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Datadog Releases Error Tracking

Datadog announced the launch of Error Tracking, a new product that automatically gathers application errors in realtime and intelligently aggregates them into actionable issues for engineering teams.

A single issue within an application can cause hundreds to thousands of errors due to events generated for every user session, code version, service, error type, SDK or other environmental detail affected by the issue. This overwhelming error volume can require several hours of manual analysis to identify underlying problems before an engineering team can triage the most critical issues, investigate and then resolve these errors. Datadog Error Tracking streamlines the troubleshooting effort by intelligently grouping individual application errors which are interrelated into a small set of issues. Engineering teams can then work off of this short list to determine the root cause and rapidly resolve the problem.

“In modern applications, the amount of errors can increase rapidly as we serve more users, make frontend code logic more complex with Single Page Applications, and increasingly rely on microservices and elastic infrastructure. Application engineers need a solution to prioritize issues in fast moving situations that impact customer experience and revenues,” said Renaud Boutet, Vice President, Product at Datadog. “Datadog Error Tracking automatically processes the data that is already available within our platform to provide engineers the insight that they need to resolve issues quickly and efficiently.”

Datadog Error Tracking will enable teams to clearly identify similar errors and view contextual data needed for resolution in a single platform. Key features include:

- Automatic Error Extraction: Errors are automatically extracted for existing users of Datadog RUM without the need to install a new SDK or write new code.

- Errors view: A simplified search function and visualization tool using tags and facets to group errors into related issues, as well as when an issue was first and last seen, to help teams prioritize their troubleshooting.

- Unminified stack traces: Access to unminified source-code, so teams can pinpoint the cause of the error from the stack trace.

- Seamless developer experience: Functions within existing CI/CD workflows using the Datadog CLI. This enables application developers to track their releases and link the associated source code with error-events generated by each release.

- Correlation across RUM sessions: Valuable data including session ID, view ID, URL, browser, location, OS, are automatically correlated with the error so teams can triage and resolve frontend application errors.

Datadog Error Tracking is now generally available within the Datadog platform and included for all RUM customers at no additional charge.

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Datadog Releases Error Tracking

Datadog announced the launch of Error Tracking, a new product that automatically gathers application errors in realtime and intelligently aggregates them into actionable issues for engineering teams.

A single issue within an application can cause hundreds to thousands of errors due to events generated for every user session, code version, service, error type, SDK or other environmental detail affected by the issue. This overwhelming error volume can require several hours of manual analysis to identify underlying problems before an engineering team can triage the most critical issues, investigate and then resolve these errors. Datadog Error Tracking streamlines the troubleshooting effort by intelligently grouping individual application errors which are interrelated into a small set of issues. Engineering teams can then work off of this short list to determine the root cause and rapidly resolve the problem.

“In modern applications, the amount of errors can increase rapidly as we serve more users, make frontend code logic more complex with Single Page Applications, and increasingly rely on microservices and elastic infrastructure. Application engineers need a solution to prioritize issues in fast moving situations that impact customer experience and revenues,” said Renaud Boutet, Vice President, Product at Datadog. “Datadog Error Tracking automatically processes the data that is already available within our platform to provide engineers the insight that they need to resolve issues quickly and efficiently.”

Datadog Error Tracking will enable teams to clearly identify similar errors and view contextual data needed for resolution in a single platform. Key features include:

- Automatic Error Extraction: Errors are automatically extracted for existing users of Datadog RUM without the need to install a new SDK or write new code.

- Errors view: A simplified search function and visualization tool using tags and facets to group errors into related issues, as well as when an issue was first and last seen, to help teams prioritize their troubleshooting.

- Unminified stack traces: Access to unminified source-code, so teams can pinpoint the cause of the error from the stack trace.

- Seamless developer experience: Functions within existing CI/CD workflows using the Datadog CLI. This enables application developers to track their releases and link the associated source code with error-events generated by each release.

- Correlation across RUM sessions: Valuable data including session ID, view ID, URL, browser, location, OS, are automatically correlated with the error so teams can triage and resolve frontend application errors.

Datadog Error Tracking is now generally available within the Datadog platform and included for all RUM customers at no additional charge.

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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 ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...