Keywords AI

Send every request to one endpoint and reach models across every major provider, switching between them by changing a single word in the model name. Built-in caching serves repeat requests instantly, and per-key budgets or rate limits block runaway spend before it lands on your bill.

You list the models to fall back to, and the gateway moves to the next the moment one errors or rate-limits. On the company's own dashboard the fallback chain holds near 99.99% uptime where a single provider sits far lower, which matters when an outage would otherwise take your app down.

Every LLM call, tool run, retrieval and agent turn becomes a span inside one trace. Each span carries the model, latency, cost, token count and the exact input and output, so you can see precisely where a run's time and money go instead of reading a wall of log lines.

Requests, errors, cost, latency and tokens sit on one dashboard, sliced by model, key or user. Watch any metric over any window and get an alert through Slack, email or a webhook the moment it crosses a threshold you set. Error tracking groups faults and shows the span behind each one.

An evaluator composes an LLM judge, a deterministic code check or a human reviewer into a single score for any output. Test cases can be pulled straight from production logs by filter and sampling rate, or uploaded as a CSV, so every case is one your users actually sent. You can also run the same evaluator on live traffic to catch regressions in real time.

Prompts live in a collaborative editor with version history, one-click deployment and release management. A model playground and Jinja templates let you test changes before they ship, and private prompts stay out of teammates' view when a workspace is shared.