Run AI applications in production
AI Operations
Operating AI software means managing normal application infrastructure plus uncertain model behavior, variable latency, external provider limits, expensive compute, and autonomous workflows. This guide connects container operations and reliability engineering with the signals unique to AI systems.
Decisions this guide helps you make
- When containers are sufficient and when orchestration is justified.
- How model size, concurrency, and GPUs change capacity planning.
- Which traces, evaluations, costs, and quality signals belong beside uptime metrics.
- How to release and roll back model, prompt, and agent changes safely.
Package and schedule workloads
Use the smallest operational layer that meets the workload. A container may be enough; orchestration should solve a demonstrated scheduling or reliability problem.
Docker vs Kubernetes for AI applications
Choose a production model based on workload, team, GPUs, and scaling needs.
Read guide →Containers for AI applications
Package APIs, model runtimes, dependencies, and local environments reproducibly.
Read guide →Ollama with Docker
Package a local model runtime with repeatable configuration.
Read guide →Deploy AI agents in production
Operate workers, queues, tools, state, and controlled retries.
Read guide →Diagnose resource failures
Model loading and inference make memory, storage, and scheduling failures more visible and more expensive.
Kubernetes OOMKilled for AI workloads
Diagnose container memory, model loading, and pod-limit failures.
Read guide →Pending AI and GPU pods
Resolve accelerator capacity, scheduling, quota, affinity, and storage constraints.
Read guide →Pod evictions in AI systems
Protect inference and agent services from node memory and disk pressure.
Read guide →AI Kubernetes troubleshooting
Work through scheduling, image, memory, networking, and GPU symptoms.
Read guide → FoundationAI memory troubleshooting
Separate RAM, VRAM, container, Node, and model-loading failures.
Read guide → FoundationLinux for AI developers
Operate GPU servers, model storage, services, permissions, and logs.
Read guide →Production agent deployment checklist
Verify safety, state, budgets, observability, and recovery before launch.
Read guide →Observe behavior—not just uptime
A 200 response does not prove that a model answer was useful, affordable, safe, or based on the expected context.
Nginx for AI applications
Operate reverse proxies, streaming routes, model gateways, limits, and trusted headers.
Read guide →Self-host Langfuse with Docker
Collect traces and evaluations while controlling the observability stack.
Read guide →Canary deployments for LLM features
Release prompts, models, and agent changes to controlled traffic.
Read guide →AI application deployment checklist
Connect health, logging, budgets, rollback, and incident ownership.
Read guide →CI/CD pipelines for AI applications
Gate releases with tests, evaluations, secret checks, and deployment verification.
Read guide → Quality layerAI testing and evaluation
Connect offline evaluations with canaries, traces, production quality, and rollback decisions.
Read guide →