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Lightrun Launches Free Debugger

Lightrun released a free, self-service version of its debugging solution for developers.

Lightrun Cloud is not only a powerful debugger a developer can use to troubleshoot production applications live from within the IntelliJ IDE - but it is also easy to set-up, with a complete self-service experience that gets developers up and running in less than five minutes.

Lightrun specializes in giving developers observability into running code, and pinpointing specific issues directly within the IDEs and CLIs they already use. Lightrun Cloud is optimized for debugging modern application architectures like containers, microservices and serverless, where the degree of difficulty is very high for pinpointing and reproducing bugs in production, as services and instances run across more than one machine. The dozens of organizations and development teams that have adopted Lightrun tend to be heavy users of Docker and Kubernetes, Spring Boot, Tomcat, Jetty, microservices frameworks like MicroProfile and Quarkus, and popular frameworks in big data and stream processing, such as Apache Spark and Apache Flink.

“Distributed frameworks are the cornerstone of the most interesting cloud-native development use cases today, but all of these frameworks also carry their own complexities and opportunities for user error,” said Ilan Peleg, co-founder and CEO of Lightrun. “Lightrun is particularly popular with developer teams that are shipping frequently, embracing failure, and who need the fastest possible way to identify and fix bugs in production. Lightrun Cloud is a free SaaS offering that gives developers the easiest way to isolate bugs in running production systems -- everything from logic bugs based on developer error, to system related bugs based on cluster deployment and configuration. These are the types of bugs that are extremely difficult to find, but increasingly common while orchestrating these complex distributed systems.”

Historically, observability into production applications relies on the paradigm of collecting massive volumes of logs, storing those logs, then analyzing them through application performance monitoring tools (APM). Lightrun Cloud inverts the model -- instead of logging everything and working backwards, the platform gives developers “shift left” observability from within the tooling they already use (IDEs, CLIs, VCS), and the ability to add log lines to running applications. This new paradigm allows developers to own debugging within the software development lifecycle, reducing the time to discovery and reproduction of bugs, and significantly cutting operational and observability costs.

- Lightrun Cloud for SaaS Debugging - In multi-tenant SaaS applications, bugs are hard to detect during development and testing, because issues tend to be specific to configuration- / tenant- / user environment- variables. These issues tend to only surface in production, and Lightrun Cloud gives developers a way to pinpoint the specific environmental dynamics of where the problem is occuring in running code.

- Lightrun Cloud for Complicated Performance Issues - Many performance issues can only be pinpointed when software is running in production, due to scale, races, data volumes and other strains that are only present in the production environment. In production environments, profilers and packet analyzers impose too big of a footprint as they instrument every invocation of every piece of code, every function innovation, and exit. Lightrun Cloud brings a more surgical approach to generating performance metrics like time measurements, counters and more - while presenting a very small footprint and keeping the service reliable.

- Lightrun Cloud for Distributed Frameworks - Frameworks give powerful capabilities to developers building distributed applications, but they also present many opportunities for bugs. Apache Spark jobs, for example, are full of unique real-data problems like fuzzy records, performance problems due to bad partitioning, serialization errors and more. Lightrun Cloud allows developers to inspect a live Spark job, capture real-time data, and inspect the RDD without bringing the application down. Lightrun Cloud allows developers to capture the many benefits of distributed applications, while shortening the debugging process and allowing bug detection in real-time instead of waiting for redeployments and reproducing environments.

Lightrun Cloud is available now.

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Lightrun Launches Free Debugger

Lightrun released a free, self-service version of its debugging solution for developers.

Lightrun Cloud is not only a powerful debugger a developer can use to troubleshoot production applications live from within the IntelliJ IDE - but it is also easy to set-up, with a complete self-service experience that gets developers up and running in less than five minutes.

Lightrun specializes in giving developers observability into running code, and pinpointing specific issues directly within the IDEs and CLIs they already use. Lightrun Cloud is optimized for debugging modern application architectures like containers, microservices and serverless, where the degree of difficulty is very high for pinpointing and reproducing bugs in production, as services and instances run across more than one machine. The dozens of organizations and development teams that have adopted Lightrun tend to be heavy users of Docker and Kubernetes, Spring Boot, Tomcat, Jetty, microservices frameworks like MicroProfile and Quarkus, and popular frameworks in big data and stream processing, such as Apache Spark and Apache Flink.

“Distributed frameworks are the cornerstone of the most interesting cloud-native development use cases today, but all of these frameworks also carry their own complexities and opportunities for user error,” said Ilan Peleg, co-founder and CEO of Lightrun. “Lightrun is particularly popular with developer teams that are shipping frequently, embracing failure, and who need the fastest possible way to identify and fix bugs in production. Lightrun Cloud is a free SaaS offering that gives developers the easiest way to isolate bugs in running production systems -- everything from logic bugs based on developer error, to system related bugs based on cluster deployment and configuration. These are the types of bugs that are extremely difficult to find, but increasingly common while orchestrating these complex distributed systems.”

Historically, observability into production applications relies on the paradigm of collecting massive volumes of logs, storing those logs, then analyzing them through application performance monitoring tools (APM). Lightrun Cloud inverts the model -- instead of logging everything and working backwards, the platform gives developers “shift left” observability from within the tooling they already use (IDEs, CLIs, VCS), and the ability to add log lines to running applications. This new paradigm allows developers to own debugging within the software development lifecycle, reducing the time to discovery and reproduction of bugs, and significantly cutting operational and observability costs.

- Lightrun Cloud for SaaS Debugging - In multi-tenant SaaS applications, bugs are hard to detect during development and testing, because issues tend to be specific to configuration- / tenant- / user environment- variables. These issues tend to only surface in production, and Lightrun Cloud gives developers a way to pinpoint the specific environmental dynamics of where the problem is occuring in running code.

- Lightrun Cloud for Complicated Performance Issues - Many performance issues can only be pinpointed when software is running in production, due to scale, races, data volumes and other strains that are only present in the production environment. In production environments, profilers and packet analyzers impose too big of a footprint as they instrument every invocation of every piece of code, every function innovation, and exit. Lightrun Cloud brings a more surgical approach to generating performance metrics like time measurements, counters and more - while presenting a very small footprint and keeping the service reliable.

- Lightrun Cloud for Distributed Frameworks - Frameworks give powerful capabilities to developers building distributed applications, but they also present many opportunities for bugs. Apache Spark jobs, for example, are full of unique real-data problems like fuzzy records, performance problems due to bad partitioning, serialization errors and more. Lightrun Cloud allows developers to inspect a live Spark job, capture real-time data, and inspect the RDD without bringing the application down. Lightrun Cloud allows developers to capture the many benefits of distributed applications, while shortening the debugging process and allowing bug detection in real-time instead of waiting for redeployments and reproducing environments.

Lightrun Cloud is available now.

The Latest

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...