Ship AI-native. Stay debuggable.
You are building AI-native. Your traces are your customer support, your QA, and your roadmap signal. Trefur gives you all three on day one, for free, in five minutes.
What you are trying to do.
- →Build the agent, ship it, and find out within an hour when it starts failing.
- →Keep model spend visible while you iterate, before the burn rate becomes a board topic.
- →Hand new hires a tool that explains what your agent is doing without a tribal walkthrough.
- →Make the next investor demo run on real production data, not a Loom video.
- →Pick observability today that scales when you raise the next round.
What hurts today.
You shipped fast and instrumented nothing
The agent works on demo day. A week later, it does something weird in front of a customer and you cannot reconstruct what happened. The screenshot is all you have.
Your cost graph is the OpenAI dashboard
You see the daily spend. You cannot see which agent, which prompt, which user. You add caching. The graph stays the same.
Every new engineer asks the same question
What does this agent actually do? Without a trace they read code, dig through logs, and form their own theory. Three different theories per team.
The first outage is the worst day of the quarter
No traces. No dashboards. Just a Slack channel of customers and one engineer typing.
What Trefur gives you.
Free tier for individual developers
Full feature access. No credit card. Ship the first agent and the first trace at the same time.
Drop-in SDKs
Python, JavaScript, Go, Rust, Java, .NET. Auto-instrumentation for the frameworks startups actually use — LangChain, CrewAI, AutoGen, Pydantic AI, MCP.
First trace in five minutes
Add one line at startup. Run your agent. Open Trefur. The trace is there with every LLM call, tool call, retry, and outcome.
Cost visibility from day one
Per trace, per agent, per model vendor. Roll up by week. Watch the chart move when you change a prompt.
Demo-ready dashboards
Share a read-only link with an investor, a customer, a candidate. Live runs, live cost, live latency. Nothing canned.
Open standards keep you portable
OpenTelemetry GenAI on the wire. When your CTO asks if you can move to a different stack, the answer is yes — without losing data.
A startup arc with Trefur.
- Week 1
Solo engineer, MVP agent shipping to ten beta users. TrefurObserve.init() at startup, free tier. Every run shows up in the dashboard.
- Week 4
First customer report of a wrong answer. You open the trace from the user email, see the agent picked the wrong tool, fix the prompt, redeploy. 12 minutes.
- Week 8
Hire #3 joins. You point them at Trefur. They watch live runs for an hour, understand the agent better than the README.
- Month 4
Cost spike. You filter by agent and see a new endpoint is calling the writer agent in a loop. Cap the iteration limit, redeploy. Spend normalizes.
- Month 6
Series A pitch. Demo runs on production traffic with Trefur dashboard on the second screen. Cost per run, p95 latency, success rate — live numbers, not a slide.
Free for individual developers. Predictable as you grow.
Start free. Move to Pro when you outgrow the limits. No surprise bills, no per-seat traps.