Editorial Team AI MLOps, AWS, DevOps, Docker, Kubernetes

How to Deploy an LLM App on AWS Using Docker and Kubernetes

A practical deployment guide for LLM applications on AWS using Docker, Kubernetes, secrets, autoscaling, observability, and cost controls.

Updated September 2026.

An LLM app is still an app. It needs a container, a deployment target, secrets, routing, observability, rollback, security reviews, and cost controls. The model call is only one part of production.

AWS, Docker, and Kubernetes are a common foundation because they give teams a repeatable way to ship AI features without treating every launch like a one-off experiment.

Quick answer: To deploy an LLM app on AWS, package the service in Docker, run it on Amazon EKS or another managed compute layer, store API keys in a secrets manager, add autoscaling and rate limits, trace every model call, and monitor cost, latency, errors, and safety events before opening traffic widely.

Start with a boring service boundary

Separate the user-facing app from the LLM orchestration layer when the product is growing. The orchestration service can handle prompts, retrieval, model routing, logging, and policy checks. That makes it easier to test, scale, and secure AI behavior independently.

web app -> API gateway -> LLM service -> model provider
                              -> retrieval service
                              -> audit log
                              -> metrics and traces

Build a clean Docker image

Use small base images, lock dependencies, run as a non-root user, and keep secrets out of the image. Docker’s getting started documentation is still the right conceptual foundation: the image should be portable, predictable, and easy to promote between environments.

  • Pin runtime versions.
  • Run vulnerability scans in CI.
  • Use environment variables or mounted secrets at runtime.
  • Expose a health endpoint.
  • Log structured JSON rather than plain strings.

Use Kubernetes for reliability, not ceremony

For many teams, Amazon EKS is a practical Kubernetes path because AWS manages the control plane. Keep the first deployment simple: Deployment, Service, Ingress, ConfigMap, Secret, HorizontalPodAutoscaler, and observability sidecars or agents where needed.

  • Set CPU and memory requests.
  • Use readiness and liveness probes.
  • Add pod disruption budgets for critical services.
  • Separate staging and production namespaces.
  • Use managed identity patterns rather than static cloud credentials.

Treat model calls as production dependencies

Model APIs can be slow, expensive, rate-limited, or temporarily unavailable. Add retries with backoff, timeouts, model fallback where appropriate, request queues for long work, and graceful user messages when the system cannot complete a task.

  • Timeout per model call
  • Max tokens by route
  • Streaming response support
  • Fallback or degraded mode
  • Budget alerts by team or tenant
  • Abuse and prompt-injection controls

Ship with observability from day one

An LLM deployment without traces is hard to debug. Instrument request flow, retrieval, model calls, tool calls, and cost. CodeRise’s DevOps and CI/CD services help teams turn this into repeatable release infrastructure instead of manual deployment work.

FAQ

Do I need Kubernetes for every LLM app?

No. Smaller apps can run well on managed containers or serverless platforms. Kubernetes makes sense when you need standardized deployments, scaling, networking, policies, and operational consistency across services.

Where should OpenAI or model provider keys live?

Store them in a managed secrets system and inject them at runtime. Do not bake keys into Docker images, repositories, browser code, or logs.

What should I monitor first?

Start with latency, errors, token usage, cost, model provider failures, retrieval misses, and user-visible failures. Then add eval and safety metrics as workflows mature.

Helpful references

Need help turning this into a production system? CodeRise helps teams design, build, secure, and operate cloud-native AI products. Start with our cloud, DevOps, and AI services or talk to us about platform engineering support.