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How to evaluate an AI API provider

A checklist for choosing an LLM API provider: uptime, latency, pricing transparency, model catalog, and what to test before you commit.

How to evaluate an AI API provider

Switching providers later is painful, so evaluate up front. Beyond the obvious — does it serve the model you want — these are the checks that separate a production-grade provider from a demo.

Uptime and latency

Look for a public status page with real uptime and latency history, not just a green badge. Run a burst of requests from your own region and measure p95 latency during your peak hours.

Pricing transparency

Per-1M-token pricing should be clear and stable, with no surprise markup on cache misses or hidden minimums. Compare input and output prices separately — output is usually the larger share of cost.

Model catalog and variants

A good provider exposes the same model at different quality/cost tiers, so you can route simple tasks to a cheap variant and hard ones to a flagship. Confirm you can switch with a single parameter.

Compatibility

OpenAI-compatible endpoints mean you keep your existing SDK and tooling. Verify streaming, function calling and embeddings all work before you depend on them.

Support and billing

Check how you can reach support, and how you can pay — a card option plus crypto covers most teams. Try a small top-up and a refund scenario before going all in.

GetVibe API covers these bases: one OpenAI-compatible endpoint, live USD pricing, and a public status page. Create a key and run your own evaluation in minutes.

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How to evaluate an AI API provider | GetVibe API