Architecting a Multi-Model AI Creative Pipeline Without Model Lock-In

Most AI workflow discussions focus on prompting. That is the wrong abstraction layer. In production environments, the failure surface is architectural: regeneration volatility cost unpredictability category mismatch pricing exposure visual drift across campaigns The problem is not output quality. It is system stability. The Single-Model Failure Pattern Single-model dependency fails in three predictable ways. Capability Ceiling Every model specializes. Cinematic-tier generators such as those compared in this long-form Kling vs Sora breakdown demonstrate strong temporal coherence, but that same tier may not optimize for rapid iteration economics. Speed-optimized models win throughput benchmarks, but break down in multi-character motion sequences. Without routing across categories, teams compensate with regeneration brute force. Which increases multiplier cost. This architectural mismatch is visible when you look at multi-category model ecosystems instead of isolated tools. A consolidated