LLMOps

LLMOps adapts the ideas of DevOps and MLOps to applications built on large language models. Its core loop is: version every prompt, model choice and retrieval configuration; run an evaluation suite before any change reaches users; trace every request with its inputs, outputs, latency and cost; monitor quality and spend over time; and roll changes out gradually with the ability to roll back.

It differs from MLOps because the artefact being managed is usually not a model you trained but a combination of prompts, a third-party or self-hosted model, retrieved context and tool calls. Changes come from many directions, including the provider updating the model without you changing anything. That makes evaluation and observability more central than training pipelines.

LLMOps is not a product you buy, though tools help. It is a discipline that a small team can practise with a golden dataset, a tracing library and a change process. The failure mode it prevents is the common one: a demo that worked, shipped, and slowly stopped working with nobody able to say when or why.

• CTO / Head of Engineering: the difference between an AI feature that stays reliable and one that generates support tickets every time a prompt is tweaked.
• CFO: cost tracing per request and per account is the only way to know whether an AI feature is profitable.
• Product manager: evaluation results give an objective basis for deciding whether a change improved the product.
• Compliance officer: versioned prompts and traced decisions provide the audit trail regulators ask for.

LLMOps exists because LLM applications fail differently from conventional software. There is no compile error when a prompt change breaks a case; the output just becomes slightly worse in ways users notice and tests do not. Providers update models silently. Costs scale with usage in ways that are easy to miss. The discipline gives you evidence before shipping and visibility after. The trade-off is the effort to build and maintain a golden dataset and to treat prompts like code. For a small team it is a few days of setup and a habit; skipping it is why so many pilots never make it to reliable production.

• A SaaS company running its evaluation suite automatically on every prompt change to its in-app copilot before merge.
• A bank tracing every request in its document-extraction pipeline so that a wrong field can be traced to the prompt and model version responsible.
• A retailer monitoring inference cost per order for its product-description generator and routing simple items to a cheaper model.
• A hospital re-running clinician-graded evals when its voice agent's underlying model is updated by the provider.
• A logistics platform rolling out a new exception-handling prompt to a small cohort of dispatchers before full release.

Technique / practiceA discipline your team practises or a partner provides. Eazyware sets up evaluation, tracing and versioning during delivery and continues them under Maintenance & Support care plans, in line with our evals-over-demos stance.

Eazyware service that covers it: Software Maintenance & Support. Starting prices are on the pricing page.