What to check before you adopt one
Start with compatibility. A good relay should accept standard OpenAI-style endpoints, headers, and JSON payloads so your apps, scripts, and tools do not need custom patches. Then verify whether the service gives you transparent request logs, rate-limit behavior, and model mapping that is easy to understand. If you plan to use Claude Code, test the exact workflow you care about: editor actions, shell calls, retries, and long prompts.
Billing matters too. Teams often prefer 按量付费 because it is easier to match usage with actual experiments and avoid paying for capacity that sits idle. If you are evaluating a relay for production use, ask how it handles outages, whether it supports fallback routing, and how quickly you can trace a failed response back to the upstream model.
Smoke-test steps
- Set the base URL in your client to the relay endpoint.
- Send one tiny completion request with a known prompt.
- Check that the response format matches your existing SDK expectations.
- Repeat with a streaming request and confirm token chunks arrive in order.
- Test a longer prompt to see whether timeouts or truncation appear.
- Inspect logs for latency, error codes, and upstream model labels.
Config example
After that, run your normal SDK call exactly as before. If the relay is well-designed, your application should not need new logic beyond the base URL and key. This is especially useful when you want to keep a consistent client while comparing different models or operational paths through one OpenAI-compatible relay.