Most evaluations between AI API providers do not fail on quality. They stall. A few engineers try a handful of prompts, everyone forms a different impression, nobody wrote down what "good" meant, and the thread dies without a decision. The fix is not a better benchmark. It is a short intake you fill in before you start.

Here are the five things worth having on one page before you compare anything.

1. The use case and where it runs. Name the one workflow you want to move or start, and where it lives in your product. "Summarise a support ticket" and "transcribe a 45-minute call" are different evaluations with different endpoints. For a chat workflow that means POST /v1/chat/completions; for audio it means POST /v1/audio/transcriptions. Write the workflow down before you write a prompt.

2. How you call a model today. Are you on the OpenAI SDK, raw HTTP, or a framework that wraps one of them? If your client already speaks the OpenAI API, an OpenAI-compatible base URL like https://webwright.ai/v1 is a base-URL and model-name change, not a rewrite. If it does not, you are evaluating a migration, not just a provider, and the estimate is larger. Knowing which one you are in changes the whole plan.

3. Who owns the provider decision. A name, not a team. Evaluations that end in "we should ask the platform team" restart from zero. The person who can say yes should be in the first conversation or named in the intake.

4. The next step you actually want. "Evaluate" is not a next step. "Run our ten hardest support tickets through both endpoints and compare the summaries" is. Decide the artifact that ends the evaluation: a side-by-side on your own data, a cost estimate at your real volume, or a yes/no to a paid pilot.

5. A rough volume or spend signal. You do not need an exact forecast. You need the order of magnitude: requests per day, tokens per request, or audio minutes per month. That single number is what turns a feature comparison into a cost conversation, and it is the number a provider will ask for first.

A worked example

A six-person team runs a meeting-notes product. Their intake reads:

  • Use case: transcribe uploaded calls, then summarise and extract action items.
  • Today: OpenAI Python SDK, two calls per meeting, one transcription and one chat.
  • Decision owner: the CTO, who also writes the integration code.
  • Next step: run twenty of their own calls through a second endpoint and compare summaries.
  • Volume: about 4,000 meeting-minutes per month, growing.

That page is enough to start. The evaluation now has an owner, an endpoint list, a test set, and a number.

Run it yourself first

If you want to evaluate without talking to anyone, you can. Read https://webwright.ai/v1, point your existing OpenAI client at the base URL, and run one request against your own key. Measure on your own audio or your own prompts, not on someone else's benchmark. Self-serving is a fine outcome, and the migration playbook and the cost calculator on the site are there for exactly that.

When a working session is worth it

A working session earns its place when the decision is genuinely open: the workflow spans more than one endpoint, the cost curve matters at your volume, or you need a second opinion on the architecture before you commit engineering time. Bring the intake above and the session starts where a cold call would take thirty minutes to reach.

The WebWright Team