Deployment pipelines (CI/CD for LLMs)
Repeatable, tested pipelines to ship prompts, models and RAG changes safely, so updates do not silently break behaviour.
LLMOps Services
LLMOps Services
Trusted across 20+ countries by Fortune 500 companies and growth-stage brands
Run large language models like production software. Noseberry builds the LLMOps backbone, deployment pipelines, prompt and version control, continuous evaluation, monitoring, guardrails and cost control, so your LLM features stay accurate, safe and affordable as prompts, models and data change. The operational discipline that keeps generative AI reliable after launch.
Book a free LLMOps reviewDefinition
What is LLMOps?
LLMOps is the operational practice of deploying, evaluating, monitoring and improving large language model systems in production. It brings DevOps and MLOps discipline to LLMs, and adds what they specifically need: prompt and version management, evaluation of open-ended outputs, retrieval grounding, drift monitoring and token-based cost control. Because LLM behaviour shifts with every prompt, model and data change, LLMOps makes quality measurable and continuous, and keeps a human in the loop on the outputs that matter.
Key takeaways
- LLMOps is the practice of deploying, evaluating, monitoring and improving large language model systems in production.
- LLMs are probabilistic and change with every prompt, model and data update, so they need continuous evaluation, not one-off testing.
- The work covers deployment pipelines, prompt and version control, evaluation, monitoring, guardrails and cost control.
- It keeps AI accurate, safe and affordable over time, with a human in the loop on consequential outputs.
2M+Lives touched
15+Fortune 500 clients
20+Countries served
250+Digital solutions delivered
Scope
What LLMOps services include
The pipelines, evaluation, monitoring and controls that keep LLM systems reliable in production.
Prompt and version management
Versioning and control over prompts, models and configurations, so you know exactly what is in production and can roll back.
Evaluation and testing
Automated evaluation sets and regression tests, so a change is measured for accuracy and safety before it ships.
Monitoring and drift detection
Live monitoring of quality, latency, drift and failures, so problems are caught before users feel them.
Guardrails and human-in-the-loop
Grounding, output controls and review workflows, so the system stays safe and people stay in control of what matters.
Cost and token optimisation
Monitoring and tuning of tokens, caching and routing, so LLM spend stays predictable as usage grows.
Who this is for
Built for teams running LLMs in production
Engineering and AI teams with LLM or RAG features live, who need to ship changes safely and keep quality, safety and cost under control over time.
Signs you need it
- You have LLM features in production but no evaluation or monitoring around them.
- Prompt or model changes break behaviour in ways you only find out about later.
- Quality is drifting and you cannot see why or where.
- Token and inference costs are unpredictable.
- You need to ship LLM changes safely and repeatably, not by hand.
How we work
Instrument, pipeline, operate
1
Assess
We review your LLM systems, how they ship and how quality and cost are tracked.
2
Instrument
We add evaluation, monitoring and logging across quality, safety and cost.
3
Build pipelines
We put CI/CD, prompt and version management in place for safe releases.
4
Guardrail
We add grounding, output controls and human-in-the-loop review.
5
Operate and optimise
We run it, catch drift, and tune cost on a rolling basis.
Why Noseberry
Why choose Noseberry for LLMOps
Evaluation-first
We make LLM quality measurable and continuous, so changes are proven, not hoped for.
Safe, repeatable releases
CI/CD, versioning and rollback for prompts and models, not hand-edited production.
Human-in-the-loop
Guardrails and review keep people in control of consequential outputs.
Proven at scale
2M+ lives touched, 15+ Fortune 500 clients, 250+ solutions across 20+ countries.
Frequently Asked Questions
LLMOps is the operational practice of deploying, evaluating, monitoring and improving large language model systems in production. It brings the discipline of MLOps and DevOps to LLMs, covering deployment pipelines, prompt and version control, evaluation, monitoring, guardrails and cost control.
MLOps operationalises traditional machine-learning models. LLMOps adapts that for large language models, which add prompt management, non-deterministic outputs, retrieval grounding, evaluation of open-ended responses, and token-based cost, on top of standard model operations.
LLM outputs are probabilistic and shift when the prompt, model version or grounding data changes. Without continuous evaluation, quality can regress silently. Automated evaluation sets and regression tests catch that before it reaches users.
Yes. It monitors and tunes token usage, caching and model routing, so LLM inference spend stays predictable as usage grows rather than creeping up unseen.
It is primarily a cloud and operations capability applied to AI, part of our cloud and DevOps practice, and linked from AI because it keeps LLM systems reliable in production.
LLMOps is the operational practice and tooling; AI managed services can run it for you as an ongoing service. AI infrastructure provides the environment underneath. They fit together, and we offer all three.
Run your LLMs with discipline
Book a free review and we will map the LLMOps your systems need.
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