Traceloop

At Traceloop, we envision a future where the reliability and quality of AI systems, especially large language models, are measurable, predictable, and optimized with the same rigor as traditional software. Our mission is to transform AI development by embedding observability and testing deeply into the AI lifecycle, reducing uncertainty for engineering teams worldwide.

By leveraging cutting-edge monitoring frameworks and pioneering automated evaluation tools, we empower organizations to confidently deploy AI agents that are robust, transparent, and trustworthy. We believe that the next era of AI innovation will be driven by rigorous data and insights rather than guesswork, enabling safer and more scalable AI applications.

As custodians of AI reliability, we are committed to building an ecosystem that blends software engineering discipline with AI’s transformative potential, forging new paths to seamless integration and operational excellence for AI-powered products.

We've been tracking Traceloop since their Y Combinator days, and frankly, we're impressed by how they've tackled one of AI development's messiest problems. While everyone's racing to build the next ChatGPT killer, these folks are solving the unglamorous but critical challenge of making sure your AI actually works reliably in production.

The Problem They're Actually Solving

Here's what caught our attention: Traceloop isn't just another monitoring tool with AI slapped on top. They're addressing the fact that most teams are essentially doing "vibe checks" on their LLM applications—manually testing prompts and hoping for the best. That's terrifying when you're running AI in production at scale.

The founders, Nir Gazit and Gal Kleinman, clearly know this pain firsthand from their time at Google and Fiverr. They've built something that treats AI models like the complex software systems they actually are, complete with proper testing, monitoring, and evaluation frameworks.

What Makes Their Approach Different

Most observability tools feel like they were designed for traditional software and awkwardly adapted for AI. Traceloop built theirs from the ground up for LLM applications. Their platform can automatically detect hallucinations, track costs in real-time, and even backtest model changes before you deploy them.

We particularly like their Jest-opentelemetry tool for integration testing. It's exactly the kind of practical developer tool that shows they understand the day-to-day frustrations of building with LLMs. When your AI agent fails, you need to know where and why—not just that it failed.

The Impressive Traction Story

With customers like Miro monitoring millions of conversations and a recent $6.1 million funding round backed by some serious names (including the CEOs of Datadog, Sentry, and Elastic), Traceloop is clearly solving a real problem. The fact that observability veterans are investing tells us they see the massive opportunity here.

Their open-source OpenLLMetry framework has also gained solid adoption, which gives them a nice flywheel effect—developers try the open-source version, then upgrade to the commercial platform when they need enterprise features.