AI-based coding has exploded in popularity on the promise that it will make developers’ jobs faster and easier. But AI coding has also resulted in something else: a vast increase in lines of code, and thus the likelihood of bugs resulting in crashes or other mishaps.
On Monday, an Israeli startup called Lightrun, which has built an observability platform to identify and debug (remediate) code before those problems arise, announced a Series B of $70 million. The funding underscores not just the gap in the market for tools like these, but also Lightrun’s traction in meeting that demand.
New backer Accel is co-leading the round alongside previous investor Insight Partners, with participation from Citi, Glilot Capital, GTM Capital, and Sorenson Capital. Lightrun has now raised $110 million to date, including a Series A led by Insight that we covered in 2021.
The startup is not disclosing its valuation, but there are some strong signs that it’s doing well.
First, there are its customers. Citi is a strategic backer and is one of an impressive list of big-name clients that also includes ADP, AT&T, ICE/NYSE, Inditex, Microsoft, Priceline, Salesforce, and SAP.
Second, there is the product and the company’s timing for how it fits into the current market landscape. Back in July 2024, Lightrun announced a new AI-based debugging tool to use within organizations’ integrated developer environments (IDEs), appropriately called the Runtime Autonomous AI Debugger. Although the company’s platform was already delivering impressive results, this was the product that really spoke to the current predicament many enterprises are facing: AI is leading to a lot more coding and a lot more problems, and Lightrun had built an AI tool to address that.
The company said that revenues have grown more than fourfold since it was launched, and that this is what got investors knocking. Andrei Brasoveanu, the Accel partner who led the investment for the firm, said that he’d had his eye on Lightrun (observing, even) for years before this, and he finally took the plunge after that launch.
“Everything came together last year,” he said. “They saw acceleration in the enterprise, all because of AI.”
Timing is something that Ilan Peleg, the CEO who co-founded the company with CTO Leonid Blouvshtein, knows something about. Before turning his attention to further education and eventually building Lightrun, Peleg was a champion middle-distance runner, winning four national championships in Israel and ranked in the top 16 of all middle-distance runners across Europe.
As Peleg sees it, there are dozens of companies building observability tools in the market today; some of the most prominent include the likes of Datadog and App Dynamics.
But none have yet reached “the holy grail” of such work: not only being able to get a big picture of all the code that is being shipped in production, but to understand how it will interact with what is already being used, and how to anticipate where problems might arise — and, to do so with minimal interruption and thus minimal cost to the organization.
“Code is becoming cheap but bugs are expensive,” he said.
That problem, meanwhile, has reached “an inflection point,” he said. “Developers now can ship more code than ever before,” due to all the automation that is being used, thanks to AI. “But it’s still a very manual process to fix it when things go wrong.”
Lightrun’s breakthrough has been to build an observability toolset that can monitor code just as it is in the IDE and understand how it will behave alongside code that is actively in production. Lightrun is then able to automatically make adjustments to the code as it moves into production to continue operating without interruption and crashes. It does this by way of being able to create AI-based simulations to understand that behaviour, and then to fix the code before issues arise.
“This is the part where we are unique,” Peleg said.
There are a lot of options for how Lightrun might develop, given how close observability sits to other activities in organizations.
One of those is building tools more specifically for cybersecurity teams, given the obvious security implications that arise out of bugs. Another is potentially building some of its tooling even closer to the point of code creation, to make finding and fixing possible bugs even more efficient.
For now, the plan is to remain focused on building out its tools, talent and business specifically in the IDE, Peleg said. “Everything that poses risk to resilience, we are mitigating,” he said, although he didn’t rule out more purpose-specific tooling in the future.
As for code assistants, “these might be in our future,” he said, “but even focusing and working only on the problem of software remediation once executed is complex and wide.” It will be hard to anticipate, he said, what code creation will look like in the future. Today, with between 30% and 60% of all production issues estimated to come from code issues generated by both humans and machines, providing a way to observe and fix everything — regardless of how it was created — is what Lightrun is racing to fix.
