Cloudflare Containers let a Workers app call on-demand serverless containers for workloads that need a full filesystem, a specific runtime, existing container images, or more CPU, memory, and disk. They do not replace Workers; Workers still handle entry, routing, and platform bindings while containers run the heavy runtime-specific work.
The full deployment path for a Python agent, from local uv run to Docker to a public API on Cloudflare Containers: the Worker enforces authentication, the Container runs FastAPI, secrets never enter the image, and the service sleeps automatically after 10 idle minutes—the right way for a side project to save money.
Cloudflare Sandboxes uses a Worker as the entry point, a named Durable Object as the control plane, and a Container inside an isolated VM as the execution plane. It fits Cloudflare-native fleets of ephemeral Linux workspaces, but persistence, security boundaries, and three layers of billing remain your responsibility.
Agent execution must constrain kernels, filesystems, processes, networks, credentials, and tool authorization; sandbox escape is only one path, and an overpowered API token is often more direct.
App Runner packages build, deployment, TLS, load balancing, and autoscaling as a web service, trading orchestration control for a simpler platform with explicit VPC, instance, and health boundaries.
Fargate removes container-host operations, but task definitions, ECS services, VPCs, IAM, scaling, deployment, and observability remain your system.
Azure Container Apps hides Kubernetes while exposing HTTP/TCP ingress, revisions, KEDA scaling, jobs, and Dapr; replica concurrency, event idempotency, VNet design, and identity remain yours.
Fly.io places fast-starting Machines in chosen regions and connects them through Fly Proxy and private 6PN; cross-region state consistency remains your hard problem.
Cloud Run is more than an HTTP container platform: choose request-serving, run-to-completion, or always-on pull work first, then design concurrency, identity, and scaling.
GKE manages the Kubernetes control plane and Autopilot manages most node infrastructure, while workloads, policy, networking, upgrade compatibility, and cost governance remain yours.
Kubernetes is fundamentally API objects, controllers, and reconciliation loops—not YAML; it manages workload lifecycles without solving application state, data consistency, or organizational governance.