LLMOps
CI/CD, evaluation, observability, and cost control so models ship and stay reliable in production.
Traditional software fails loudly. A model regression returns a confident, well-formed, wrong answer, and every monitor stays green. The operational practices that work for services do not detect this class of failure at all.
Meanwhile cost behaves unlike other infrastructure: it scales with tokens rather than requests, a prompt change can double spend without a deploy, and the bill arrives a month after the decision that caused it.
LLMOps is the practice that closes both gaps — treating prompts and models as versioned artifacts, evaluation as a build gate, and cost as a monitored budget.
What the engagement includes
Evaluation pipelines as CI gates
Automated evaluation on every prompt, model, or retrieval change, wired so a quality regression fails the build rather than reaching users.
Prompt and model versioning
Prompts, model versions, and parameters as versioned artifacts with a rollback path — so "what changed?" has an answer.
Observability
Traces, token accounting, latency, and quality signals per request, with PII kept out of the logs by design rather than redacted afterwards.
Cost control
Model routing by task difficulty, caching, context budgeting, and per-feature spend visibility, so cost is a monitored number rather than a monthly surprise.
Release and rollback
Canary and staged rollout for model and prompt changes, because a provider updating a model underneath you is a production event.
Who this is for
- Teams with LLM features live and no way to detect quality regressions
- Organisations whose model spend is growing faster than usage
- Platform teams supporting several product teams building on shared models
When this is the wrong engagement: If nothing is in production yet, this is premature. Build the system first; the operational practice is worth adding as it approaches real users.
How it runs
- Discovery (30 minutes). A working call to map your stack, constraints, and the highest-value first step. No pitch deck.
- Scoped fixed-price proposal. A written statement of work with deliverables, milestones, and a fixed price — not an open-ended time-and-materials meter.
- Delivery. Senior architects execute against milestones, with governance applied to every AI-agent action from day one.
- Operate or hand off. Monitored operation, or a clean handoff with runbooks so your team can run it without us.
Questions we get asked
How is LLMOps different from MLOps?
MLOps assumes you train and own the model, so it centres on training pipelines, feature stores, and model registries. LLMOps usually assumes you consume a model you do not control, which moves the centre of gravity to prompts, retrieval, evaluation, and cost. They overlap in deployment and monitoring, and they diverge sharply in what is versioned and what can regress underneath you without any deploy on your side.
What does an evaluation gate actually block?
A change that reduces answer quality below an agreed threshold on a labelled evaluation set. In practice it catches prompt edits with unintended side effects, retrieval changes that improve one query class while degrading another, and provider model updates that shift behaviour. It is deliberately a build gate rather than a dashboard, because a dashboard requires someone to be looking.
Can you reduce our model spend?
Usually, and the largest wins are routing and caching rather than switching to a cheaper provider. A great deal of production traffic is classification, extraction, or scoring that a small fast model handles as well as a frontier one at a fraction of the cost. We will not quote a percentage before seeing your traffic, since any number offered in advance would be a guess dressed as a promise.
Do we have to adopt a specific vendor platform?
No. The practice is built on the CI/CD and observability stack you already run, and it works with self-hosted, open-source components throughout. Where a commercial tool genuinely earns its place we will say so, and we do not receive anything for that recommendation.
Related services
RAG Platform Development
Grounded retrieval on your own corpora, evaluated for accuracy and built to keep your data isolated.
AI Governance
Identity per agent, autonomy tiers, and audit trails your risk and security teams will actually accept.
Cloud AI Modernization
Bring production AI to a multi-cloud estate — AWS, Azure, GCP — without a rip-and-replace.
Start with a 30-minute discovery call
No pitch deck. We map your constraints and tell you the highest-value first step — including when that step is not an engagement with us.
Book a discovery call