Latest news with #Causely
Business Wire
06-05-2025
- Business
- Business Wire
Causely's New Integration with Grafana Labs Goes Beyond Observability to Understandability
SEATTLE--(BUSINESS WIRE)-- GRAFANCON– Causely, the causal reasoning platform for modern engineering teams, today launches a new integration with Grafana Labs that automatically surfaces root causes directly within Grafana dashboards. Grafana Labs is the observability company behind the world's most ubiquitous and open dashboards leveraged by over 25 million users around the world. The Causely platform utilizes purpose-built causal models to infer root causes automatically, saving engineers hours of manual correlation and guesswork. 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. Share '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. About Causely Causely leverages causal reasoning to cut through the observability noise and pinpoint what matters. Engineers are overwhelmed by too many tools, alerts, and data coming from their existing observability solutions. Causely automatically surfaces only the most critical risks to service reliability, enabling businesses to minimize operational overhead and maintain reliability without manual troubleshooting.
Int'l Business Times
06-05-2025
- Business
- Int'l Business Times
Getting Humans Out of the Observability Loop with Causely
Causely is a new player in modern DevOps. Their pitch is simple: your observability systems are too noisy. It takes too much time to identify root causes among all of the alerts and dashboards. And these issues are being exacerbated by the rise of AI. Their founder, Shmuel Kliger, has been thinking about IT Operations and complex systems for decades. And he has some counterculture opinions when it comes to how to best manage these complex systems. Firstly, he doesn't believe in the "collect all the data" approach that many modern observability platforms promote. Because while yes, storing data is becoming cheaper, there is also a lot more of it. And perhaps more importantly than the cost implications of collecting everything is simply the fact that 80% of that data will never be needed. In other words, teams are accepting a lot of noise in the name of making sure they have total visibility. When it's time to actually address an alert or a customer complaint, this is all the data that gets in the way and makes finding the actionable root cause like trying to find a needle in a haystack. Instead, Kliger believes that companies should take a "top-down" approach, starting with the likely root causes and then drilling down into the necessary data. The platform he's built, called Causely, leverages causal reasoning to map observability signals to likely root causes. Kliger is also very direct about the fact that in the not-so-distant future, humans will be out of the loop when it comes to managing IT Operations. "Planes can fly themselves—what makes us think IT Operations can't be autonomous?" he asks us to consider. He does, however, acknowledge that it's a journey and we aren't quite there yet—but more and more we should expect machines to take over identifying performance, security, and reliability issues. A nod in that direction is the company's latest integration with Grafana. Grafana Labs is the observability company behind the world's most ubiquitous and open dashboards leveraged by over 25 million users around the world. 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. " Engineers are overwhelmed by too many tools, alerts, and data coming from their existing observability solutions," said Kliger. "We are trying to make it as simple as possible to plug in our solution to your existing workflow and significantly reduce the manual toil required to identify root causes within complex modern applications."
