A procurement document asks for a ModelOps platform. The engineering team has been building an MLOps platform. An analyst brief on the same desk describes AIOps, and the newest vendor deck says LLMOps. Nobody in the meeting is wrong, and nobody is describing the same thing.
These are not four competing product categories. They are four different scopes, invented by different audiences, that overlap in the middle. Buying the wrong one is expensive in a specific way: you end up with excellent delivery tooling and no governance evidence, or a governance console with nothing underneath it that can ship a model. If the MLOps side of that comparison is the unfamiliar one, MLOps platform explained defines it by the seven layers it has to cover.
Comparison table: the four labels side by side
| Label | Whose vocabulary | Primary unit of concern | What you buy |
|---|---|---|---|
| MLOps | ML and platform engineers | The pipeline that produces and serves a model | Training, orchestration, registry, serving, monitoring |
| ModelOps | Risk, audit and enterprise architecture | The portfolio of models in production | Inventory, approvals, lineage, controls, retirement |
| AIOps | IT operations and SRE | Infrastructure and incident telemetry | Anomaly detection and correlation for your systems |
| LLMOps | Application teams shipping GenAI | Prompts, traces, retrieval and evaluation | Eval harnesses, tracing, guardrails, prompt versioning |
Read that table twice before shortlisting anything. Three of the four rows describe tooling that operates on your models. One of them, AIOps, describes tooling that operates on your servers.
ModelOps: governance over the whole model portfolio
ModelOps is the enterprise-architecture framing, and its defining move is that it does not care whether a model is machine learning. A credit scorecard built from business rules, an optimisation solver, a statistical forecast and a gradient-boosted classifier are all models in the ModelOps sense, because they all make consequential decisions and all need the same controls: an owner, an approval record, a validation date, a monitoring plan and a retirement path.
That scope explains why ModelOps tooling looks the way it does. The centre of a ModelOps platform is an inventory, not a pipeline. The questions it is built to answer are portfolio questions:
- How many models are in production, and who owns each one?
- Which of them are past their revalidation date?
- Which decisions in the business depend on a model nobody has reviewed in eighteen months?
- What is the evidence trail if a regulator asks about one specific decision?
If those questions sound like your actual problem, an MLOps platform alone will not solve them. It will hold most of the raw material — a registry knows what versions exist and when they were promoted — but it will not hold the business context, the validation memos, or the non-ML models. What a registry can and cannot be relied on for is the subject of model registry patterns that hold in production.
MLOps: the delivery pipeline for machine learning
MLOps is the engineering framing, and it inherits its shape from DevOps. The unit of concern is the path from data to a deployed model, and the maturity ladder that most reference architectures describe runs from manual notebooks, to an automated training pipeline, to full CI/CD for the pipeline itself.
An MLOps platform is therefore a delivery system. It owns feature computation, orchestration, training, artifact versioning, deployment and drift monitoring. It is measured in cycle time and reliability: how long from a data change to a validated model in production, and how often that path breaks. Every component in that path has its own trade-offs, which is why the useful comparisons happen per layer rather than per vendor logo. The layer-by-layer view is in our enterprise MLOps platform comparison, and the feature layer specifically in the feature store comparison.
The important thing for a buying decision: MLOps platforms produce the artifacts ModelOps consumes. Lineage, version history, approval state and monitoring output are exactly what a governance layer needs. A platform that treats those as first-class objects makes ModelOps cheap later. A platform where lineage depends on every team remembering to instrument it makes ModelOps a permanent tax.
AIOps: the label that is not about your models
AIOps is applied machine learning for IT operations: anomaly detection on metrics, log correlation, alert deduplication, incident triage. It is a genuinely useful category and it has nothing to do with governing or deploying your own models. It appears in MLOps searches purely because the acronyms rhyme.
The one legitimate intersection is that a mature ML platform generates operational telemetry, and that telemetry can be fed to an AIOps tool like any other service’s. If a vendor pitches AIOps as a substitute for model monitoring, that is a category error worth catching in the first meeting. Model monitoring watches distributions, drift and prediction quality; AIOps watches infrastructure behaviour.
LLMOps: the split that evaluation forced
LLMOps split off from MLOps because the parts that changed were the parts MLOps had settled. Training is often somebody else’s, the artifact is frequently a prompt and a retrieval index rather than weights, and the evaluation problem stopped being a metric on a holdout set and became a judgement about open-ended text.
What an LLMOps stack adds on top of MLOps is specific: prompt and configuration versioning, trace collection across multi-step chains and tool calls, evaluation datasets with human or model graders, and output guardrails. What it inherits unchanged is deployment, autoscaling, cost control and access management. That is why the pragmatic pattern is one platform plus an evaluation and tracing layer rather than two platforms. Where CI-style evaluation falls short for both worlds is covered in evaluation pipeline design, and our sister publication LLMOps Report goes deeper on the GenAI-specific operational surface.
What “ML platform” means when an engineer says it
The plain phrase ML platform usually means something narrower than any analyst category: the internal system a company built so that data scientists do not each invent their own infrastructure. It is an internal product with internal users, and its success metric is adoption.
That framing matters during vendor selection because an internal ML platform is rarely a single purchased product. It is typically one primary managed platform, one or two open-source components filling gaps the managed platform handles poorly, and a thin internal layer that hides the seams. Teams evaluating replacements for a single vendor component, rather than the whole stack, will find the component-level view in SageMaker alternatives compared.
At a glance: where the categories overlap
| Product family | Strongest as | Weakest as |
|---|---|---|
| SageMaker, Vertex AI | MLOps delivery | Portfolio-level ModelOps inventory |
| Databricks with Unity Catalog | MLOps plus lineage-grade governance | Non-ML model coverage |
| Azure ML with Responsible AI tooling | MLOps plus audit evidence | Cross-vendor portfolio view |
| Dedicated governance suites | ModelOps inventory and controls | Actually shipping a model |
| MLflow, Kubeflow, Metaflow | MLOps components | Governance narrative for auditors |
| Eval and tracing tools | LLMOps | Classical tabular ML monitoring |
The gap in that table is the interesting part. No managed platform is strong at both portfolio governance across every model type and low-friction delivery. Organisations that need both either buy a governance layer on top of a delivery platform, or accept that their inventory lives in a spreadsheet with a quarterly reconciliation ritual. The second option is more common than vendors admit, and it fails first in regulated settings, which is why the regulated-industries comparison treats evidence generation as the ranking criterion rather than feature count.
Where this sits in the wider MLOps landscape
Searches for the “MLOps market” or the “MLOps landscape” are usually asking one of two things: which categories of tooling exist, or which products sit in each. The category answer is the table at the top of this page, crossed with one distinction that cuts across all four labels: whether you buy the capability as a managed suite or assemble it from components.
- Managed suites (SageMaker, Vertex AI, Databricks, Azure ML) cover most layers at uneven depth and bill you in licence and usage. The layer-by-layer scoring is in the enterprise MLOps platform comparison.
- Assembled stacks (MLflow, Kubeflow, Feast, Flyte and peers) let you pick the best component per layer and bill you in engineering and on-call time instead. The open-source MLOps platform stack maps which project covers which layer.
That managed-versus-assembled split, not a vendor market-share chart, is the decision the landscape actually forces on a buyer.
How to choose when the requirement says ModelOps
- Ask what the requirement is protecting against. If the answer is an audit finding or a model nobody owns, you need inventory and controls. If it is slow releases, you need delivery tooling with a governance-friendly registry.
- Count the non-ML models. If rules engines, scorecards and optimisation models are in scope, an MLOps-only platform cannot cover the requirement no matter how good it is.
- Check what evidence the platform emits automatically. Governance that depends on engineers filling in fields degrades within two quarters.
- Insist on portable artifacts. A registry whose contents move is what keeps the governance layer independent of the delivery platform.
- Weight the layers against your own constraints rather than a generic ranking. The MLOps Platform Selector scores platforms per capability axis and names the gap each one forces you to fill with a second tool.
FAQ
Is ModelOps just a rebranding of MLOps?
No. MLOps describes the engineering pipeline that builds and serves machine learning models. ModelOps describes governance over every operational model in the organisation, including rule-based, statistical and optimisation models that no ML pipeline ever touches. The two overlap where lineage, versioning and approval records live, which is why a strong registry serves both.
Do I need a separate ModelOps platform if I already run MLOps tooling?
Only if your model portfolio spans more than what the ML pipeline produces, or if an auditor needs evidence your platform does not emit automatically. Many organisations get to adequate governance by tightening registry discipline and lineage capture in the platform they already run, rather than buying a second console.
Is AIOps related to MLOps at all?
Only by acronym. AIOps applies machine learning to IT operations telemetry to detect anomalies and correlate incidents. It does not deploy, govern or monitor the models your data science team builds, and it is not a substitute for drift monitoring.
Is LLMOps a separate platform purchase?
Usually not. The deployment, scaling and access-control layers are the same ones an MLOps platform already provides. What genuinely differs is prompt versioning, tracing across multi-step calls, and evaluation with graders, so the common pattern is an evaluation and observability tool layered on the existing platform.
What does the MLOps market or landscape look like?
At the category level it is the four scopes in the table above (MLOps, ModelOps, AIOps, LLMOps) crossed with one buying decision: a managed suite versus an assembled open-source stack. Vendor-share figures move every quarter and rarely change a shortlist; the durable structure is which layers a category covers and whether you operate them or pay someone to. Start from the layers in MLOps platform explained, then compare the products that implement them in the enterprise MLOps platform comparison.
Which category should appear in an RFP?
Name the outcomes rather than the acronym. Asking for model inventory with owner and revalidation date, automatic lineage from data to endpoint, and drift monitoring with alert routing gets comparable answers from vendors in every category. Asking for a ModelOps platform gets four vendors describing four different products.
