Serverless vs Containers: Deployment Architecture Compared

Serverless computing, exemplified by AWS Lambda, Cloudflare Workers, and Vercel Functions, abstracts away all infrastructure management. You deploy individual functions or small applications, and the platform handles provisioning, scaling, and availability. You pay only for actual execution time, which means zero cost when there is no traffic. Serverless functions scale automatically from zero to thousands of concurrent instances, making them ideal for variable workloads. Cloudflare Workers, which Adapter uses extensively, take serverless further by running at the edge (in 300+ data centers globally), providing sub-10ms cold starts and placing compute close to users.

Containers, orchestrated by Kubernetes (EKS, GKE, AKS) or simpler platforms like AWS ECS, Google Cloud Run, or Fly.io, package your application and its dependencies into a portable unit that runs consistently across environments. Containers provide full control over the runtime: any language, any framework, any system dependency, any port. Kubernetes adds sophisticated orchestration including auto-scaling, rolling deployments, service mesh networking, and self-healing. Cloud Run occupies an interesting middle ground, providing container-based deployment with serverless-like scaling (including scale-to-zero) and per-request billing.

Adapter's architecture practice uses both models extensively, often in the same project. We default to serverless (specifically Cloudflare Workers) for API endpoints, webhooks, cron jobs, and edge logic because the operational overhead is near zero and the cost at moderate scale is lower than running always-on containers. We use containers for workloads that require long-running processes, WebSocket connections, specific runtime dependencies (like Python ML libraries), or more than 128 MB of memory (a common serverless limit). The practical decision framework is straightforward: if your workload consists of short-lived, stateless request/response cycles with moderate memory requirements, serverless is almost always the right choice. If your workload needs persistent connections, heavy computation, large memory, or specific system-level dependencies, containers provide the necessary flexibility. The trend is toward serverless-like developer experiences on container platforms (Cloud Run being the best example), which suggests the line between these models will continue to blur.