Why We Built AiKey

Every team we talked to building on top of LLMs ran into the same wall: one provider was never enough. OpenAI for one workload, Anthropic for another, Groq when latency mattered, a local model for anything sensitive. Each one meant a new SDK, a new key to rotate, a new place cost could quietly get away from you.

None of that is a modeling problem. It's a plumbing problem — and plumbing problems are the ones worth solving once, centrally, instead of every team solving it again inside their own app.

One gateway, not one more SDK

AiKey sits between your application and every provider you use. Your code talks to one URL with one key. Behind that key, AiKey holds the real provider credentials, so swapping GPT-4 for Claude — or routing cheaper requests to a cheaper model automatically — never means touching a call site.

Cost and usage, not guesswork

Every request through AiKey is logged, priced, and traceable back to the project and key that made it. "Which team's calls are burning budget this month" stops being a Slack thread and starts being a query.

Guardrails before the request leaves

PII redaction happens before a request hits any provider's servers — not as an afterthought bolted onto responses, but as a step the request has to pass through on the way out.

What's next

This is v1. The gateway, the routing, the observability — all live today. If you're gluing together provider SDKs by hand, create an account and point your first request at AiKey instead.