When should I deploy a function to Cloud Run?

Cloud Run removes the work of managing servers, configuring software, updating frameworks, and patching operating systems. The software and infrastructure are fully managed by Google so that you just add code. Furthermore, provisioning of resources happens automatically in response to requests or events. This means that a Cloud Run service automatically scales out from a few invocations a day to many millions of invocations without any work from you. You can optionally use manual scaling if you need more control over your scaling behavior.

When you deploy a function, source code or a container image to Cloud Run, you receive all of the benefits described in the Container runtime contract.

Use cases for deploying functions

You can directly deploy a function bound to events in order to implement asynchronous workloads (such as lightweight ETL) or cloud automations (such as triggering application builds). In addition, the automatic provisioning of an HTTPS endpoint makes functions a perfect candidate for webhooks.

See the following table for additional common use cases for deploying a function to Cloud Run:

Use case Description
Streaming data processing / ETL Listen and respond to Cloud Storage events such as when a file is created, changed, or removed. Process images, perform video transcoding, validate and transform data, and invoke any service on the internet from Cloud Run.
Webhooks Using an HTTP trigger, respond to events originating from 3rd party systems like GitHub, Slack, Stripe, or from anywhere that can send HTTP requests.
Mobile backend Use Google's mobile platform for app developers, Firebase, and write your mobile backend in Cloud Run functions. Listen and respond to events from Firebase Analytics, Realtime Database, Authentication, and Storage.
IoT Imagine tens or hundreds of thousands of devices streaming data into Pub/Sub, thereby launching Cloud Run functions to process, transform and store data. Cloud Run lets you do it in a way that's completely serverless.
AI/ML Create a scalable image processing service with the Cloud Vision API, or post process output data from a Vertex AI custom-trained model.

Connect and extend cloud services

Cloud Run functions provides a connective layer of logic that lets you write code to connect and extend cloud services. Listen and respond to a file upload to Cloud Storage, a log change, or an incoming message on a Pub/Sub topic. Cloud Run functions augments existing cloud services and lets you address an increasing number of use cases with arbitrary programming logic. Cloud Run functions have access to the Google Service Account credential and are thus seamlessly authenticated with the majority of Google Cloud services, including Cloud Vision, as well as many others. In addition, Cloud Run functions are supported by numerous Cloud Client Libraries, which further simplify these integrations.

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