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Causely Integrates with Grafana Labs

Causely launches a new integration with Grafana Labs that automatically surfaces root causes directly within Grafana dashboards. 

The Causely platform utilizes purpose-built causal models to infer root causes automatically, saving engineers hours of manual correlation and guesswork.

“Grafana has millions of users and some of the most well-known companies using their cloud solutions to drive their digital businesses,” said Yotam Yemini, CEO of Causely. “They are servicing massive companies like Atlassian, Dell, Roblox and Wells Fargo – which means a tremendous amount of data is being generated that needs to be made sense of. Causely automatically identifies the root cause of anomalies and instantly makes all the data shown in a customer’s Grafana dashboards more actionable.”

By embedding Causely’s intelligence directly into a Grafana dashboard, engineers can instantly see the “why” behind performance issues in the context of their services, significantly cutting resolution time when there’s an alert that needs to be addressed. Causely also plugs into Grafana Alertmanager, enriching existing alerts with real-time, continuously-updated-root-cause intelligence. This AI-powered capability goes beyond sending alerts when something is wrong, getting deeper into where the problem originated and what to do next within the incident response workflow.

“By integrating Causely with Grafana, engineers have another option to see the inferred root causes of issues amongst the context of all of their relevant services,” said Ash Mazhari, VP of Corporate Development at Grafana Labs. "We're thrilled to collaborate with Causely to offer our users more choices and enhance their insights and value."

The Causely system works by automatically mapping an application’s topology and service dependencies, then applying a targeted set of high-probability root causes to this data. This novel approach to observability significantly reduces manual troubleshooting by making sense of patterns and determining the best path to remediation, converting alerts into actionable root causes. 

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Causely Integrates with Grafana Labs

Causely launches a new integration with Grafana Labs that automatically surfaces root causes directly within Grafana dashboards. 

The Causely platform utilizes purpose-built causal models to infer root causes automatically, saving engineers hours of manual correlation and guesswork.

“Grafana has millions of users and some of the most well-known companies using their cloud solutions to drive their digital businesses,” said Yotam Yemini, CEO of Causely. “They are servicing massive companies like Atlassian, Dell, Roblox and Wells Fargo – which means a tremendous amount of data is being generated that needs to be made sense of. Causely automatically identifies the root cause of anomalies and instantly makes all the data shown in a customer’s Grafana dashboards more actionable.”

By embedding Causely’s intelligence directly into a Grafana dashboard, engineers can instantly see the “why” behind performance issues in the context of their services, significantly cutting resolution time when there’s an alert that needs to be addressed. Causely also plugs into Grafana Alertmanager, enriching existing alerts with real-time, continuously-updated-root-cause intelligence. This AI-powered capability goes beyond sending alerts when something is wrong, getting deeper into where the problem originated and what to do next within the incident response workflow.

“By integrating Causely with Grafana, engineers have another option to see the inferred root causes of issues amongst the context of all of their relevant services,” said Ash Mazhari, VP of Corporate Development at Grafana Labs. "We're thrilled to collaborate with Causely to offer our users more choices and enhance their insights and value."

The Causely system works by automatically mapping an application’s topology and service dependencies, then applying a targeted set of high-probability root causes to this data. This novel approach to observability significantly reduces manual troubleshooting by making sense of patterns and determining the best path to remediation, converting alerts into actionable root causes. 

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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 ...

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