Google Cloud Functions vs Google Cloud Run

Overview

Google Cloud Functions

Stacks478

Followers479

Votes25

Google Cloud Run

Stacks290

Followers243

Votes62

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

Introduction: This Markdown code provides a comparison between Google Cloud Functions and Google Cloud Run, highlighting key differences between the two services.

Serverless vs. Containerization: Google Cloud Functions is a serverless computing service, which means developers only need to write and deploy code without worrying about managing infrastructure. On the other hand, Google Cloud Run allows developers to deploy containerized applications, giving more control over the runtime environment and the ability to package applications with their dependencies.
Request-Driven vs. Event-Driven: Google Cloud Functions is primarily designed for event-driven scenarios. It reacts to events, triggered by various Google Cloud services, by executing the associated code. In contrast, Google Cloud Run is request-driven. It responds to incoming HTTP requests and executes the corresponding containerized application.
Scaling Model: Google Cloud Functions automatically scales to handle incoming events and can scale to zero when there is no traffic. It allows developers to focus solely on the code logic without worrying about managing infrastructure scaling. Google Cloud Run also auto-scales based on incoming HTTP requests, but it requires the developer to provision and manage the underlying infrastructure for the containerized application.
Billing Structure: Google Cloud Functions is billed on the number of function invocations, execution time, and memory usage. It offers a free tier for a certain level of usage. On the other hand, Google Cloud Run is billed based on the number of requests and the amount of CPU and memory resources used by the container instances. It does not have a free tier and requires the developer to pay for the resources used.
Execution Environment: Google Cloud Functions supports several programming languages, such as Node.js, Python, and Go. It provides a convenient development and deployment experience, abstracting away the underlying infrastructure. Google Cloud Run, on the other hand, supports any language or runtime that can be packaged into a container.
Flexibility and Portability: Google Cloud Functions is tightly integrated with the Google Cloud ecosystem, making it easy to trigger functions based on various events from Google Cloud services. Google Cloud Run, being based on containerization technology, offers more flexibility and allows developers to deploy applications that are not tightly coupled with the Google Cloud ecosystem. It offers portability to other cloud platforms that support containerization, such as Kubernetes.

In Summary, Google Cloud Functions is a serverless computing service optimized for event-driven scenarios with automatic scaling, while Google Cloud Run allows developers to deploy containerized applications with more control over the runtime environment and flexibility across different cloud platforms.

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CLI (Node.js)

Manual

Advice on Google Cloud 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

Google Cloud Functions	Google Cloud Run
Construct applications from bite-sized business logic billed to the nearest 100 milliseconds, only while your code is running	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.
-	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 478	Stacks 290
Followers 479	Followers 243
Votes 25	Votes 62
Pros & Cons
Pros 7 Serverless Applications 5 Its not AWS 4 Simplicity 3 Free Tiers and Trainging 2 Simple config with GitLab CI/CD Cons 1 Node.js only 0 Typescript Support 0 Blaze, pay as you go	Pros 11 HTTPS endpoints 10 Pay per use 10 Fully managed 7 Deploy containers 7 Serverless
Integrations
Firebase Google Cloud Storage Stackdriver	Google Kubernetes Engine Google Cloud Build Docker Knative

What are some alternatives to Google Cloud 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.

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

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.

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

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

Introduction: This Markdown code provides a comparison between Google Cloud Functions and Google Cloud Run, highlighting key differences between the two services.

Serverless vs. Containerization: Google Cloud Functions is a serverless computing service, which means developers only need to write and deploy code without worrying about managing infrastructure. On the other hand, Google Cloud Run allows developers to deploy containerized applications, giving more control over the runtime environment and the ability to package applications with their dependencies.
Request-Driven vs. Event-Driven: Google Cloud Functions is primarily designed for event-driven scenarios. It reacts to events, triggered by various Google Cloud services, by executing the associated code. In contrast, Google Cloud Run is request-driven. It responds to incoming HTTP requests and executes the corresponding containerized application.
Scaling Model: Google Cloud Functions automatically scales to handle incoming events and can scale to zero when there is no traffic. It allows developers to focus solely on the code logic without worrying about managing infrastructure scaling. Google Cloud Run also auto-scales based on incoming HTTP requests, but it requires the developer to provision and manage the underlying infrastructure for the containerized application.
Billing Structure: Google Cloud Functions is billed on the number of function invocations, execution time, and memory usage. It offers a free tier for a certain level of usage. On the other hand, Google Cloud Run is billed based on the number of requests and the amount of CPU and memory resources used by the container instances. It does not have a free tier and requires the developer to pay for the resources used.
Execution Environment: Google Cloud Functions supports several programming languages, such as Node.js, Python, and Go. It provides a convenient development and deployment experience, abstracting away the underlying infrastructure. Google Cloud Run, on the other hand, supports any language or runtime that can be packaged into a container.
Flexibility and Portability: Google Cloud Functions is tightly integrated with the Google Cloud ecosystem, making it easy to trigger functions based on various events from Google Cloud services. Google Cloud Run, being based on containerization technology, offers more flexibility and allows developers to deploy applications that are not tightly coupled with the Google Cloud ecosystem. It offers portability to other cloud platforms that support containerization, such as Kubernetes.

Google Cloud Functions vs Google Cloud Run

Overview