Currai is observability for LLM apps. It traces every prompt, token, and tool call your app makes so you can debug, measure, and ship with confidence — full traces, token usage, and cost in one view. Drop in the Python or TypeScript SDK and you get a hosted dashboard without running any infrastructure yourself.

About five minutes. Install the SDK with pip install currai or npm i currai, paste your public / secret key pair, and wrap a single LLM call. There's no agent to deploy and no collector to run — the first request you make shows up in the dashboard right away.

Currai ships first-class Python and TypeScript SDKs, and it's byte-compatible with the Langfuse SDKs and OpenTelemetry. If you're already instrumented, you migrate by changing a single host line — your existing trace code, spans, and exporters keep working.

No. Traces are batched and flushed in the background, so instrumentation never blocks a request. In short-lived processes you call flush() (or flush_async()) before exit to make sure everything is sent.

Usage is billed on the data Currai actually processes — measured in processed bytes — not on a flat per-trace fee. Large traces and tiny traces are priced for what they are, so your cost tracks real volume instead of row counts.

Retention follows your plan, so you keep traces for as long as your plan's window allows. You can export or delete your data on demand at any time.

None. Currai hosts ingestion, columnar storage, and dashboards for you — there's no ClickHouse to babysit. It scales with your traffic so you watch data, not infrastructure.