Model Registry Comparison: Tracking What Is Actually Deployed

A registry answers what is deployed, where it came from, and who approved it. This guide covers comparing them, drawing on FISTA Solutions' AI enablement governance work.

What should a registry record?

Six things, only one of which is the artefact.

Record	Why it is needed	Frequently missing
Version deployed where	The primary question	Rarely
Lineage: data, code, config	Reproducibility	Often
Evaluation results	Whether it was tested	Often
Approval and approver	Accountability	Often
Hosted model versions	Most of what runs	Usually
Prompts and retrieval config	Behaviour determinants	Usually

Why does lineage matter more than storage?

Because an artefact without provenance cannot be reproduced or diagnosed.

Knowing which dataset, which code version, and which parameters produced a model is what lets you rebuild it, explain it, and investigate a problem with it.

Storage is easy. Capturing lineage automatically, so it is recorded rather than remembered, is the harder and more valuable capability. See AI model change log template.

What makes approval gating work?

Enforcement in the deployment path.

A registry where a model can only be promoted to production with a recorded evaluation result and a named approval is a control. One where approval is a field somebody fills in afterwards is documentation.

Check how the registry integrates with your deployment pipeline, since that is where enforcement lives. See AI release checklist.

Why register hosted models?

Because they are what most organisations actually run.

A registry designed around models you train has nothing to say about a system calling a hosted frontier model. Yet the same questions apply: which version, in which system, approved by whom, evaluated against what.

Registering hosted model versions alongside any models you train gives one answer to the governance question rather than two partial ones. See AI service catalog template.

What else determines behaviour?

Prompts, retrieval configuration, and tool definitions.

For a system built on a hosted model, those three determine behaviour far more than the model version does. A registry tracking only the model tells you little about what the system actually does.

Registering the full configuration — model version, prompt version, retrieval config, tool set — is what makes a deployment reproducible. See prompt management tools comparison.

How does it support incidents?

By answering what changed and when.

During an investigation, the question is which configuration was running when the problem started. A registry with deployment history answers it in minutes.

That requires the registry to record deployments rather than only artefacts, and to be reachable when your primary systems are degraded. See AI rollback checklist.

What makes a registry unused?

Being separate from the workflow.

A registry requiring manual entry after deployment gets skipped under pressure and becomes stale, at which point nobody trusts it and everybody stops consulting it.

Registration should be a by-product of deploying rather than an additional step. That integration is the difference between a control and a database.

How do you run your own comparison?

Try to answer, using each candidate, what configuration produced a specific output last month. Whether you can is the test.

Then check whether it can register a hosted model version and an associated prompt, since that is most of what modern systems run.

What does switching cost later?

Moderate. Lineage records are historical data worth keeping, so export matters. Integration with pipelines is usually a thin layer.

Record the same information in your change log as well, so the registry is not the only copy.

What do people get wrong here?

Tracking only trained models. Lineage entered manually. Approval as a field rather than a gate. Prompts and retrieval config omitted. And a registry separate from the deployment path.

Do you need one if you only use hosted models?

You need the record, though it may not need a dedicated product. A change log plus a service catalog entry covers the same ground for many organisations.

The question is whether you can answer what configuration was running when, and by what approval. If a simpler arrangement answers it, that is sufficient. See AI model change log template.

Which should you choose?

Compare on lineage capture, approval gating, and whether hosted model versions and prompts can be registered. If you only use hosted models, a change log and service catalog may cover the requirement without a dedicated registry.

What should you do first?

Try to determine which model and prompt version produced an output from last month. If you cannot, that is the gap a registry or change log should fill.

How FISTA Solutions helps

FISTA Solutions builds and operates production AI systems through AI agents, AI enablement, and forward deployed engineering: full deployed configuration recorded including hosted model and prompt versions, with registration a by-product of deployment rather than a manual step, decisions documented with their reasoning, and handover that leaves your team able to maintain what was delivered. The record is 150+ projects for 50+ companies across 12+ countries.

To run this comparison against your own workload, message FISTA on WhatsApp, or read AI model change log template.