Most developers who go looking for an OpenAI API alternative already have working code. The client works, the requests are shaped, and the real question is not how to integrate a new provider but whether a specific one will behave like the one you have. Here is the short version of how to find out.
Start with the surface, not the demo. Ask one question
first: does the candidate speak the same API you already call? If your
app only sends chat completions, an OpenAI-compatible endpoint usually
means a base URL and a key change, and your existing client keeps
working. WebWright serves an OpenAI-compatible endpoint at https://webwright.ai/v1 with the documented model wren-r1, so a Cline or OpenCode setup points at it with
those two changes.
Run one real workload, not a hello world. A one-line "hello" tells you the endpoint is up. It does not tell you whether your actual prompt, your token volume, or your response parsing survives. Take one request from production, run it against the candidate, and compare the response object field by field. If you stream, test streaming. If you parse a usage object, check that it is there.
Decide the failure path before you move traffic. An alternative provider is only useful if you have already decided when to use it. Do you send a fraction of traffic, run it in shadow, or keep it as a fallback? Each choice needs a retry policy and a way to notice when the fallback ran. This is the step a demo skips and production punishes.
Keep the first move small. The lowest-risk way to evaluate an alternative is to send one non-critical workload to it, watch it for a week, and keep the switch reversible. That is enough evidence to decide, and it costs less than a migration.
Work through those four and you have a decision with evidence rather than a feature list. If one area stays open, that is usually the thing worth a short working session.