Run language models in production without flying blind.
Evaluation, observability, cost control, security and governance for teams shipping LLM applications past the prototype stage.
The operations layer for intelligent systems in production.
New to LLMOps? Follow the path.
Five steps from understanding the stack to picking your tools - in the order that actually works.
Read the Stack
Understand the eight layers between a prototype and production.
Explore the stack → 2Score your maturity
A weighted 0–100 read on where you stand across the stack.
Take the quiz → 3Run the calculator
Model token spend per request, per user and per year.
Open cost calculator → 4Use the checklist
Close the gaps with 50 production-readiness checks.
Open checklist → 5Choose your tools
Pick what fits each layer from the categorized directory.
Browse tools →Prefer structured learning? Courses & certifications →
LLMOps is the discipline of deploying, monitoring and improving large language model applications after the prototype works. It is the set of practices that turns a demo into a system you can run, measure and trust - across the full lifecycle.
This site turns that discipline into practical checklists, operating models and reference architectures.
Used across major cloud and AI platforms
Vendor definitions independently verified Updated September 2026
Seven major platforms. One operational discipline.
LLMOps isn't a label we invented - these platforms use it independently for the same discipline. We don't define it around one vendor's product; we synthesize the operating model emerging across all of them.
From prototype to production.
A prototype answers one question. Production answers a harder one. LLMOps is the gap between the two columns.
Eight layers between a prototype and production.
A reliable LLM application is rarely a model problem. It is an operations problem spread across these layers.
Which are you building?
Every LLM system fails differently. Find yours - its key risk, the layers it leans on, and the blueprint to follow.
Customer support chatbot
Answers customers and takes actions like lookups and refunds.
A wrong answer becomes a wrong action, and injection can trigger it.
Internal knowledge assistant
Answers staff from internal docs, wikis and systems.
The right answer shown to the wrong person - cross-permission leakage.
RAG search
Grounded answers retrieved from your knowledge base.
Stale index or wrong retrieval - and hallucination when nothing matches.
Agentic workflows
Multi-step agents that plan and call tools to get work done.
Runaway tool calls and actions taken without validation or approval.
Regulated enterprise assistant
LLM workflows in finance, health or the public sector.
No audit trail or unexplainable decisions when a regulator asks.
Go deeper.
The checklist, calculator, maturity score and tool directory are all steps in the path above. These are the reference material behind them.
LLMOps Glossary
Evals, traces, routing, guardrails, embeddings - the vocabulary, plainly.
Browse terms → BlueprintsReference Architectures
Five production LLM systems - diagram, components, failure modes and checklist for each.
View blueprints → WritingArticles & Guides
Deep dives on each layer of running LLMs in production.
Read the guides →Are you actually ready for production?
Tick what is already true for your system. The full 50-point checklist ships as an interactive page and a downloadable PDF.
Good start. Now validate the remaining 44 production checks.
Continue to the full checklist →LLMOps, briefly
Five questions teams ask before they invest in operating LLMs. For the long answers, start with What is LLMOps?
What is LLMOps?
LLMOps (LLM operations) is the practice of running large language models reliably in production - the evaluation, observability, cost control, security and governance that sit between a working prototype and a system you can trust with real users.
How is LLMOps different from MLOps?
MLOps centres on training and deploying your own models. LLMOps usually assumes the model is a third-party API you call, so the work shifts to prompts, retrieval, evaluation, guardrails, token cost and observability around that API rather than the training pipeline.
When do I actually need LLMOps?
The moment an LLM feature faces real users. A demo only has to work once; production has to work on every input, stay within budget, fail safe, and be debuggable when it does not - which is exactly what the LLMOps layers provide.
What does the LLMOps stack include?
Eight layers: prompt management, evaluation, observability, cost control, RAG operations, security, governance and deployment. Each answers a different production question, from "can I roll back a prompt?" to "can I prove a human reviewed this?".
Do I need expensive tools to start?
No. You can start with logging every request, a small eval dataset and budget alerts using open-source tools or your existing stack. The checklist and tool directory show a minimal-to-mature path for each layer.
Independent resource
LLMOps.si is an independent, vendor-neutral resource for teams operating large language models in production - no agenda, no upsell, just the practices that hold up.