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Currai
Observability & A/B Testing for LLM Apps
You shipped your LLM app — now you're debugging it with print(). You can't see what the model actually received, why it answered the way it did, or what each call cost. Currai fixes that: it traces every prompt, token, and tool call in one view, so you can debug and track spend with confidence — and A/B test prompt versions on live traffic to see which one actually wins. Drop in the Python or TypeScript SDK and your first trace lands in ~5 minutes, with zero infrastructure to run.
Hey Product Hunt! 👋 I'm Yasser, the founder of Currai.
I've spent the last 6+ years as a software engineer, and a lot of that lately has been building on top of LLMs. Every time I shipped something with AI in it, I hit the same wall: I had no idea what the model actually did. Why did it give that answer? Which prompt did it really see? Why did one little feature quietly cost more than the rest of my app combined? I was debugging surprise token bills with print() statements.
So I built Currai — the observability layer I wished I had. Drop in one SDK and you can finally see inside your LLM app.
Here's how it works:
🎯 Traces every prompt, token, and tool call in one view — install the SDK, paste your keys, wrap a call, and your first trace shows up in ~5 minutes. No agent, no collector, no infrastructure to run.
💸 Rolls up token cost for you — per trace, model, user, and day — so you stop guessing what an AI feature actually costs.
🧪 A/B tests your prompts in production — split live traffic across prompt versions by weight, then compare cost and quality right in your traces. Stop arguing about wording; measure it.
And if you're already on Langfuse or OpenTelemetry, you migrate by changing a single line — your existing trace code, spans, and exporters just keep working.
Currai isn't another print() statement, and it isn't a self-hosted cluster you have to babysit (no ClickHouse to keep alive). It's hosted observability you drop in and own — you watch your data, not your infrastructure.
It's free to start — first traces in about five minutes, no credit card needed. 👉 currai.app
print() → production. 🧵
About Currai on Product Hunt
“Observability & A/B Testing for LLM Apps”
Currai was submitted on Product Hunt and earned 4 upvotes and 1 comments, placing #147 on the daily leaderboard. You shipped your LLM app — now you're debugging it with print(). You can't see what the model actually received, why it answered the way it did, or what each call cost. Currai fixes that: it traces every prompt, token, and tool call in one view, so you can debug and track spend with confidence — and A/B test prompt versions on live traffic to see which one actually wins. Drop in the Python or TypeScript SDK and your first trace lands in ~5 minutes, with zero infrastructure to run.
On the analytics side, Currai competes within Software Engineering, Artificial Intelligence, Tech and Vercel Day — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how Currai performed against the three products that launched closest to it on the same day.
Who hunted Currai?
Currai was hunted by yssr. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Currai including community comment highlights and product details, visit the product overview.
yssr