Containers & Compute Compared: Cloudflare Workers/Containers vs AWS ECS/EKS vs Azure AKS vs Google GKE

They solve different problems. Kubernetes orchestrates containers in a cluster — managing scheduling, scaling, networking, and lifecycle across nodes. Workers are individual request handlers that run in V8 isolates at edge locations. Workers have no concept of pods, services, ingress, or persistent volumes. For simple request/response workloads, Workers is dramatically simpler. For complex microservice architectures with persistent processes, service mesh, and stateful workloads, Kubernetes is necessary.

ECS (Elastic Container Service) is AWS's proprietary container orchestrator — simpler than Kubernetes, deeply integrated with AWS services, no control plane cost. EKS (Elastic Kubernetes Service) is managed Kubernetes on AWS — standard Kubernetes APIs, broader ecosystem compatibility, /bin/sh.10/hour control plane cost. Both can use Fargate (serverless) or EC2 (self-managed) for compute. Choose ECS for AWS-native simplicity; choose EKS for Kubernetes ecosystem compatibility and portability.

Cloud Run is Google's serverless container platform — you deploy a container image, and Cloud Run handles scaling (including to zero), load balancing, and SSL. It is more capable than Workers (full Linux, any language, up to 32GB memory, 60-minute timeout) but slower (container cold starts vs no cold starts) and regional (not global). Cloud Run is the best choice when you need container flexibility with serverless simplicity on GCP.

The control plane: EKS costs /bin/sh.10/hour (3/month), AKS is free (control plane), GKE Autopilot includes control plane in pod pricing, GKE Standard is free (one zonal cluster) or /bin/sh.10/hour for regional. Node compute is additional: EC2/Azure VMs/GCE instances at standard pricing. Fargate/serverless costs more per-vCPU but eliminates node management. A minimal production Kubernetes cluster typically costs 50-500/month before application workloads.

Use serverless containers (Fargate, Cloud Run, Azure Container Apps) when: you have simple scaling requirements, want zero infrastructure management, run HTTP-based workloads, and prefer per-request/per-second pricing. Use Kubernetes (EKS, AKS, GKE) when: you need service mesh, custom scheduling, stateful workloads (databases, message queues), GPU workloads, complex networking, or want maximum control and portability. Kubernetes is more powerful but requires significantly more operational expertise.

No. Azure Container Apps is a serverless container platform built on top of Kubernetes and KEDA (event-driven autoscaling). It abstracts away cluster management — you deploy containers without configuring nodes, networking, or Kubernetes resources. AKS is managed Kubernetes where you manage the cluster configuration, node pools, and Kubernetes resources. Container Apps is simpler but less flexible; AKS is more powerful but more complex. Container Apps is Microsoft's answer to Cloud Run and AWS Fargate on ECS.

For certain workloads, yes. If your application is a stateless HTTP API, a web proxy, an edge middleware layer, or a data aggregation service — and it fits within Workers' constraints (128MB memory, 30s execution, JS/TS/WASM) — Workers can replace containers with better latency and simpler operations. For microservices requiring full language runtimes, large memory, persistent processes, gRPC service mesh, or complex orchestration, Kubernetes is still necessary.

GKE Autopilot is Google's fully managed Kubernetes mode where Google manages nodes, scaling, and infrastructure. You only define pods and Kubernetes resources — GKE handles the rest. Pricing is per-pod (vCPU and memory) with no node management overhead. Autopilot is more expensive per-resource than Standard mode but eliminates node provisioning, patching, and right-sizing. It is the closest Kubernetes experience to 'serverless' while remaining fully Kubernetes-compatible.

Directly and significantly. A 50MB container image might have a 500ms cold start on Cloud Run; a 2GB image might take 10-15 seconds. Kubernetes mitigates this by keeping pods running (no cold starts for established services), but initial deployments and scaling events are slower with large images. Workers avoid this entirely — V8 isolates do not load container images. If cold starts matter, minimize image size (use Alpine/distroless bases) or use Workers for latency-sensitive paths.