OpenObserve

Jacob Swiss, Hengfei Yang, and crew looked at the observability space—logs, metrics, traces, the whole stack that keeps engineering teams sane—and said "yeah, Datadog is great but what if it didn't cost half your Series B?" So they built OpenObserve, an AI-native, open-source observability platform that handles petabyte-scale data without the eye-watering price tag.

Here's the thing about observability: everyone needs it, everyone's frustrated with the cost, and no one wants to lock into a vendor that charges you per ingest like they're metering oxygen. OpenObserve claims 140x lower cost than the incumbents—and they're open source, so you're not trapped. Over 6000 companies already rely on them, ranging from Fortune 500 giants to scrappy startups who discovered they don't need to spend $50k/month to debug their production issues.

They're positioning themselves as the Datadog alternative for teams who want control, transparency, and zero licensing nightmares. Open source means you can self-host, audit the code, and walk away anytime. AI-native means they're not just moving logs around—they're using AI to actually make sense of the noise. Petabyte-scale means they're not playing small. This is the kind of project that makes enterprises nervous about their vendor contracts and excited about their engineering budgets.

Based in the cloud, and they're looking for developers who are tired of paying per gigabyte to debug their code.