What Docker Containers Actually Are
Docker is a containerisation platform that packages your application code, runtime, dependencies, and configuration into a portable image that runs identically across development, staging, and production. When you deploy a Docker container, it runs as a persistent process on a server. The server can be a VPS, a managed container platform like Railway or Fly.io, or a Kubernetes cluster on AWS EKS or Google GKE. The container stays running between requests, so there is no cold start delay. State can be maintained in memory between requests (though this should be used carefully). Long-running processes like background job workers, AI model inference servers, WebSocket connections, and scheduled tasks are all natural fits for containers. Docker's portability means your development environment matches production closely, reducing the classic works on my machine problem. Container-based deployment gives you control over the runtime environment: you choose your Python version, install system-level dependencies, and configure exactly what is present in the image.
What Serverless Functions Actually Are
Serverless functions (also called functions-as-a-service or FaaS) run your code in ephemeral execution environments that spin up on demand and shut down when idle. AWS Lambda, Vercel Functions, Netlify Functions, and Cloudflare Workers are the major platforms. You deploy individual functions rather than a persistent application, and the platform handles scaling, infrastructure, and availability automatically. The appeal for early-stage products is the operational simplicity and cost model: you pay only for actual invocations, with no cost when there is no traffic. For an MVP with variable traffic (spikes around demos, marketing campaigns, or press coverage), the automatic scaling without pre-provisioned infrastructure is genuinely valuable. Vercel Functions, in particular, integrate tightly with Next.js and make deploying API routes simple. The limitations are execution time limits (typically 5-30 seconds on standard plans), no persistent in-memory state, cold start latency (50-500ms depending on the platform and runtime), and no access to GPU resources.
Cold Starts and Latency Implications
Cold starts are the primary user-experience concern with serverless for AI products. When a serverless function has not been invoked recently, the platform must spin up a new execution environment before processing the request. This adds 50-500ms of latency for the first request, and for AI applications where users are already waiting on LLM inference (which can take 1-5 seconds), adding a cold start on top is noticeable. Vercel has improved cold start performance significantly, and their Pro plan includes options to keep functions warm. Cloudflare Workers have near-zero cold starts due to their V8 isolate architecture, though this limits the runtime environment. Docker containers have no cold start concept because the process is always running. For AI APIs where every millisecond of latency matters, or for applications using streaming LLM responses where the first token time is critical to user experience, containers' consistent latency profile is a meaningful advantage. For background processing or low-frequency API calls where occasional cold start latency is acceptable, serverless is fine.
AI and ML Workload Suitability
Most serverless platforms are unsuitable for self-hosted AI model inference. Running a local LLM (Llama, Mistral, or any PyTorch model) requires GPU access, significant memory, and long inference times that exceed serverless execution limits. If your AI MVP uses API-based LLMs (OpenAI, Anthropic, Cohere), the inference happens on the provider's servers and your code simply makes HTTP requests, which work fine in serverless. If you need to run any AI model locally (for cost, privacy, or latency reasons), you need a container environment with access to the appropriate hardware. Docker containers on GPU-enabled instances (AWS EC2 G4, Fly.io GPU machines) are the right deployment target for self-hosted models. Additionally, AI workloads often involve preprocessing pipelines, vector index management, and background jobs (e.g., document ingestion for RAG systems) that run for minutes rather than seconds. These are natural container workloads and hard or impossible to implement in standard serverless functions.
Cost at MVP Stage and Early Traction
At MVP stage with low traffic, serverless is almost always cheaper. Vercel's free tier provides substantial function invocation allowances. AWS Lambda's free tier includes 1 million invocations per month. If your product has 50 users in the first month, serverless infrastructure cost is effectively zero. A Docker container on Railway, Fly.io, or a VPS runs 24/7 and incurs a fixed monthly cost regardless of traffic. Railway's starter plan starts at around GBP 5 per month for small services, which is not expensive but is more than zero. As traffic grows, the calculation shifts. At high request volumes, the per-invocation cost of serverless can exceed the fixed cost of a container. The break-even point varies by workload, but for many SaaS products it occurs somewhere in the hundreds of thousands of monthly requests. For a typical early-stage product, serverless is more cost-efficient at MVP stage, and this advantage is real even if the absolute numbers are small.
Operational Complexity and Maintenance
Serverless wins on operational simplicity for standard web API workloads. There are no servers to patch, no container registries to manage, no health checks to configure, and no autoscaling policies to tune. You push code and it deploys. Vercel in particular has reduced the deployment experience to near-zero friction for Next.js applications. Docker container deployments require more operational knowledge: building and pushing images, configuring deployment platforms, setting up health checks, managing environment variables, and handling rollback procedures. Platforms like Railway abstract much of this, but the conceptual model is more complex. For a team without DevOps experience, the simplicity of serverless deployment is a genuine capability enabler. For teams with infrastructure experience, Docker's control and portability are worth the setup cost. SpeedMVPs defaults to serverless (Vercel) for standard Next.js API routes and uses Docker containers (Railway or Fly.io) when the workload requires it.
When Docker Containers Are the Right Choice
Docker containers are the right choice for MVP deployments in several specific situations. If your application has long-running background jobs (document processing, batch embeddings, scheduled tasks), these need a persistent process that serverless cannot reliably host. If you are running a self-hosted AI model or need GPU access, containers are the only option. If your application uses WebSockets for real-time communication (chat, live updates), serverless execution models make persistent connections difficult. If you have Python-heavy backend code with complex system dependencies (CUDA libraries, OS-level packages), containers give you full control over the environment that serverless does not. If you are in a regulated industry where your infrastructure must run in a specific region under your control for GDPR or NHS Digital data governance requirements, containers on a designated cloud region give you that control.
Verdict
For most Next.js-based AI MVPs that use API-based LLMs, serverless is the right default. The zero-ops model, free tier cost, and tight Vercel integration make it the fastest path to a running product. For AI MVPs that include self-hosted model inference, background job processing, WebSocket requirements, or workloads with strict execution time needs, Docker containers are necessary. Many production AI products run a hybrid approach: serverless for the web frontend and API routes that call external LLMs, containers for background workers, model inference endpoints, and data pipeline processing. Choosing the infrastructure should follow from the workload requirements, not the other way around. At SpeedMVPs, we architect this hybrid deployment as a standard pattern for AI products that need both fast API response and background processing capability.