Ultra AI

Every request goes to https://api.ultraai.app/v1, so the official OpenAI SDKs work unchanged. You name the provider and model inside the request instead of hard-coding one vendor. That single endpoint is why a provider swap takes minutes rather than a sprint.

Prompts live in the dashboard, not scattered across source files. You edit, version, and reference them by key, which keeps copy changes out of deploys. Teams that rewrite wording every day get the most from this.

Two modes cover most cases: exact caching for identical requests, similarity caching for prompts that mean the same thing. You set the similarity threshold and cache age per call. The company claims up to 10x savings on API costs, a figure that depends entirely on how repetitive your traffic is.

Every call lands in a log with latency, token counts, cost, and the route it took. Aggregate views show spend by model and error rates over time. That's the AI observability piece most homegrown gateways skip, and it's usually the reason teams keep the tool.

List several models in priority order and the gateway tries the next one when a call fails. Failover covers rate limits, timeouts, and provider errors. Your users get a slower answer instead of an error page.

Limits attach to a user ID you pass with each request, along with a request count and a time window. You can tighten one account without touching global settings. For freemium products, that's the difference between a bad day and a billing surprise.