LLM Model Migration Checklist Template

It depends on the number of integration points and prompt complexity. A product with 1-2 simple prompts can migrate in under a week. A product with 10+ prompts, custom function calling, and strict quality requirements typically takes 2-4 weeks including shadow and canary phases. Do not compress the rollout timeline. Quality issues that slip through canary are expensive to fix post-launch.

Not necessarily. Different use cases may be better served by different models. A classification task might perform well on a small, fast model, while a generation task needs a larger one. The [AI Build vs Buy Framework](/frameworks/ai-build-vs-buy) can help you evaluate whether a multi-model architecture makes sense for your product.

Shadow mode roughly doubles your LLM API costs for the duration. Budget for 1-2 weeks of dual-run costs. For high-volume products, you can shadow a random sample (e.g., 10% of requests) instead of all traffic to reduce costs while still catching systematic issues.

This is common. You have three options: adapt prompts specifically for the failing edge cases, keep the current model for those specific use cases while migrating everything else, or accept the regression if the edge cases are rare enough that the overall quality improvement justifies it. Document the decision either way.

Point to production incidents from rushed LLM migrations. Every team that has swapped models without structured testing has a story about broken output parsing, unexpected content policy blocks, or quality regressions that took weeks to diagnose. The cost of a 3-week structured migration is always less than the cost of a production fire drill. The [AI PM Handbook](/ai-guide) covers how to build the business case for responsible AI operations.