Helicone —The open-source LLM observability platform for developers.

Helicone envisions a future where the complexities of large language models become transparent and manageable, empowering developers to innovate confidently with AI. They are building a world where every interaction with language models is observable and optimizable, ensuring applications powered by AI deliver maximum value with clear insights into performance and cost.

Driven by technology and a passion for open collaboration, Helicone leverages open-source innovation and robust infrastructure to create a seamless experience for teams integrating AI. Their platform addresses the critical need for visibility, debugging, and control within AI development lifecycles, transforming how products harness the power of language models.

By fostering a community-centric approach and integrating advanced monitoring, analytics, and optimization tools, Helicone is shaping the foundation for responsible, efficient, and scalable AI solutions that will underpin the next generation of intelligent applications.

We've been keeping an eye on Helicone since their Y Combinator debut, and honestly, they're tackling one of those "why didn't someone build this sooner?" problems. If you've ever deployed an LLM feature to production and then sat there wondering why your OpenAI bill exploded or why responses suddenly got sluggish, you'll get why this exists.

The founding story resonates with us—Scott and Justin literally built this because they were frustrated by the lack of visibility into their own LLM integrations. That's the kind of authentic "scratch your own itch" origin that usually leads to solid products.

What Caught Our Attention

The integration is refreshingly simple. One line of code change and suddenly you're logging every prompt, completion, latency metric, and cost across OpenAI, Anthropic, Gemini, and more. We appreciate when developer tools don't require a PhD in DevOps to implement.

But here's what really impressed us: they've gone beyond just logging. The caching feature alone could save teams thousands on redundant API calls, and their intelligent retry handling means fewer frustrated users when rate limits hit. It's clear they've thought through the real pain points of LLM development.

The Open-Source Angle

Helicone's commitment to open-source feels genuine, not like a marketing tactic. They've built an active community on GitHub and Discord, and the fact that you can self-host gives enterprise teams the control they need. Plus, the free tier (10k requests/month) is generous enough for smaller teams to actually get value without immediately hitting a paywall.

The enterprise features—SOC 2 compliance, GDPR readiness, and their "Vault" for API key management—show they're serious about scaling beyond indie developers.

Where We See the Real Value

This isn't just another monitoring dashboard. The prompt management and experimentation tools turn Helicone into a proper MLOps platform for LLMs. Being able to version prompts, run evaluations, and track performance across different model providers in one place? That's the kind of workflow efficiency that saves engineering teams weeks of custom tooling.

We're particularly bullish on their AI Gateway—a Rust-powered router that handles failovers between 100+ models. As the LLM landscape gets more fragmented, having a unified interface that can switch between providers seamlessly becomes incredibly valuable.