LLMs are moving from demos to critical user-facing and internal workflows. Productionizing them demands reliability, cost control, and governance that many organizations are still building. The same discipline you apply to databases, APIs, and queues applies to LLM calls: they can fail, they have latency and cost, and they need to be versioned and monitored.
Reliability patterns
Treat LLM calls as external dependencies: timeouts, retries, fallbacks, and circuit breakers. Version prompts and models; A/B test and canary. Monitor latency, error rates, and quality metrics (e.g., relevance, safety scores). Start with a simple wrapper or gateway that enforces timeouts and retries so every team doesn't reinvent the wheel. As usage grows, add circuit breakers so that a failing or slow model doesn't take down your application.
Implement fallbacks: when the primary model is unavailable or returns low-confidence results, fall back to a simpler model or a cached response. Define SLAs for latency and availability and track them in your observability stack. Run chaos-style tests where you simulate model outages or degradation so your team knows how the system behaves under failure.
Cost at scale
Token usage grows fast. Implement caching, prompt compression, and tiered model strategies (small/fast for simple tasks, larger models for complex ones). Set budgets and alerts per team and use case. Break down cost by use case and by team so you can see who is driving spend and where optimization will have the most impact. Review usage regularly and trim or consolidate prompts and contexts that are larger than necessary.
Consider prompt caching and response caching where the same or similar inputs recur. Use smaller models for classification, routing, and simple extraction; reserve larger models for tasks that need the extra capability. Document your cost model and share it with product and eng so that new features are designed with cost in mind from the start.
Governance and compliance
Define data handling: what goes to third-party APIs, what stays on-prem. Implement PII detection, content filters, and audit logs. Align with legal and security on model risk classification and approval workflows. Map your LLM use cases to your compliance obligations (SOC 2, GDPR, HIPAA where applicable) and ensure that data flows and retention are documented and controlled.
Establish a model and prompt review process for high-risk or customer-facing use cases. Keep an inventory of which models and prompts are in production, who owns them, and when they were last reviewed. When you add a new vendor or model, run it through the same security and compliance review you would for any new third-party dependency.
LLMs in production are software systems first. Apply the same rigor you use for payments or identity-then add model-specific controls.