2 posts tagged with "model-migration"

Every team that has been running LLM-powered features for more than six months has faced the same moment: a better model drops, the current provider raises prices, or the model you depend on gets deprecated with 90 days' notice. You need to swap the foundation model underneath a running production system. Most teams treat this as a configuration change — update the model ID, re-run the eval suite, ship it. Then they spend the next two weeks firefighting regressions that the evals never caught.

The model migration problem is fundamentally different from traditional software upgrades. When you swap a database version, the query semantics are preserved. When you swap a foundation model, everything changes: output distributions shift, edge-case behaviors diverge, and downstream systems that learned to depend on specific model quirks silently break. The failure modes are distributional, not binary, which means they hide in the long tail where your eval suite has the least coverage.

Every production LLM system will face a model migration. The provider releases a new version. Your costs need to drop. A competitor offers better latency. Regulatory requirements demand a different vendor. The question is never if you'll swap models — it's whether you'll do it safely or learn the hard way that "just run the eval suite" leaves a crater-sized gap between staging confidence and production reality.

Most teams treat model migration like a library upgrade: swap the dependency, run the tests, ship it. This works for deterministic software. It fails catastrophically for probabilistic systems where the same input can produce semantically different outputs across model versions, and where your prompt was implicitly tuned to the behavioral quirks of the model you're replacing.