Cloud Functions Gen 2

Cloud Functions (2nd gen) is the latest version of Google Cloud Functions, built on top of Cloud Run and Eventarc. It offers significant improvements over the 1st generation, including higher performance, longer timeouts, concurrency support, and a unified eventing model. Google recommends using Gen 2 for all new function deployments.

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Architecture: Built on Cloud Run

The most significant architectural change in Gen 2 is that functions are deployed as Cloud Run services under the hood. When you deploy a Gen 2 function, Google Cloud automatically creates a Cloud Run service, builds a container image using Cloud Build, stores it in Artifact Registry, and deploys it to Cloud Run. This gives Gen 2 functions all the benefits of Cloud Run's container-based infrastructure.

What This Means in Practice

Feature	Gen 1	Gen 2
Max timeout	540 seconds (9 min)	60 minutes (event-driven), 60 minutes (HTTP)
Max memory	8 GiB	32 GiB
Max vCPUs	2	8
Concurrency	1 request per instance	Up to 1,000 concurrent requests per instance
Min instances	0	0 (configurable minimum)
Traffic splitting	Not supported	Supported (revision-based)
Eventarc integration	Limited	Full support (130+ event sources)

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Concurrency

One of the most impactful improvements in Gen 2 is concurrency support. In Gen 1, each function instance processes exactly one request at a time. If 100 requests arrive simultaneously, Gen 1 spins up 100 instances. In Gen 2, a single instance can handle up to 1,000 concurrent requests (configurable).

Benefits of Concurrency

• Reduced cold starts: Fewer instances need to be started because each instance handles multiple requests
• Lower cost: Fewer instances running means less compute time billed
• Better resource utilisation: A function waiting on I/O (database queries, API calls) can process other requests on the same instance

# Deploy with concurrency set to 80
gcloud functions deploy myFunction \
  --gen2 \
  --runtime python312 \
  --trigger-http \
  --concurrency 80 \
  --region europe-west2

Concurrency Considerations

When using concurrency, your function code must be thread-safe. Shared in-memory state (global variables, caches) must be protected with appropriate synchronisation. Each concurrent request shares the same memory allocation, so you may need to increase memory to support more concurrent requests.

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Traffic Splitting