BlogLLMOps
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LLMOps: running prompts and agents in production
LLMOps is the operational discipline for prompts and agents in production: versioning every change, observing every call, holding cost to a budget, and keeping behaviour inside a variance you have measured. It is what turns a prompt that works in a demo into a service a product can depend on.
The prompt is the most important code in the codebase and usually the least engineered: a string copied between branches, with no record of what it looked like last Tuesday, no number that says whether today's change is better, and a bill that compounds with every loop step. LLMOps gives that string the treatment the rest of the stack already has. The articles below cover its parts in turn: what a prompt version records, what to trace so "why did my agent do that?" has an answer, where the cost goes and how to enforce a ceiling rather than monitor one, and what to do about a model that is non-deterministic even at temperature zero.
The scale of the problem is real. Enterprise LLM API spending more than doubled in six months, rising from $3.5 billion in late 2024 to $8.4 billion by mid-2025 as workloads moved into production, and 37% of enterprises now spend over $250,000 per year on LLMs. Per task, running an AI agent in production costs $0.0003 to over $0.50, a range that spans three orders of magnitude; and without a hard ceiling the outliers are the story: one multi-agent pipeline looped for 264 hours, at a final cost of $47,000, from a system budgeted at under $200 a month.
Articles on LLMOps (8)
How Much Does an AI Agent Cost to Run in Production?
Concrete production cost breakdown for AI agents — real model prices, token math, tool call fees, and three levers that cut your inference bill in production.
· 7 min read
RAG Tools: A Production Engineer's Stack Checklist
The engineering guide to RAG tools: chunking strategies, hybrid retrieval, faithfulness thresholds, and CI gates that catch regressions before they ship.
· 6 min read
LLM Observability: A Production Engineer's Guide
LLM observability captures traces, eval scores, and cost signals from agents at runtime—catching failures that HTTP 200 hides. A concrete engineering guide.
· 6 min read
Why Is Your LLM Non-Deterministic? Own the Variance
LLMs are non-deterministic even at temperature=0. Learn why batch-invariance failure is the real cause, and how to declare and gate on a variance budget in CI.
· 6 min read
How to add automatic model fallback to your AI app
Automatic model fallback switches your AI feature to a backup the moment the primary fails. Here’s the failure modes, the DIY path, and the runtime approach.
· 6 min read
LLM cost optimization in production
LLM cost optimization means running on the cheapest model that clears your quality bar. Here’s how quality-floor model selection works in production.
· 6 min read
What is prompt versioning?
Prompt versioning tracks every change to an LLM prompt—instructions, model, parameters, schema—as an immutable snapshot you can evaluate, compare and roll back.
· 5 min read
Functional AI: Your Prompt or Agent, as an Agentic Function
Why we give your prompt or agent a typed contract and host it as a versioned Agentic Function, instead of a string copied between branches.
· 6 min read
FAQ
- What is LLMOps?
- LLMOps is the operational discipline for prompts and agents in production: versioning every change, observing every call, holding cost to a budget, and keeping behaviour inside a measured variance. It covers the lifecycle after the prompt works: deploy, trace, measure, roll back.
- How is LLMOps different from MLOps?
- MLOps manages models you train: data pipelines, training runs, model registries. LLMOps manages behaviour you rent: the model is a provider's, so the operational surface is the prompt, the tools, the context, the version and the call, and the risks are regressions you did not cause, non-determinism and cost that compounds per step.
- What does prompt versioning record?
- Every change to a prompt as an immutable version: the instruction text, the model identifier, the parameters and the output schema, with its own evaluation results, so "what changed?" and "roll it back" are one-line answers rather than archaeology. Git alone does not do this, because the model and its parameters live outside the repository.
- What should LLM observability capture?
- Every call's input, output, status, model, cost and tokens, and for an agent every step: each tool call with its arguments, result, latency and error. On top of the trace, quality signal from evals and a cost view by version and by key, so a regression, a loop and a spend spike are each visible before a user reports them.
The LLM ops platform, in one page
Functional AI is the LLM ops platform: evaluation, runtime and observability for prompts and agents. Build your prompt or agent as an Agentic Function; it evaluates it, runs it, traces every call, and lets you ship a new version without redeploying.
What Functional AI is →