As AI-generated code becomes a larger part of modern software development, engineering teams are facing a new challenge: ensuring that rapidly produced code can be deployed safely into production. While AI coding assistants have accelerated development cycles, they have also increased the need for production-aware validation, risk assessment, and faster incident resolution.
Against that backdrop, ClickHouse and Hud have announced a new partnership designed to connect observability with runtime intelligence, giving software teams greater visibility into how AI-generated code behaves before and after deployment. The collaboration combines ClickHouse’s observability platform with Hud’s Runtime Code Sensor to help engineering organizations move from detecting production issues to understanding their root cause more quickly.
Closing the Loop in AI-Powered Development
According to the companies, AI now generates or assists with 42% of the code developers ship, a figure expected to rise to 65% by 2027. As coding agents take on a larger share of software creation, the bottleneck is shifting from writing code to determining whether those changes are safe to release and how they will perform in real-world environments.
The partnership brings together ClickStack, ClickHouse’s open-source observability stack, and Hud’s Runtime Code Sensor. ClickStack provides visibility across applications and infrastructure, helping engineers identify where an issue originates, while Hud connects production behavior to the specific functions and code changes responsible.
The platforms are linked through shared trace IDs, allowing engineers to move directly from an issue detected in ClickStack to the relevant code-level context inside Hud. The integration also works in reverse, enabling Hud to surface the runtime context needed to investigate problems detected by ClickStack.
“AI is accelerating how quickly teams can generate code, but safely shipping it with high confidence requires production context,” said May Walter, CTO & Co-Founder of Hud. “Hud and ClickHouse bring real production behavior at an unparalleled breadth and depth, so teams can build a production-aware AI SDLC: Gating before it ships, proactive verification once it is deployed, and fix issues as they arise – all using real runtime truth. Together with ClickHouse, we are bringing that intelligence across the entire AI SDLC.”
Connecting Observability With Code-Level Intelligence
The companies position the integration as a way to bridge two different layers of software operations. ClickStack delivers broad operational visibility across infrastructure and services, while Hud focuses on the code layer by mapping runtime behavior back to individual functions and software changes.
The combined workflow enables engineering teams to move seamlessly between operational signals and the code responsible for them, reducing the time needed to investigate production incidents.
“Our users already trust ClickHouse to store and query their Open Telemetry data at scale,” said Mike Shi, Head of Observability at ClickHouse. “The shift now underway is from simply watching systems or investigating issues to using that data to make day-to-day engineering decisions. Hud connects observability data to the code and changes behind it, making the entire stack more useful for teams building and shipping software with AI.”
From Detection to Resolution
Many software issues begin with relatively small changes, such as slower database queries, unexpected resource consumption, or changes in how a function behaves under a particular workload. Hud detects these issues at the function level, while ClickStack provides the broader operational context surrounding the application.
Together, the companies say the platforms support several AI-powered engineering workflows, including pre-deployment risk assessment, release verification and regression detection, automated investigation with code-level fixes, and rollback and remediation workflows.
The partnership also introduces runtime intelligence as a potential gate for AI-generated code. Rather than relying solely on static analysis, engineering teams can evaluate code changes against live production behavior, allowing higher-risk changes to receive additional review while lower-risk updates move through deployment more quickly.
Building a Production-Aware AI SDLC
The companies say the integration is intended to support a production-aware AI software development lifecycle, where coding agents can use real operational data instead of relying only on source code or isolated alerts. By incorporating function-level runtime context and application history, teams gain additional information when evaluating changes before deployment, verifying releases during rollout, and resolving production issues afterward.
One organization already using both platforms is monday.com. “Like every modern engineering organization, a growing share of our code is now written with AI,” said Rom Kadria, Senior Software Engineer at monday.com. “We write code much faster, but the challenge has shifted to shipping just as quickly while maintaining confidence that new code won’t cause harm. ClickHouse gives us the wide operational picture at scale, while Hud gives us the runtime intelligence and next-level introspection needed to evaluate and ship AI-generated code confidently. When issues do arise, combining ClickHouse and Hud allows us to triage and resolve them quickly. For a company building with AI, that combination is the obvious choice.”
Engineering teams can adopt the integration by installing the Hud SDK and connecting it to their ClickStack service, allowing Hud’s runtime intelligence to flow alongside existing OpenTelemetry data. The companies describe the partnership as the first step in a broader collaboration, with additional integrations planned for the future.
