[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"glossary-model-deprecation::en":3,"gloss-cluster-model-deprecation::en":23,"gloss-next-model-deprecation::en":9},{"slug":4,"category":5,"name":6,"definition":7,"meta_desc":8,"faq":9,"schema_markup":9,"related":10},"model-deprecation","mlops","Model Deprecation","Model deprecation is a provider retiring a specific model version, after which requests naming it either fail or are silently served by a successor. It is the single most underrated operational risk in building on a hosted model, because it is the one dependency in your stack that can change underneath you without a deploy on your side. Frontier providers ship new versions on a cadence measured in months and retire old ones on a schedule measured in quarters, so any feature you shipped against a named snapshot has an expiry date whether or not anyone wrote it down. Two failure modes matter. The loud one is the retirement date arriving and the endpoint returning an error, which is disruptive but obvious and easy to catch in staging. The quiet one is worse: a provider aliases a general model name to a newer snapshot, so the same request now reaches a different model with different behaviour. Your prompts still work, your evaluations still pass at the aggregate level, and the specific formatting or edge-case handling you tuned around drifts without anything failing. This is why teams pin dated model identifiers in production rather than the floating alias, and why an evaluation set you can rerun on demand is worth more than a one-time quality review. What to ask a vendor before you depend on them: what notice period is committed for deprecating a version, is that commitment in the contract or only in a blog post, how long do dated snapshots stay available after a successor ships, and what happens to a fine-tuned model when its base model retires — the usual answer is that the fine-tune retires with it and must be retrained, which turns a vendor's calendar into your engineering backlog.","Model deprecation is a provider retiring a model version. The loud failure is an error; the quiet one is an alias silently pointing at a different model.",null,[11,14,17,20],{"slug":12,"name":13},"fallback-model","Fallback Model",{"slug":15,"name":16},"fine-tuning","Fine-Tuning",{"slug":18,"name":19},"model-card","Model Card",{"slug":21,"name":22},"switching-cost","Switching Cost",[24,28,32,35,38,42,45,48,51,54,57,60],{"slug":25,"category":5,"name":26,"updated_at":27},"annotation-guidelines","Annotation Guidelines","2026-08-24T03:30:02+00:00",{"slug":29,"category":5,"name":30,"updated_at":31},"baseline-model","Baseline Model","2026-08-24T02:46:38+00:00",{"slug":33,"category":5,"name":34,"updated_at":31},"batch-inference","Batch Inference",{"slug":36,"category":5,"name":37,"updated_at":31},"canary-prompt","Canary Prompt",{"slug":39,"category":5,"name":40,"updated_at":41},"champion-challenger","Champion-Challenger (A\u002FB Model Testing)","2026-08-24T02:46:37+00:00",{"slug":43,"category":5,"name":44,"updated_at":31},"class-imbalance","Class Imbalance",{"slug":46,"category":5,"name":47,"updated_at":31},"continuous-batching","Continuous Batching",{"slug":49,"category":5,"name":50,"updated_at":31},"cross-validation","Cross-Validation",{"slug":52,"category":5,"name":53,"updated_at":31},"data-labeling","Data Labeling",{"slug":55,"category":5,"name":56,"updated_at":41},"drift-detection","Drift Detection",{"slug":58,"category":5,"name":59,"updated_at":41},"eval-harness","Eval Harness",{"slug":61,"category":5,"name":62,"updated_at":41},"experiment-tracking","Experiment Tracking"]