Model Catalogues and Registries
A model catalogue or model registry is a structured inventory of AI models, model versions, endpoints, owners, approved uses, limitations, release status, and related metadata. It helps organizations know which models exist, where they are used, and whether they are approved for a given integration.
Key takeaways
- A model catalogue helps people find and understand approved AI models.
- A model registry often tracks versions, artefacts, endpoints, metadata, and release status.
- Model inventories support ownership, review, audit trails, rollback, and retirement.
- Each model record should explain intended use, limitations, access rules, and status.
- Unknown or undocumented models are harder to govern, monitor, replace, or retire.
What is a model catalogue?
A model catalogue is a searchable or organized list of models that an organization knows about. It may include model names, descriptions, owners, approved use cases, risk notes, data rules, platform location, and current status.
In plain terms, a model catalogue answers questions such as: What models do we use? Who owns them? What are they for? Which applications depend on them? Are they approved, experimental, deprecated, or retired?
What is a model registry?
A model registry is often more technical than a catalogue. It may track model artefacts, versions, release stages, training or configuration metadata, serving endpoints, approval records, and deployment history. In some organizations, the catalogue and registry are separate. In others, they are part of the same platform.
The exact tool matters less than the function: the organization needs a reliable way to know which model version is active, where it is deployed, what changed, and how to roll back or retire it.
Model catalogue vs model registry
The words are sometimes used loosely. For integration planning, it helps to separate the business inventory view from the technical version-and-deployment view.
| Concept | Plain meaning | Typical audience |
|---|---|---|
| Model catalogue | A readable inventory of known models, owners, intended uses, limits, and status. | Product owners, integration teams, governance reviewers, security, compliance, operations. |
| Model registry | A technical record of model artefacts, versions, metadata, deployment stages, and endpoints. | Data science, engineering, platform, MLOps, AI operations, release teams. |
| Model card or profile | A structured description of a model’s use, limitations, inputs, outputs, and risks. | Technical and non-technical reviewers who need model context. |
| Release record | A record of what changed, who approved it, where it was deployed, and how to roll back. | Operations, engineering, audit, incident response, product owners. |
Why model inventories matter for AI integration
AI integrations can become hard to manage when models are added one project at a time. One team uses a vendor API. Another hosts a private model. Another tests a new version. Another embeds a model inside a workflow tool. Without an inventory, the organization may not know what it is relying on.
A model catalogue or registry helps with:
- Finding approved models for new integrations.
- Knowing who owns each model or route.
- Tracking which systems depend on which model versions.
- Separating approved models from experiments.
- Recording intended uses and known limitations.
- Supporting access control and release approval.
- Helping incident responders identify affected systems.
- Retiring old, unsupported, or risky models.
What a useful model record may include
A model record should be useful to both technical and operational reviewers. It should avoid vague descriptions like “AI model for support” when the model affects real workflows.
| Model record field | What it explains | Why it matters |
|---|---|---|
| Model name and ID | How the model is identified in systems and documentation. | Prevents confusion between similar models or versions. |
| Owner | Person, team, or function responsible for the model. | Gives questions, incidents, and changes a clear destination. |
| Approved use | What the model is allowed to support. | Prevents casual reuse in unsuitable workflows. |
| Not approved for | Known uses that are blocked or require separate review. | Helps prevent scope creep. |
| Version and release status | Which version is active and whether it is test, approved, deprecated, or retired. | Supports release control and rollback. |
| Data rules | What data types the model may or may not process. | Supports privacy, security, and compliance review. |
| Monitoring signals | Usage, latency, errors, cost, review outcomes, and known issues. | Shows whether the model is operating acceptably. |
| Retirement plan | How the model will be replaced, disabled, or archived. | Prevents abandoned models from staying in production quietly. |
Model status labels
Status labels help people understand whether a model is safe to use for a particular integration. The exact wording can vary, but the labels should be clear.
| Status | Plain meaning | Integration implication |
|---|---|---|
| Proposed | A model is being considered but has not been reviewed. | Do not use in production. |
| Experimental | A model is being tested in a controlled setting. | Use only in approved test environments. |
| Approved | A model is approved for defined use cases. | Use only within its approved scope. |
| Restricted | A model is allowed only for certain roles, data, systems, or workflows. | Requires stronger access and review controls. |
| Deprecated | A model should be replaced and not used for new work. | Plan migration and avoid new dependencies. |
| Retired | A model should no longer be used. | Disable routes and remove active dependencies. |
| Blocked | A model is not allowed for the organization or use case. | Prevent access and document the reason. |
Track where models are used
A model inventory is much more useful when it tracks dependencies. A model may serve several applications, workflows, dashboards, agents, document tools, or customer-facing features. If a model changes or fails, teams need to know what may be affected.
Useful dependency records include:
- Applications that call the model.
- Gateways, endpoints, or routes that use it.
- Workflows, agents, or tools that depend on it.
- Data sources or retrieval layers connected to it.
- Customer-facing or internal uses.
- Service accounts and credentials involved.
- Approval gates or human-review steps tied to the model.
- Fallback route or replacement model.
Model versions and configuration versions
AI behaviour can change when the model changes, but it can also change when prompts, retrieval settings, routes, tools, temperature settings, output formats, or context sources change. A useful registry does not track only the model name.
Version records may include:
- Model version or provider version.
- Prompt version.
- System instruction version.
- Retrieval or RAG configuration version.
- Tool definitions and connector versions.
- Gateway route version.
- Evaluation or test results.
- Release date, approver, and rollback target.
Approval and review records
Model catalogues and registries can also support approval records. This matters when models are used in customer-facing, regulated, sensitive, high-volume, or operational settings.
Approval records may show:
- Who requested model approval.
- Who reviewed the model.
- Which use cases were approved.
- Which data types are allowed or excluded.
- What testing or evaluation was completed.
- What limitations or cautions were recorded.
- What monitoring is required after release.
- When approval should be reviewed again.
Deprecation and retirement
Models should not remain active forever by accident. A model may become outdated, unsupported, expensive, unreliable, unsuitable, replaced, or no longer approved for a particular use.
A retirement plan should consider:
- Which applications depend on the model.
- Which routes or endpoints need to change.
- Which data sources or prompts are tied to it.
- Which users or teams need notice.
- Whether a replacement model has been tested.
- How to roll back if the replacement performs poorly.
- When old credentials, routes, or endpoints should be disabled.
- How historical logs and records should be preserved.
Small-business approach
A small business does not need a complex model registry to benefit from model inventory thinking. A simple internal list can still prevent confusion, forgotten keys, unknown model dependencies, and surprise cost or quality problems.
A practical small-business model inventory may list:
- Which AI tools, APIs, models, or vendors are used.
- Which website, workflow, plugin, or application uses each one.
- What each AI feature is supposed to do.
- Whether the feature is internal, public, or customer-facing.
- Where the API key or connector is managed.
- Who can disable the feature.
- Approximate monthly cost or usage.
- Any output that must be reviewed before use.
Common catalogue and registry mistakes
Model inventories are only useful if they are accurate enough to support real decisions.
| Mistake | Why it is risky | Better habit |
|---|---|---|
| Tracking only model names. | Names alone do not show versions, owners, uses, limits, or dependencies. | Track ownership, use case, status, version, and dependencies. |
| No owner listed. | No one is clearly responsible for questions, incidents, or changes. | Assign a model owner or responsible team. |
| Experimental models mixed with approved models. | Teams may use test models in production by mistake. | Use clear status labels and environment separation. |
| No dependency tracking. | Changing a model can break unknown applications or workflows. | Record where each model is used. |
| No retirement process. | Old models remain callable long after they should be replaced. | Use deprecated and retired status with migration plans. |
| No review cycle. | Model records become stale as integrations evolve. | Review important model records periodically or after major changes. |
Model catalogue and registry checklist
Use this checklist before treating a model inventory as useful for production AI integration.
| Area | Question | Good signal |
|---|---|---|
| Identity | Can the model be clearly identified? | Name, ID, provider, endpoint, and version are recorded. |
| Ownership | Who is responsible for the model? | A person, team, or function is listed. |
| Use case | What is the model approved to do? | Approved and excluded uses are documented. |
| Status | Is the model proposed, experimental, approved, deprecated, retired, or blocked? | Status is clear and kept current. |
| Dependencies | Which systems rely on the model? | Applications, workflows, routes, agents, and tools are linked where practical. |
| Versions | Can behaviour changes be traced? | Model, prompt, route, retrieval, and configuration versions are recorded as appropriate. |
| Review | Has the model been evaluated for its use case? | Testing, approval, limitations, monitoring needs, and review dates are recorded. |
| Retirement | Can the model be replaced or disabled? | Migration, fallback, rollback, and retirement paths are known. |
Where to go next
After model catalogues and registries, the next step is versioning, rollback, and release controls: how to change models and configurations without losing control of production behaviour.
Versioning, Rollback, and Release Controls
Learn how model changes should be tested, approved, released, monitored, and reversed.
Model Drift and Data Drift
Understand why model behaviour and input patterns may change over time.
Audit Trails for AI Integrations
See how model identity, source retrieval, outputs, approvals, and changes can be reviewed later.
Compliance Evidence for AI-Integrated Systems
Learn how model records and approval history support governed AI use.
Educational limitation
This article provides general educational information. It is not legal, financial, medical, engineering, safety, cybersecurity, procurement, compliance, privacy, tax, accounting, or professional advice. It does not provide instructions for bypassing controls, exploiting systems, unauthorized access, or unsafe automation. Use qualified review before relying on model catalogues or registries for sensitive data, regulated systems, production infrastructure, customer records, financial processes, safety systems, connected devices, or other high-consequence environments.