Model Lifecycle Management

Model lifecycle management is the disciplined control of an AI model from selection and onboarding through production operation, migration, replacement, and retirement. In production AI systems, choosing a strong model once is not enough. Providers change pricing, availability, model versions, security commitments, latency profiles, and terms of use. Internal requirements, regulation, and quality metrics also move over time. A robust lifecycle process records which model version is in use, which evaluations must pass before deployment, which cost and quality thresholds apply, how rollouts are staged, which fallback models are available, and what conditions trigger migration. It also includes model pinning, monitoring, regression tests, decision documentation, and a clear retirement plan. The discipline becomes especially important when teams route different task classes to different models. In that setup, it must remain clear which model handles which workload and how changes can be released without breaking quality, compliance, or cost controls. Model lifecycle management makes AI systems more stable, auditable, and less exposed to sudden provider decisions.