Steps

Build and push your container image to Artifact Registry (preferred) or Container Registry; ensure the image listens on the PORT environment variable (Cloud Run injects this, default 8080)
Deploy with gcloud run deploy specifying --concurrency (requests per container instance, up to 1000), --min-instances to keep warm instances alive and eliminate cold starts for latency-sensitive services, and --max-instances to cap cost
Set --cpu and --memory according to your workload; note that CPU is throttled to near-zero between requests unless you pass --no-cpu-throttling (which keeps CPU allocated during idle — incurs cost)
Use --execution-environment=gen2 for workloads needing full Linux compatibility or higher networking performance; gen2 also reduces cold-start frequency
Configure startup and liveness probes via the service YAML (httpGet or tcpSocket) so Cloud Run can distinguish a slow start from a crashed container; set initialDelaySeconds to match your application boot time
Set environment variables and secrets via --set-env-vars or --set-secrets (binds Secret Manager secrets at deploy time); avoid baking credentials into the image

Known gotchas

With --min-instances 0 (the default), every scale-from-zero event incurs a cold start that includes container pull, runtime init, and application init — for JVM or Python apps this can be several seconds; use --min-instances 1 or more for latency-critical paths
CPU is only allocated during request processing by default; background goroutines, timers, or async tasks that run after the response is sent may be preempted or produce unpredictable behavior unless you enable --no-cpu-throttling
Container images larger than ~500 MiB significantly worsen cold-start times; use multi-stage builds and distroless or slim base images

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Deploy a containerized service to Google Cloud Run with tuned concurrency and minimized cold-start latency

Steps

Known gotchas

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