;Scale Functions based on customer demand;Develop how you want, using a browser-based UI or existing tools;Get continuous deployment, remote debugging, and authentication out of the box	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 785	Stacks 292
Followers 705	Followers 243
Votes 62	Votes 62
Pros & Cons
Pros 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 Cons 1 Poor support for Linux environments 1 Sporadic server & language runtime issues 1 Not suited for long-running applications 1 No persistent (writable) file system available	Pros 11 HTTPS endpoints 10 Pay per use 10 Fully managed 7 Concurrency: multiple requests sent to each container 7 Serverless
Integrations
Azure DevOps Java Bitbucket Node.js Microsoft Azure GitHub Visual Studio Code JavaScript Azure Cosmos DB C#	Google Kubernetes Engine Google Cloud Build Docker Knative

What are some alternatives to Azure Functions, Google Cloud Run?

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.

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

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

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

Serverless Functions Made Simple for Docker and Kubernetes

Nuclio

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

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 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

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

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.

Related Comparisons

Azure Functions vs Google Cloud Run: What are the differences?

Pricing model: Azure Functions follows a consumption-based pricing model where users pay for the actual execution time and resources used. On the other hand, Google Cloud Run uses a pay-as-you-go pricing model based on the number of requests and the resources consumed. Azure Functions' model is more granular and can be more cost-effective for applications with sporadic or unpredictable workloads. Google Cloud Run's model is ideal for applications with a steady or predictable traffic pattern.
Platform support: Azure Functions is part of the larger Azure ecosystem and tightly integrates with other Azure services such as Azure Logic Apps, Azure Event Grid, and Azure Storage. It also supports multiple programming languages, including C#, JavaScript, Java, Python, and PowerShell. Google Cloud Run, on the other hand, supports any language that can be containerized using Docker. This flexibility allows developers to use the language and tools of their choice, regardless of the underlying cloud platform.
Containerization: Azure Functions supports running code in a serverless environment without the need for containerization. It automatically manages the underlying infrastructure and allows developers to focus solely on writing code. Google Cloud Run, however, requires applications to be containerized using Docker. This provides developers with greater control over the runtime environment and enables them to leverage the rich ecosystem of Docker containers.
Scale and concurrency: Azure Functions automatically scales based on demand, allowing applications to handle high loads without manual intervention. It also provides concurrent execution and can process multiple requests simultaneously. Google Cloud Run also scales automatically, but it has a maximum limit on the number of concurrent requests that can be processed. This limit is configurable but may affect the performance of highly concurrent workloads.
Integration with CI/CD pipelines: Azure Functions integrates seamlessly with Azure DevOps and other popular CI/CD tools. This enables developers to easily deploy, test, and manage their functions as part of their continuous integration and deployment pipelines. Google Cloud Run integrates with Google Cloud Build, making it well-suited for organizations already using Google Cloud Platform in their development workflows.
Platform maturity and ecosystem: Azure Functions has been on the market for a longer period and has a well-established ecosystem with extensive documentation, community support, and third-party integrations. Google Cloud Run, although relatively new, benefits from the vast ecosystem of Google Cloud Platform services and the expertise of Google in managing distributed systems.

Azure Functions vs Google Cloud Run

Overview