As OpenAI models become deeply embedded in enterprise workflows, a key architectural concern is vendor concentration risk.

How should organizations design AI systems that:

• Maintain interoperability across multiple model providers

• Avoid lock-in at the API, fine-tuning, and orchestration layers

• Preserve evaluation consistency across different LLMs

• Manage governance, safety, and auditability in multi-model environments

• Control inference cost without degrading performance

Is the answer model abstraction layers, agent orchestration frameworks, open-weight fallbacks, or something else?

Looking for insights from those building production-scale AI systems.