AI-driven coding has surged in popularity by promising to ease developers’ workloads and accelerate software production. But as coding speeds have risen, so too have the volume of code and, inevitably, the incidence of bugs and errors that can disrupt platforms or trigger software failures. Capitalizing on this dilemma, Israeli startup Lightrun, the maker of an observability platform designed to detect, diagnose, and remedy code issues before serious problems can occur, has just closed a $70 million Series B funding round.
Led jointly by new investor Accel and returning investor Insight Partners, the round attracted additional participation from Citi, Glilot Capital, GTM Capital, and Sorenson Capital, bringing Lightrun’s total funding raised to date to $110 million. The company has chosen not to publicly disclose its valuation.
Lightrun’s impressive customer roster lends credibility to its commercial momentum. Citi, not only an investor but also a prominent client, joins an array of major corporations including AT&T, ADP, Microsoft, Salesforce, SAP, Priceline, Inditex, and ICE/NYSE who rely on Lightrun’s solutions in their software environments.
Last summer, Lightrun made headlines when it introduced its Runtime Autonomous AI Debugger, an artificial intelligence-powered tool integrated directly into developers’ existing integrated development environments (IDEs). Already growing rapidly, the launch of this AI-centered product significantly boosted the platform’s visibility and appeal. According to Lightrun, its revenue has expanded more than four-fold following this innovation—a numbers game that quickly drew interest from investors.
Accel partner Andrei Brasoveanu explained he had been closely following Lightrun’s development for years, but the momentum provided by the company’s latest AI product cemented the firm’s decision to invest. “Everything aligned this past year. They saw acceleration in enterprise adoption—all driven by AI,” he said.
Lightrun CEO and co-founder Ilan Peleg comes from a competitive athletics background, having won four Israeli national championships and ranking among Europe’s elite middle-distance runners. Peleg understands timing—and in business, he’s convinced that the surging popularity of AI-assisted coding has created precisely the kind of tipping point his company was built to handle.
Dozens of companies currently compete in the observability space, including industry leaders like Datadog and AppDynamics. But Peleg argues that none have yet achieved the ultimate goal: sophisticated, predictive analysis that automatically accounts for how new code behaves alongside existing, production-level applications. Current approaches typically remain reactive, manual, and costly, whereas Lightrun aims to offer proactive diagnosis, automated remediation, and uninterrupted software performance—what Peleg calls “the holy grail.”
“As code becomes cheaper to produce, the cost of bugs grows significantly higher,” Peleg explained. Today’s development environment allows engineers to ship software faster and in greater volumes than ever, especially with the assistance of powerful AI tools. But the process of resolving bugs remains remarkably slow and manual. This gap—between speed of production and latency in remediation—is exactly what Lightrun is targeting.
The startup differentiates itself by providing seamless monitoring that integrates deeply into the IDE and production environment. Its AI-driven technology can simulate runtime interactions, anticipate potential malfunctions, and proactively remedy issues to ensure software stability. “That’s truly our unique advantage,” Peleg asserted.
Looking ahead, Peleg sees several potential strategic directions. Given the close connection between reliability and cybersecurity, Lightrun could expand into dedicated security tools. Another possibility could be pushing its offerings even deeper into the coding workflow—capturing problems at their inception to further streamline and automate debugging. But for now, he says the company is committed to growing and enhancing its current observability solution directly within developer environments, maintaining a laser-focus on resilience and performance.
Though code-generation tools like AI-assisted assistants or chatbots may enter Lightrun’s roadmap down the line, Peleg emphasizes the complexity and breadth of the software remediation challenge itself. With between 30% to 60% of current software issues traced to problematic code—often accelerated by the use of AI—the immediate priority remains clear: to perfect the means of observing, predicting, and autonomously correcting software issues regardless of how or where they originate.
