FAQ
Common questions
Everything you need to know about instrumenting LLM apps with Puse.
- How is Puse different from Langfuse or LangSmith?
- Those tools optimize prompts and evals. Puse optimizes uptime — error rates, cost regressions, and pager-style Slack alerts. Think SRE for AI agents, not prompt engineering.
- Do you store prompts?
- No. Puse stores metadata only: model, tokens, latency, cost, error messages, and trace IDs. Your completions never land in our database.
- Does streaming work?
- Yes. The SDK wraps streaming responses and captures token usage as chunks arrive, so cost tracking stays accurate even on long agent runs.
- Can I self-host?
- Absolutely. Puse runs on a single VPS: Fastify ingest, Next.js dashboard, Postgres. See the self-host guide for Docker Compose instructions.
- Which providers are supported?
- OpenAI and Anthropic out of the box via one-line SDK wrappers. More providers are on the roadmap — reach out if you need one today.
- How do alerts work?
- Configure thresholds per project for error rate and cost spikes. When a regression is detected over a rolling window, Puse posts to your Slack channel with context.