Langfuse is strongest as open-source LLM observability for traces, prompts, evaluations, and product analytics. Latten is different: it maps company AI by actor, cost, data reach, and PII exposure, observe-only, so teams can see the blast radius of agents and copilots before they decide what to bound.
See it on a live graph — no signup →Traditional LLM observability starts with a trace. Latten starts with the actor: who acted, on whose behalf, which model ran, what it cost, and which data domain it reached.
A prompt trace can tell you what happened in a request. Latten connects that request to authority: the service account, the data domain, the PII types, and the spend with an owner.
Choose Langfuse for prompt analytics, evals, and trace-level LLM product iteration. Choose Latten when the question is what your company AI can reach and what that reach costs.
Not usually. They answer different questions: trace/eval observability versus actor-level cost and reach governance.
Yes. Keep trace analytics where they help product teams, and use Latten for cost, reach, PII exposure, and blast-radius visibility.
No. Latten is values-free: PII is counted locally and reported as types and counts, never raw values.