LLMOps: run LLMs in production without chaos
LLMOps for SMEs: evals, prompt versioning, observability, token cost, guardrails and deploy. Different from classic MLOps and pipeline automation.
LLMOps: production for token systems
LLMOps is the set of practices to build, deploy, observe and improve LLM-based apps. Fewer “AI strategy” slides, more replayable pipelines: versioned prompts, evals, logs, budgets, incidents.
Unlike classic tabular MLOps: output is language (fuzzy), cost is often per token, external deps (model APIs, tools, MCP) move outside your control, and the “dataset” is sometimes a living document corpus.
Without LLMOps every dev keeps a prompt in a pad, the surprise bill lands end of month, and nobody knows why quality dropped on Tuesday.
Eval: the only compass
Three useful layers: (1) offline unit cases (gold, deterministic assertions on JSON/structure), (2) bounded LLM judges (with distrust and human sample), (3) online (thumbs, edits, incidents).
Start small: 30 critical business cases. Block merge if regression exceeds a threshold. A model “better on a public bench” that breaks your 30 cases is not an upgrade.
For RAG, split retrieval eval and generation eval. For agents, evaluate the trajectory (tools called), not only final text.
Versioning: prompts, models, tools
Everything that changes behavior has an ID: prompt/system, skills, temperature, model (pin snapshot), tools/MCP, RAG corpus. Log those IDs on every request.
Ban “we changed the prompt in prod without a ticket”. Same discipline as code: PR, review, rollback.
Feature flags: enable a new prompt on 5% traffic or an internal workspace before 100%.
Observability and cost
Traces: retrieve / generate / tools latency, tokens in/out, provider errors, retries. OpenTelemetry when you can. Without traces, agent debug is random log reading.
Budget: ceiling per team / project / day, alerts, dashboards. Multi-agents and ultra modes multiply windows: cost is no longer linear with “one chat”.
Online quality: human edit rate, rejects, support tickets tied to AI. Those are your real KPIs, not the vendor leaderboard.
Guardrails and deployment
Guardrails: schema validation, PII filters, tool allowlist, HITL on risky actions, sandbox. See human-in-the-loop for supervision design.
Deploy: environments (dev / staging / prod), secrets out of code, timeouts, circuit breakers if the provider lags. An agent without timeout is an open bill.
Incident: “quality drop” runbook (last change, logs, prompt/model rollback), “cost spike” runbook (top routes, multi-agent, tool loops).
Minimal LLMOps for an SME (checklist)
1) AI system owner. 2) 30 eval cases in CI. 3) Pinned prompts and models. 4) Logs + traces on 100% prod traffic. 5) Token budget and alert. 6) HITL on risk. 7) Weekly 30-minute quality/cost review.
You do not need an enterprise LLMOps platform on day 1. You need these 7 points written and kept.
If you want to set an LLMOps base on a real agent or RAG, we can scope in 20-40 minutes.
FAQ
- What is LLMOps?
- Practices to operate LLM apps in production: evals, versioning, observability, cost, guardrails, deploy and incidents.
- LLMOps vs MLOps?
- Same spirit (reproducibility, monitoring), different object: language, tokens, prompts, external tools, document corpora rather than tabular features alone.
- Where should SMEs start?
- 30-case eval + pin model/prompt + token logs. Then HITL and budget. Platform comes after discipline.
- Do we need a paid LLMOps platform?
- Not on day 1. Existing tools (CI, OpenTelemetry, log tables) are enough to start. Buy when volume or compliance requires it.
- How do RAG and LLMOps link?
- RAG needs retrieval eval, corpus versioning and freshness monitoring. LLMOps provides the frame; the RAG LLM guide details the pipeline.
- Do multi-agents change LLMOps?
- Yes: more traces, more tokens, more failure modes. Hop and tool ceilings become mandatory. See multi-agents and coding-agent controls news.
Scope your first AI agent
20 minutes to review your tools, data and the first useful case. No jargon, no commitment.