Your team is migrating a monolithic application to GCP. Some services are stateless HTTP APIs, while others need persistent connections and GPU access. How do you decide between GKE and Cloud Run, and what are the cost and operational trade-offs in production?

Cloud Run vs GKE: Decision Framework

Cloud Run is a fully managed serverless container platform that excels for stateless HTTP and gRPC services. It automatically scales from zero to thousands of instances, charges only for request processing time, and requires zero cluster management. In production, this means your team spends no time on node upgrades, autoscaler tuning, or cluster security patching. Cloud Run supports concurrency up to 1000 requests per instance, custom domains, VPC connectors for private networking, and Cloud SQL/Memorystore integrations.

When GKE Becomes Necessary

GKE is the right choice when workloads require persistent WebSocket connections, GPU/TPU scheduling, DaemonSets for node-level agents (like Datadog or Falco), custom CNI plugins, or StatefulSets for databases. GKE Autopilot reduces operational burden by managing node provisioning, but Standard mode gives full control over machine types, taints, and node pools. Production clusters typically need at least 3 nodes across 3 zones for HA, which means a baseline cost even at zero traffic.

Cost Analysis in Production

Cloud Run pricing is per-request (CPU-seconds + memory-seconds + requests), making it extremely cost-effective for bursty or low-traffic services. A service handling 1M requests/month at 200ms average latency might cost $5-15/month. The same workload on GKE requires at minimum 3 e2-medium nodes (~$75/month) plus management fee ($74.40/month for Standard). However, at sustained high throughput (>50 RPS continuously), GKE with committed use discounts can be 40-60% cheaper than Cloud Run.

Hybrid Architecture Pattern

Most production deployments use both. Stateless API gateways, webhooks, and async processors run on Cloud Run. Core platform services requiring service mesh, complex health checks, or GPU inference run on GKE. Both can sit behind the same Global HTTP(S) Load Balancer using serverless NEGs for Cloud Run and container-native NEGs for GKE, enabling gradual migration.

Operational Maturity Considerations

Cloud Run requires a team comfortable with 12-factor app design and stateless architecture. GKE requires Kubernetes expertise for RBAC, network policies, pod security standards, and upgrade strategies. In FAANG-scale environments, platform teams typically manage GKE clusters and expose Cloud Run as a self-service deployment target for product teams.