Hud is the world's first Runtime Intelligence platform. Its Runtime Code Sensor watches how your code behaves in production, function by function, and turns that into root cause and fixes.
Installed in 10 seconds with one line and no configuration, Hud gives software engineers and AI coding agents the live production context they are missing: what broke, why it broke, and the code responsible. Teams use it to gate AI-generated pull requests, roll out changes safely, and resolve errors and performance degradations without sifting through logs, traces and dashboards.
No matter how carefully code is written, systems fail in surprising ways once they reach production. That gap is widening now that AI agents write more of the code. They plan, write, test and review, then stop at the pull request with no idea what happens after the merge. The usual monitoring tools do not close it. They were built for humans reading dashboards after the fact, they need to be told in advance where to look, and they tell you that something broke without telling you why.
Hud closes the gap with a Runtime Code Sensor that runs alongside your code in production. It installs in about 10 seconds with a single line, needs no configuration and no maintenance, and maps your entire codebase on its own. From there it continuously follows function-level behavior under real conditions, with no sampling and negligible overhead, and gathers deep forensic context for the exact case that happened. Two components do the work: an Impact Map that connects code to live behavior across services and deployments, and a Forensic Engine that captures the evidence behind a specific failure in a form both engineers and LLMs can use.
Engineering teams rely on Hud to:
Gate AI-generated pull requests by analyzing them against live production data and classifying their risk before merge
Roll out changes safely, catching function-level regressions early, rolling back automatically, and generating a fix PR so the release can go out again
Detect and fix the errors, performance degradations and CPU spikes that surface weeks after deploy as production conditions shift
Pinpoint root cause in the code with real-time forensic data instead of reconstructing the story from logs, traces and dashboards
Guide coding agents with accurate production context, delivered where the work already happens: IDE extensions, agents over MCP, and CI/CD
Hud already runs across millions of services in production at monday.com, Riverside, Drata, Lemonade, Axonius and Cyera. Teams fix fast, merge with confidence, and bring production reality into the AI development loop.
Highlights
Install in 10 seconds and get function-level runtime context. One line, no configuration, no maintenance. Hud maps your entire codebase on its own, watches how every function behaves in real production conditions with no sampling and negligible overhead, and gathers forensic context that both engineers and AI coding agents can act on.
Gate and safely roll out AI-generated code. Hud analyzes pull requests against live production data to classify their risk before merge, catches function-level regressions during rollout, rolls back automatically, and generates a fix PR so the release can go out again safely.
Find root cause and fix production issues faster. Hud detects errors, performance degradations and CPU spikes as production conditions shift, pinpoints the responsible code with real-time forensic data, and generates a code-level fix, so nobody has to reconstruct the story from logs, traces and dashboards.
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Pricing is based on the duration and terms of your contract with the vendor. This entitles you to a specified quantity of use for the contract duration. If you choose not to renew or replace your contract before it ends, access to these entitlements will expire.
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Runtime code sensor that shows what every change actually does in production, so your team ships safely and fixes fast. Request a private offer to receive a custom quote.
This listing has one pricing dimension, billed as a contract priced by users. There are no tiers or instance sizes to choose from. Instead, you request a custom private offer to receive a quote based on your team size. The private offer sets your contract terms and total cost. Because pricing scales with the number of users, larger teams factor into the negotiated quote. Contact the vendor to arrange your private offer and confirm the details that apply to your deployment.
Top-of-mind questions for buyers
What counts as one user for billing under this contract?
Pricing scales by the number of users on your team who access the platform. The private offer quote reflects your total user count. Since the quote is customized, confirm with the vendor how your team members map to billed users for your specific deployment.
Do I pay based on how many services or servers the sensor runs on?
No. This contract meters by users, not by servers, services, or invocation volume. The runtime sensor runs across your services and captures all invocation counts and exceptions, but your billed amount reflects team size, not the number of monitored systems.
Does adding more monitored code or higher traffic increase my cost?
No. Your cost is tied to user count, not to traffic or data volume. The sensor captures all invocation counts and exceptions with no sampling, and runs with low overhead. Growth in traffic does not change your billed amount; only a change in users would.
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