Shai Alani has spent his career in one of the more demanding corners of B2B technology marketing: developer tooling and AI monitoring, where buyers are technically sophisticated, category definitions are actively contested, and the gap between a strong product and a recognized market position requires sustained effort to close. His appointment as Vice President of Marketing at Hud is the latest move in a trajectory that has kept him close to some of the most consequential problems in modern software engineering.
A Background That Maps Directly
Alani’s prior roles are coherent in retrospect. At Coralogix, a log management and observability platform, he built experience communicating a complex infrastructure proposition to engineering and security buyers. At Aporia, an AI model monitoring company, he worked in a market still establishing its own category boundaries. At Lightrun, a developer observability platform focused on live debugging and dynamic instrumentation, he served as VP Marketing and led go-to-market strategy for a product built to give developers runtime visibility into production systems.
Each stop dealt with a version of the same fundamental challenge: helping engineering teams understand what their software is doing in production, and building go-to-market strategy around that capability for buyers who value precision over abstraction.
Why He Joined Hud
The gap Alani is now charged with addressing is specific. Traditional observability confirms that failures occurred. It does not provide function-level evidence of why a failure occurred: what the code was doing, under what conditions, at the precise moment of the incident. Reconstructing that picture from logs and traces is slow and often inconclusive.
In AI-native environments, the problem compounds. Coding agents can read a codebase and suggest fixes but operate without access to runtime evidence of how code actually performed in production. The gap between what an agent knows about the code and what actually happened when it ran is precisely where debugging breaks down.
“Runtime Intelligence is the missing layer in the AI software stack,” said Shai Alani, VP Marketing at Hud. “AI has made it easy to generate code, but it has not made it any easier to stand behind that code once it is running in production, where reliability is actually decided. That gap is fast becoming one of the defining problems for AI-native engineering teams, and it is exactly the kind of category you build a company around. That is why I joined Hud, and it is the story I am excited to take to market.”
The Scope of the Role
At Hud, Alani’s mandate covers global marketing strategy, category creation, brand, and demand generation. The category creation component reflects Hud’s core ambition. Runtime Intelligence needs to move from an internal conviction to a term that engineering leaders reach for naturally when describing a problem they already experience.
“AI has changed the speed of software creation, but production is still where code proves itself,” said Roee Adler, Co-founder and CEO of Hud. “The next major category in the AI SDLC is Runtime Intelligence: production behavior resolved to the function level, coupled with deep forensics when things go wrong, so humans and agents can understand, fix, and validate software with confidence. Shai brings the experience we need to build that category and scale Hud into a defining company for AI-native engineering teams.”
That framing treats Alani’s role as strategic and definitional, not just operational.
The Work Ahead
The category Alani is charged with building is early in terms of market recognition. Engineering teams experiencing the problem Hud addresses may not yet have language for what they are missing. Building that language, establishing it as the natural way to describe the gap, and connecting it credibly to Hud’s product is the work in front of him.
His background at Lightrun, Coralogix, and Aporia is the preparation. The market conditions created by AI-native development are the opportunity. The execution starts now.
