LLMOps — LLM in production

What LLMOps is

LLMOps (Large Language Model Operations) is the set of practices for running and maintaining LLM products in production — the equivalent of DevOps/MLOps, but adapted to the unique challenges of language models: non-deterministic output, variable cost, hallucinations, and dependence on a third-party API.

The difference from a prototype: a prototype needs to work once, on your machine. A production product needs to work a million times — reliably, quickly, cheaply and safely — even when the provider's API goes down, even when a user tries to break it, and even when volume spikes 10x.

The lifecycle of an LLM product

LLMOps is a loop, not a straight line:

1. Development: prompt, RAG, agent — with evals from day one.
2. Testing: the evals run in CI. A change doesn't pass if quality drops.
3. Deployment: a controlled rollout (staging → canary → production).
4. Monitoring: Observability on real traffic — quality, cost, latency, errors.
5. Improvement: production failures become new eval cases, and back to step 1.

Versioning — prompts and models

In the LLM world, the prompt is code — it affects behavior just like logic. So:

• Prompts in git, not pasted into code or a DB without history. Every change goes through review and evals.
• Pin the model version. Don't use an alias that shifts under you — specify an explicit version (e.g. claude-opus-4-8), because a model upgrade can change behavior without you knowing.
• Centralized config. Model, temperature, prompt and parameters in one place you can change without a deploy.
• A/B and rollback. Infrastructure to run two prompt/model versions in parallel and roll back immediately if something breaks.

Reliability and model routing

You depend on a third-party API. Plan for failures:

• Retries with backoff: transient errors and rate limits happen. Retry with increasing delay.
• Fallback model: if the primary provider goes down or is slow — automatically switch to an alternative model/provider.
• Timeouts: don't let a request hang the system. Set a timeout and handle it.
• Model routing: route by complexity — a cheap model (Haiku/mini/Flash) for most calls, premium only for complexity. Saves money and load.
• Circuit breaker: if a provider keeps failing, "cut it off" temporarily instead of continuing to retry.

Cost, latency and caching

LLM cost can quietly explode. Control it:

• Prompt caching: providers let you cache the fixed part of the prompt (system, fixed context) — significant savings on repeated calls.
• Semantic caching: if a similar question was already answered — return the cached answer instead of a new call.
• Streaming: stream the response to the user to improve perceived latency.
• Budgets and limits: set cost ceilings and alerts. See the cost-reduction guide.
• Measure cost per request/user — to know what's really expensive and optimize correctly.

Pre-production checklist

• ✅ Evals run in CI and block regressions
• ✅ Prompts in git with review; model version pinned
• ✅ Guardrails and prompt-injection defense
• ✅ Retries, timeouts and a fallback model
• ✅ Observability: logging, metrics and tracing
• ✅ Cost control: caching, budget and alerts
• ✅ Validation of every output before downstream use
• ✅ A rollback plan and incident response