An OpenAI-compatible API may reduce transport changes, but it covers only part of the problem.
What makes a system portable?
Portable systems separate business logic from provider calls and keep a representative evaluation set. A provider change can then be measured against stable application behavior. Prompts, tool schemas, output validation, observability, and fallback behavior still need adaptation because model capabilities differ.
Portability means a controlled migration path, not a zero-effort model swap.
When is portability worth the cost?
It matters when pricing, regulation, regions, resilience, or capability may force a future change. Invest in the specific seams likely to change rather than building a universal abstraction. An LLM gateway can centralize provider access, while portable evaluation and data reduce AI vendor lock-in. A multi-provider AI system is optional, not a prerequisite.
How can portability be tested?
Choose one representative workflow and run it against a second provider without changing business logic or evaluation criteria. Record every required code, prompt, schema, data, and operational change. That exercise reveals the real switching boundary and its cost. Portability is a demonstrated migration path, not an interface diagram. Repeat the test when the application adopts provider-specific tools or output formats, and keep the shared behavior covered by an AI evaluation harness rather than trying to hide meaningful model differences.
ONNX documents an open model format designed to represent models across tools, frameworks, and runtimes.