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Datadog Introduces Kubernetes Active Remediation

Datadog announced the launch of Kubernetes Active Remediation, which builds on Datadog's automated troubleshooting capabilities to provide curated remediation guidance, best practices and end-to-end issue management for Kubernetes organizations.

Kubernetes Active Remediation offers the deeper insights at scale that teams need by providing a comprehensive overview of cluster-level resource problems ranked by their importance and relevance. After a problematic cluster or workload has been identified, teams can view consolidated troubleshooting information, including root cause analysis and recommended fixes. They can then directly trigger deployment patches for the key issues from within the Datadog platform.

"Today's announcement builds on our launch of Kubernetes Autoscaling at DASH to empower users to detect issues, gain contextual insights and make changes to their Kubernetes resources directly from Datadog's unified platform," said Yrieix Garnier, VP of Product at Datadog. "Customers are looking for help to identify an issue, curate data about it and take steps to solve it. Kubernetes Active Remediation will help customers do just that so they can resolve issues faster."

Kubernetes Active Remediation helps organizations:

- Automate root-cause analysis and detection: Recommendations are automatically issued with full contextual data and triaged to the correct owner. Curated remediation actions can also be pre-approved by the DevOps or security teams to automate the process downstream.

- Directly repair Kubernetes environments: Explanations and suggestions are provided based on troubleshooting patterns that are commonly seen in Kubernetes environments. Once the user has the full context and recommended next steps, they can directly trigger deployment patches from within Datadog to remediate the issue.

- Improve troubleshooting and remediation speed: By accelerating a user's time to detect and resolve an issue, Datadog helps application development teams be more efficient, understand root causes and automate their remediation processes.

Kubernetes Active Remediation is now in preview.

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Datadog Introduces Kubernetes Active Remediation

Datadog announced the launch of Kubernetes Active Remediation, which builds on Datadog's automated troubleshooting capabilities to provide curated remediation guidance, best practices and end-to-end issue management for Kubernetes organizations.

Kubernetes Active Remediation offers the deeper insights at scale that teams need by providing a comprehensive overview of cluster-level resource problems ranked by their importance and relevance. After a problematic cluster or workload has been identified, teams can view consolidated troubleshooting information, including root cause analysis and recommended fixes. They can then directly trigger deployment patches for the key issues from within the Datadog platform.

"Today's announcement builds on our launch of Kubernetes Autoscaling at DASH to empower users to detect issues, gain contextual insights and make changes to their Kubernetes resources directly from Datadog's unified platform," said Yrieix Garnier, VP of Product at Datadog. "Customers are looking for help to identify an issue, curate data about it and take steps to solve it. Kubernetes Active Remediation will help customers do just that so they can resolve issues faster."

Kubernetes Active Remediation helps organizations:

- Automate root-cause analysis and detection: Recommendations are automatically issued with full contextual data and triaged to the correct owner. Curated remediation actions can also be pre-approved by the DevOps or security teams to automate the process downstream.

- Directly repair Kubernetes environments: Explanations and suggestions are provided based on troubleshooting patterns that are commonly seen in Kubernetes environments. Once the user has the full context and recommended next steps, they can directly trigger deployment patches from within Datadog to remediate the issue.

- Improve troubleshooting and remediation speed: By accelerating a user's time to detect and resolve an issue, Datadog helps application development teams be more efficient, understand root causes and automate their remediation processes.

Kubernetes Active Remediation is now in preview.

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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 ...