Lightrun Secures $18M Funding and Unveils AI-Powered Debugger for Production Code

Israeli developer observability startup Lightrun raises $18M, launches new AI debugger - SiliconANGLE

Israeli developer observability startup Lightrun raises $18M, launches new AI debugger Israeli developer observability startup Lightrun Inc. announced today that it has raised $18 million in new funding and launched Runtime Autonomous AI Debugger, a runtime autonomous debugger based on generative artificial intelligence. Founded in 2019, Lightrun offers a developer-centered observability platform that allows developers to debug their code in real time without the need for extensive pretesting or redeployment. The company claims its services provide unparalleled visibility into application behavior across various environments, including development, staging and production. The company offers a "shift left" observability platform that gives developers deeper insights into running applications by allowing them to insert logs, metrics, traces and more during runtime. That, the company says, drastically reduces mean time to resolution and logging costs while elevating developer productivity and improving software quality. The shift left observability mimics and automates the existing developer workflow for troubleshooting runtime issues by hypothesizing the potential root cause based on IT operations and observability signals. The service then adds dynamic snapshots and logs on the fly to specific lines of code using a dynamic observability software development kit, which enables line-by-line runtime debugging. The cycle repeats until the root cause is identified. Lightrun's runtime debugging generative AI models suggest potential root causes, validating these hypotheses with real-time production data gathered by the software development kit. Notable customers include Salesforce Inc., Microsoft Corp., AT&T Inc., SAP SE, Advanced Data Process Inc., Booking Holdings Inc. (Priceline) and Industria de Diseño Textil, S.A. The new Runtime Autonomous AI Debugger, available today in private beta, automates the entire debugging journey from the initial ticket to pinpointing the exact culprit line of code in the integrated development environment. The results liberate developers from the endless cycle of troubleshooting, it says. The approach taken with the new service turbo charges observability and software debugging, saving developers from spending half of their time troubleshooting. In doing so, it claims, it cuts the operational mean time to resolution of production incidents to minutes. "While billions have been invested the last year in IDE-based gen AI tools like Copilot for code generation, testing and QA, gen AI-based runtime debugging in the IDE has been the missing link," said co-founder and Chief Executive Ilan Peleg. "Lightrun integrates seamlessly into the developer workflow -- from code writing to production operation -- enabling a complete, automated gen AI experience within the IDE." GTM Capital led the Series B funding round, with previous investors Insight Partners LP and Glilot Capital Partners Ltd. also participating. Including the new funding, Lightrun has raised $48 million to date.

Lightrun launches its AI debugger to help developers fix their production code | TechCrunch

Lightrun, a Tel Aviv-based startup that helps developers debug their production code from within their IDE, on Wednesday announced the launch of its first AI-based tool: the Runtime Autonomous AI Debugger. The new tool, which is currently in private beta, aims to help developers fix issues with their production code within minutes instead of hours. In addition, Lightrun also on Wednesday disclosed an $18 million SAFE round it raised last year from GTM Capital, with existing investors Insight Partners and Glilot Capital also participating. This brings Lightrun's total funding to date to $45 million. It's our understanding that the company plans to raise a Series B round next year. "Until now, we slashed [Meant Time to Recovery] to say 30 minutes, maybe 45 minutes on average, based on how we measure ourselves and customer feedback," Lightrun CEO and co-founder Ilan Peleg told me. "Now, we're going to automate everything from the moment you have a ticket that was raised, up until finding the root cause down to a single level of granularity, like which of your single lines of code is responsible for this very specific root cause." Over time, Peleg said, Lightrun would like to extend this to also using generative AI to fix bugs automatically. For now, though, that's not yet an option, but given how quickly the technology has advanced, it's probably just a matter of time. To do this, Lightrun is fine-tuning existing models to focus on debugging, something the company can do in part because it gets insights not only from the code itself but also the entire monitoring and observability stack. Looking ahead, the company also plans to connect this system to other enterprise inputs like ticketing systems. "There's so much data in the enterprise landscape that is somehow related to troubleshooting or debugging -- and that's missing from the Copilot-like solutions," Peleg said. Most of the Copilot-like chat interfaces, he argued, only look at the code but don't have enough insights into the context to present the best solutions. As Peleg noted, the team went through quite a few iterations before it felt like its system was ready for day-to-day use. About half a year ago, Lightrun started experimenting with existing models to see where generative AI could help its users. But at the time, the solution was far too expensive to offer as a product. "Now we have tuned our system [...] so that it's not going to add significant cost to us for the solution, which is why we're talking now. In the past, I didn't feel comfortable to announce something that was not yet there." At least for now, these generative AI features will simply be part of the existing Lightrun solution for the users in the private beta. Peleg stressed that the company wants to prove that the system does indeed bring value to users and isn't trying to optimize for monetization in the short term.