AI, Data & Intelligence
LLMOps
The operating discipline for language model applications in production: prompt and model versioning, evaluation pipelines, tracing, guardrails, cost monitoring and safe release of changes.
Capability overview
What llmops involves
LLM applications change constantly even when nobody touches the code: providers update models, documents in the index change and user questions drift. LLMOps provides the practices and tooling to notice quality shifts, release improvements safely and keep costs predictable as usage grows.
We set up tracing of every request with tools such as Langfuse, LangSmith or OpenTelemetry-based platforms, maintain versioned prompts and evaluation datasets, run automated evaluations before each release, apply guardrails for unsafe inputs and outputs, and report quality, latency and spend per feature.
What is included
LLMOps capabilities
Tracing and observability
Each request's prompt, retrieved context, model version, output, latency and token cost recorded for debugging and analysis.
Evaluation pipelines
Offline test suites and LLM-as-judge scoring calibrated against human ratings, run in CI before prompts or models change.
Guardrails
Input and output checks for prompt injection, personal data leakage, toxic content and off-topic requests.
Release management
Prompt and model versions promoted through environments with canary exposure and quick rollback.
Cost and capacity management
Budgets, rate limits, caching and model routing policies monitored with alerts on unusual spend.
How we work
How we deliver llmops
Current state review
Existing LLM applications, logging, prompt storage and release practices assessed.
Tooling setup
Tracing, prompt management and evaluation tools deployed, self-hosted where data policies require.
Evaluation baseline
Golden datasets and scoring methods created so current quality is measured before any change.
Pipeline integration
Evaluations and guardrail tests added to CI/CD with thresholds that block regressions.
Operational reviews
Monthly reviews of quality trends, incidents, user feedback and cost with product owners, ending with a short list of agreed improvements.
Related capabilities
Related capabilities in Generative AI & LLM Engineering
Generative AI Strategy
Generative AI Strategy within our generative ai & llm engineering services, scoped after a short discovery conversation.
Generative AI Applications
Applications that produce new content for your business, such as proposals, product descriptions, marketing variants, reports and images, with brand rules, approval workflows and provenance built in.
Large Language Model Applications
LLM-powered processing behind the scenes: classifying, extracting, summarising and routing large volumes of text such as emails, tickets, contracts and feedback, often with no chat interface at all.
Retrieval-Augmented Generation (RAG)
Engineering of retrieval-augmented generation pipelines that ground language model answers in your documents, covering ingestion, chunking, hybrid retrieval, reranking, citations and systematic evaluation.
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Questions & answers
Questions about LLMOps
Cannot find what you need? Our team responds to technical and commercial questions within one business day.
Ask a questionMLOps focuses on training and serving models you build. LLMOps usually deals with models you consume, so the emphasis shifts to prompts, retrieval, evaluation of open-ended outputs, guardrails and token costs.
Pinned model versions prevent surprise changes. Before adopting a new version, the evaluation suite runs against it and the switch happens only if quality holds or improves.
Yes, so tracing is configured with redaction, access controls and retention limits, and can be self-hosted within your environment when policy requires it.
Initial setup is a fixed-price project. Ongoing LLMOps operation, including evaluation maintenance and monthly reviews, is a monthly managed service.
Next step
Discuss llmops with Acmez
Share what you need to change, build, integrate or support. We will map the practical next step.