Deployment, Operations, Cost, and Performance Optimization
Deployment and operations
Understand release patterns for AI systems: canary and blue/green deploys, feature flags for prompt and model changes, and versioned configuration so you can roll back a prompt as fast as code. Know the operational signals: latency percentiles (p50/p95/p99), error and retry rates, token usage, cache hit rates, and quality metrics sampled from production. Set up tracing across the orchestration chain so a slow or failing step is identifiable.
Cost and performance levers
Know the levers and their tradeoffs: model tiering, prompt and output length control, prompt caching of stable prefixes, batching, streaming for perceived latency, semantic caching of repeated queries, and retrieval precision to shrink context. Understand that output tokens usually cost more than input tokens and that latency scales with generated length. Concurrency and rate limits shape throughput; backoff and queueing handle bursts.
Reliability
Design for provider outages: fallbacks to alternate models or regions, graceful degradation, and circuit breakers. Monitor for quality drift after model version changes and pin versions where stability matters.
Traps
Questions bait you with "scale up the model" when the fix is shorter prompts or caching, or "add more replicas" when latency is dominated by sequential tool calls. Watch for optimizing average latency while ignoring p99, and for cost cuts that silently degrade quality without eval coverage. Another trap: unbounded retries amplifying load during an outage.
Rule of thumb
Measure before optimizing: attribute latency and cost to each pipeline stage, then attack the dominant term. Cap retries with backoff, and gate every cost or model change behind evals and a rollback path.