Jut, an Operations Data Hub for DevOps, announced the availability of its platform in open beta.
Built for people who are constantly looking for clues into what their company's software is actually doing, Jut is an analytics platform designed to bring together all of your metrics, log and event data in a single place -- enabling DevOps teams to visually and in real-time ask questions to understand usage, improve software performance, react faster to anomalies and become predictive.
DevOps teams are tasked with the responsibility of handling large volumes of data that accumulates quickly and requires real-time analysis, but are armed with a myriad of disparate tools that offer very little insight into how software and systems are truly operating. To solve this problem, Jut leverages its own dataflow programming language, Juttle, that unifies analytics on live-streaming and historical data with both structured and unstructured data types, meaning users can make any possible query across all their operational data.The platform is built on a unique Hybrid SaaS model designed to give users complete control of their operational data, whether software runs in their public cloud or their data center.
With Jut, users are able to:
- Ask the right questions. Jut provides a powerful way to look at your data the way you want. No more dead ends when you're exploring.
- Understand, monitor, and alert on systems behavior. Data that was scattered in many places now can be correlated more effectively. That means better answers, faster, and an easier way to see your whole system.
- Troubleshoot more effectively. Problems with software and systems rarely make their solutions known -- Jut's iterative and real-time approach answers questions through data exploration in a way today's monitoring systems simply can't.
- Correlate user activity to system performance. Most systems look at users, or systems. Jut is a framework that enables the best of both worlds in one place, providing a single, holistic view.
- Manipulate and visualize data. Play with data, get real-time feedback and then share the results with interactive, customizable data visualizations.
"Just about every business today runs on software. As a result, the health of the business depends on understanding the health of the software -- how software performs, how users are interacting with it, where the bottlenecks lie and where it can perform better," said Steve McCanne, founder and CEO at Jut. "As companies realize that software is core to the business, the people developing the software are in reality developing the business. We want to empower DevOps teams with a holistic, unified platform that enables them to correlate all their data and make bigger decisions about the software that powers the rest of their organization."
Jut is available immediately in open beta. There is no cost to use Jut at any scale during the open beta period.
The Latest
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 ...
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.