MLOps and LLMOps for Production AI Systems
MLOps and LLMOps both keep AI systems reliable in production by versioning what changes, gating releases with tests, monitoring live behaviour and triggering retraining or re-evaluation when quality drops; they diverge because MLOps owns a model's training data and weights, while LLMOps governs a pretrained model's prompts, retrieval pipeline and per-token cost.
Book a solution review See the related serviceBy Netbase AI and Automation Engineering Desk · Reviewed by David (CEO) · Updated · 8 min read
This guide is for an engineering lead or AI product owner moving a model or an LLM-based feature from a pilot into production, and for the platform or DevOps engineer who will own its release pipeline afterward. It sits under the AI automation for business operations guide and feeds agentic AI automation and MLOps and responsible AI operations work.
In this guide
- What is the difference between MLOps and LLMOps?
- MLOps vs LLMOps: what changes, what doesn't
- A minimum production checklist, in order
- Where AI fits
- What delivery record exists, and what does not
- Alternatives: when not to build a full pipeline yet
- Limits of this guide
- Frequently asked questions
- Next step
What is the difference between MLOps and LLMOps?
MLOps and LLMOps share the same four jobs: automate the path from a working artifact to a served one, version everything that can change, monitor what is running, and feed production signals back into the next iteration. They diverge because MLOps usually trains a model on the team's own data, so the artifact to version is a trained model file plus the data and code that produced it, and failure shows up as accuracy or data drift measured against a held-out test set. LLMOps starts from a pretrained foundation model nobody on the team trained, so the artifacts to version are prompts, a retrieval pipeline and tool/function definitions, cost is dominated by per-token inference rather than batch training, and failure shows up as hallucination, prompt injection or context overflow rather than a falling accuracy score.
MLOps vs LLMOps: what changes, what doesn't
| Dimension | MLOps | LLMOps |
|---|---|---|
| What gets versioned | Training data, feature definitions, model weights | Prompts, retrieval/vector index, tool and function definitions |
| Who trained the model | The team, from its own data | A model provider; the team adapts and governs it |
| Cost shape | Training-dominated; inference is comparatively cheap | Inference-dominated; cost scales per token and per call |
| Primary failure mode | Accuracy decay, data drift | Hallucination, prompt injection, context overflow |
| Evaluation signal | Accuracy, F1, AUC against a held-out set | Faithfulness, relevance, safety and cost per outcome |
| Shared practice | Registry, CI/CD gate, monitoring, retraining/re-evaluation loop, rollback path | Same four practices, applied to a different artifact set |
Choose MLOps-first thinking when the team owns training and the question is "did the model get worse"; choose LLMOps-first thinking when the team owns a prompt and a retrieval layer around someone else's model and the question is "did the output get worse or the bill get bigger." A system that does both (for example, a classic model scoring a case and an LLM explaining the score) needs both practices side by side, not a single pipeline that pretends they are the same.
A minimum production checklist, in order
-
Registry before release
Register the model version or the prompt/retrieval version, with the data or corpus version it was built against, before it serves a single production request; an artifact nobody can name cannot be rolled back.
-
A CI/CD gate, not a manual sign-off
Run automated tests on every change: for a model, a convergence or accuracy-threshold check against a held-out set; for an LLM feature, a regression suite of prompts with expected properties (faithfulness, refusal behaviour, output format). A change that fails the gate does not reach production, regardless of who wrote it.
-
A monitoring signal that maps to the actual failure mode
For a model: input distribution and accuracy proxies in production, since the true label often arrives late or never. For an LLM feature: faithfulness, safety-filter hit rate and cost per request; see AI agent observability and cost control for the equivalent question on a live agent's own actions and spend, which is a related but separate monitoring target from the model or LLM feature it calls.
-
A retraining or re-evaluation trigger, defined in advance
A schedule, a data-availability event or a monitored metric crossing a threshold should start a defined job, not an ad hoc conversation; the trigger and its owner are written down before the first drop happens, in the same spirit as AI agent drift detection defines a trigger for an agent's own behaviour change.
-
A rollback path that has been exercised, not assumed
Keep the previous registered version servable and practice switching back to it before an incident forces the first attempt.
Where AI fits
A classic ML model (fraud scoring, demand forecasting, recommendation) is the MLOps case: the team trains it, so it owns the registry, the training pipeline and the drift evaluation. An LLM-based feature (a drafting assistant, a retrieval-augmented answer, a classification prompt) is the LLMOps case: the team adapts a provider's model, so it owns the prompt/retrieval registry, the regression suite and the per-token cost ceiling. Netbase's AI tooling is chosen per project (model-agnostic); we work with models from OpenAI, Anthropic (Claude), Google (Gemini) and Meta (Llama), among other commercial and open-weight models, chosen per project.
-
In delivered work
MLOps pipeline
Delivered for a client that is not named; no industry, figure or duration is published for that record.
-
Available capability
MLOps and LLMOps pipeline design
Registry, CI/CD gate and retraining-trigger design as part of agentic AI automation delivery; not yet tied to a published case that names this instrumentation specifically.
What delivery record exists, and what does not
Beyond the named 4over4 recommendation engine, Netbase has delivered further AI projects for clients, including the anonymised MLOps pipeline above (F072): no client name, industry, figure, duration or accuracy result is published for that record. Netbase holds ISO 27001 certification and a SOC 2 Type II attestation for its own operations, with no certificate number, auditor or scope published (F022, F051, F052). No published Netbase record states a model's accuracy figure, a retraining cadence, an LLM's cost-per-request or a rollback time for any project; this guide does not invent one.
Alternatives: when not to build a full pipeline yet
A single internal pilot with one user group and no external commitment does not need a registry, an automated CI/CD gate or a scheduled retraining job; a dated spreadsheet of the model or prompt version used, and a human who checks the output weekly, covers the risk at that stage. Build the registry and the automated gate once the system serves a customer-facing decision, a second team depends on its output, or a single bad output could cause a compliance, financial or safety problem; building the full practice earlier adds maintenance cost with no matching risk to cover, and building it later than that point means an incident is doing the design work instead of the team.
Limits of this guide
- This guide does not cover feature-store or vector-database product selection, GPU capacity planning, or fine-tuning versus prompting trade-offs; those are implementation choices inside the practice described here, not the practice itself.
- It does not cover supervising a live agent's own tool calls, spend or approval chain once a model or LLM feature is wrapped in agent logic; see AI agent observability and cost control and AI agent security and human approval controls for that question.
- No figure, cadence or duration from Netbase's own delivery record is published here beyond what F072 states.
Plan the next step with a Netbase consultant
Frequently asked questions
No; a pilot with one user group and no external commitment is covered by a dated version note and a human spot-check, but add the registry as soon as a customer-facing decision, a second dependent team or a compliance-relevant output is involved.
The four practices (registry, CI/CD gate, monitoring, retraining or re-evaluation trigger) apply to both, but each artifact type needs its own registry entry, test suite and monitoring signal, because a model's accuracy drift and an LLM's hallucination or cost spike are different failure modes that a single shared pipeline would mask.
A schedule, a defined data-availability event, or a monitored accuracy or quality metric crossing a pre-agreed threshold; a one-off manual patch to a single bad output is not a substitute for finding out whether the underlying data or prompt has shifted.
Next step
Most Netbase projects run on fixed-price contracts by default (F067); book a solution review to scope a production MLOps or LLMOps pipeline for a specific model or LLM feature.
Work behind this guide
Netbase delivered an MLOps pipeline remotely, combining AI consulting, engineering and integration.
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