Checklist: what matters in an AI API relay
- Compatibility first: confirm the endpoint works with the same request shape your SDK expects. For OpenAI-style clients, the base URL should behave predictably and return standard errors.
- Clear routing behavior: a good relay explains whether it proxies Claude, OpenAI, or both. Ambiguous routing makes debugging harder, especially when switching models.
- Stable configuration: look for a documented base URL such as OPENAI_BASE_URL and, when needed, ANTHROPIC_BASE_URL so environment setup is explicit.
- Simple retry logic: transient failures happen. You want a relay that handles backoff sensibly rather than masking every issue as a timeout.
- Latency transparency: measure response time from your region, not just a dashboard claim. A relay that is fast in one test may slow down under real traffic.
- Error readability: useful error messages save hours. Look for codes and messages that point to auth, quota, model name, or upstream availability.
Smoke-test steps
- Set your environment variables and keep the test script small.
- Send one minimal chat request and one basic Claude request if both are supported.
- Check whether the reply format matches your client library without manual parsing hacks.
- Repeat the test after a few minutes to confirm the route is not flaky.
export OPENAI_BASE_URL=https://59api.com/v1
export OPENAI_API_KEY=your_key_here
export ANTHROPIC_BASE_URL=#/v1
# then run a minimal request from your SDK or curl test
# verify: 200 status, valid JSON, expected model response
For a quick proof of compatibility, start with one model call, confirm the schema, then move to a second request with the same client. If both pass, you have a stronger signal than any landing-page promise.