AI Model Migration: A Regression Checklist Before Switching Providers or Versions
Compare AI models on a fixed task baseline covering citations, structured outputs, tool behaviour, latency, cost and rollout recovery.
Treat an AI model change as an application change. Compare task quality, tool behaviour, structured outputs, latency and operating cost against a fixed baseline before switching traffic. A newer model or a better general benchmark does not establish that your existing workflows will improve.
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Freeze a reproducible baseline
Record model identifiers, prompts, generation settings, tool schemas and retrieval configuration. Preserve an authorised evaluation dataset with expected outcomes and reviewer labels. Without those details, a comparison can accidentally change several variables at once.
Include successful cases, known weaknesses and cases where the correct response is uncertainty or refusal. Avoid evaluating only examples chosen to demonstrate the new model's strengths.
Use a migration comparison sheet
| Dimension | Question | Acceptance evidence |
|---|---|---|
| Task result | Does the response solve the business task? | Reviewed outcomes on the same cases |
| Evidence | Are source claims supported? | Citation and qualification checks |
| Structure | Can the application parse the output? | Valid schema and error handling |
| Tools | Are actions appropriate and authorised? | Tool traces and boundary cases |
| Runtime | Does it meet the workflow's timing needs? | Representative latency distribution |
| Cost | What is the cost per accepted result? | Retries and review effort included |
| Operations | Can traffic be controlled and reversed? | Rollout and recovery rehearsal |
Choose release gates from the actual task. Do not offset a serious access-control failure with a higher average writing-quality score.
Inspect differences rather than only averages
For a hypothetical document assistant, the new model may improve summaries but omit conditions in a narrow class of policies. Review disagreement cases and break results down by document type, language and task.
Document changes needed in prompts or schemas, then rerun the relevant baseline. If both the prompt and model change, report that honestly rather than attributing all improvement to the model alone.
Roll out with accountable monitoring
Use an appropriate limited rollout, compare real authorised outcomes and retain a recovery plan. Ensure the prior version remains usable for the intended recovery period where the provider supports it; do not promise rollback to a retired external service.
Review logs and retention arrangements for the new provider. Equivalent API shapes do not establish equivalent data handling or commercial terms.
Model migration questions
Can we switch because the API is compatible?
Compatibility helps integration, but output behaviour and operating responsibilities still need evaluation.
Is a small sample enough?
It can expose obvious issues. Select sample size and coverage according to task variation and consequence, and state the remaining uncertainty.
Use the AI evaluation dataset guide and cost controls guide, then review the migration with Drema's AI team.