What does a standard production deployment architecture for AI apps look like?

A five-layer path: user → API Gateway → Load Balancer → AI Service (multi-instance) → LLM API / Vector DB. The API Gateway handles auth and rate limiting, the Load Balancer spreads traffic across multiple AI Service instances for HA, and only the AI Service touches the LLM and vector store. A single instance going down does not take the service with it.

What four performance optimizations must ship before going live with an AI app?

Streaming output (so users see tokens immediately, not after 30s of waiting), caching (reuse results for similar questions, saves cost and latency), async processing (offload long tasks so the UI does not freeze), and rate limiting (stop abnormal traffic from overwhelming downstream). Pure model optimization does not save you when users wait 30 seconds for a full response.

Which core metrics should an AI app monitor?

Four mandatory metrics: latency (first-token and end-to-end P95), error rate (sliced by provider/model so you can find the source), token usage (catch abnormal requests inflating the bill early), and cost tracking (cost per successful request is closer to a business metric than success rate). Without the last one, you find out at month-end when it is too late.

How do I choose between Vercel, AWS Lambda, and Docker + K8s?

Vercel for simple apps that need to ship fast (MVPs and side projects, zero ops); AWS Lambda for pay-per-use + auto-scaling workloads (bursty traffic, no need for long-lived connections, requests under 15 min); Docker + K8s for large-scale, high-availability deployments (multi-region, strict SLAs, full runtime control). Do not start on K8s — the ops overhead will sink a small team.

What are the minimum security defenses for a production AI app?

Three indispensable layers: prompt-injection defense (tag external content as material not instruction, scope-guard the system prompt), output filtering (sensitive fields, internal SOPs, API keys must never leak), and sensitive-data masking (mask phone / email / IDs before the model sees them and again before responses go out). Skip a layer and you ship an incident in week one.

Production Deployment

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