Guide / Third-party AI APIs
What should you verify before using a third-party AI API?
An evidence checklist for teams assessing a third-party AI API endpoint.
Short answer
Verify the endpoint's protocol compatibility, repeated availability, latency range, identifier behavior, usage reporting, data-handling terms, and failure modes. Separate directly observed evidence from provider claims and from any experimental behavioral comparison.
Operational evidence
Confirm the endpoint works repeatedly under a controlled request, not only in a provider demonstration.
- Availability across repeated samples
- Response schema integrity
- Latency range and timeout behavior
- Model identifier and finish-reason consistency
- Usage reporting
Commercial and security context
Technical verification does not replace contract, privacy, residency, support, or pricing review. Those facts must come from the provider and your own procurement process.
Behavioral evidence
A controlled comparison can show that behavior is consistent with a reference under a named benchmark. It still cannot prove shared weights or provider intent. ModelTrust keeps this capability in research preview until a real production reference/candidate run passes review.
Decision rule
Adopt an endpoint only when the observed evidence, provider terms, and your own workload tests are all acceptable. Recheck after provider or model changes.
Start with directly observed evidence.
Check an endpoint, inspect the evidence format, and review the scientific limitations.