LLMOps
We set up LLMOps — infrastructure for operating LLM applications in production: monitoring answer quality and cost, logging requests, tracking errors and hallucinations, prompt versioning, alerts. So your AI solution works stably and predictably on costs. Honestly upfront: LLMOps makes the AI's work OBSERVABLE and manageable but does NOT make the model itself smarter or more accurate — it is an operational layer; it adds its own cost and maintenance, but without it AI in production is a 'black box' with unpredictable costs and quality.
LLMOps — overview
LLMOps is practices and tools for operating LLM applications in production (by analogy with DevOps/MLOps): end-to-end logging of requests and answers, monitoring quality and the appearance of hallucinations, tracking cost (tokens/expenses) and latency, versioning and A/B of prompts, alerts on failures and anomalies, tracing of chains (e.g. via Langfuse and similar). Honestly about the role, this is key: LLMOps does NOT improve the model itself — it does not make the AI smarter, more accurate or less prone to hallucinations. It is an operational layer that makes the AI's work VISIBLE and MANAGEABLE: you see what is happening, how much it costs, where quality drops, and can react. Confusing LLMOps with 'improving AI' is wrong — it is about operation, not intelligence. Honestly about necessity: without LLMOps AI in production is a 'black box': you do not know the real quality, costs can unexpectedly grow, and problems (a rise in hallucinations after a model change, a cost spike) are discovered late. So for serious production it is important, often necessary infrastructure. Honestly about cost and maintenance: LLMOps tools and log storage are their own costs and support; it is an investment in reliability, not a 'free bonus'. Honestly about the effect: it gives control over quality and cost, early problem detection and the ability to improve on data — but you must do the improving (based on what monitoring showed). Honestly about access: access to the AI application and infrastructure is needed. An important boundary: this is operation; output quality — prompts (890)/RAG (893)/guardrails (905); quality evaluation as a discipline — 899; prompt management — 903; cost optimization — 904. Picture this: instead of 'AI in production as a black box' — observability of quality, cost and errors with alerts. The base price starts from 55,000 ₽ (depends on scale).
Problems we solve
- AI in production is a 'black box': quality and costs are unclear.
- Token cost grows unnoticed, the budget is unpredictable.
- Problems (rising hallucinations, failures) are discovered late.
- No prompt versioning and chain tracing.
What's included in the LLMOps service
- Logging of requests/answers and chain tracing
- Monitoring of quality, hallucinations, latency
- Cost tracking (tokens/expenses) and alerts
- Versioning and A/B of prompts
- Honest boundaries (observability, not 'a smarter model')
- Early detection of problems and anomalies
- A link with eval (899), prompt management (903), cost-opt (904)
- Handover and review with you
What you get
- Observability of AI quality, cost and errors in production
- Predictable costs and early problem alerts
- Prompt versioning and tracing
- Honest boundaries (an operational layer; improvement is yours)
How the work goes: steps
- We assess the AI application and monitoring metrics; collect access
- We set up logging, monitoring, alerts, versioning
- We hand over the process, honestly set boundaries (it is operation) with you
Why PDV Expert
- Fixed price and timeline — no surprises on the invoice.
- Report and recommendations in plain language — clear without a technical background.
- In touch at every step and answering questions about the result.
FAQ
Will LLMOps make my AI smarter and more accurate?
No, honestly: LLMOps does not improve the model itself — it does not make AI smarter, more accurate or less prone to hallucinations. It is an operational layer that makes the AI's work visible and manageable: you see quality, cost, errors and can react. Quality improvement is the job of prompts (890), RAG (893), guardrails (905). Confusing operation with intelligence is wrong.
Can we do without LLMOps?
For experiments — yes, but for serious production it is risky. Without LLMOps AI is a 'black box': you do not know the real quality, costs can unexpectedly grow, and problems (a hallucination spike after a model change, cost growth) surface late. It is important, often necessary reliability infrastructure. We will honestly say whether its scale is justified for your stage.
Is it a one-off setup?
No, honestly: LLMOps is an operational practice and infrastructure with its own costs (tools, log storage) and maintenance. It is an investment in reliability and control, not a 'free bonus'. But it pays off in cost predictability and early problem detection. We honestly factor in the cost of ownership.
About the provider
The «LLMOps» service is provided by PDV Expert — a team specialising in «Site quality». We work under contract and deliver a written report with recommendations.