At scale, understanding performance in a distributed system means seeing beyond metrics and into real production execution.
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Continuous Deployment: How to Ship Faster Without Breaking Production
A couple of years ago, I was on a team that pushed to production every Thursday. It was always tense. We’d kick off the release and just sit…
Incident Monitoring in Production: How to Detect, Prioritize, and Resolve Issues Faster
Most teams don't discover incidents. Their users do. A Microsoft Research paper published at SoCC '22 analyzed 152 high-severity incidents across a cloud service used by hundreds of…
Error Tracking in Production: How to Detect Critical Failures Before Users Notice
Detect production errors fast, catch critical failures early with alerts, tracing, and triage workflows, before users notice today.
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Hud runs on millions of services across massive production environments, with negligible overhead.
“Hud eliminated our voodoo incidents. AI-powered root cause analysis turned days of drilling through tools into minutes.”
“Hud completely changed how early we detect, and how quickly we solve issues long before they impact the business.”
“AI helps us move faster only when it’s grounded in reality. Having accurate runtime context means our engineers, and our AI workflows, can reason about real production behavior and act on it safely.”
“We knew the crashes were caused by an ELU spike, but the question was what caused that spike. Using Hud, we found the answer in minutes with an AI-ready fix.”
“When engineering decisions are grounded in real execution data, it changes how confidently we ship, scale, and invest. That level of certainty directly supports the business as the platform continues to grow.”
