Moogsoft announced the launch of Moogsoft Observe, extending Moogsoft’s core AIOps platform capabilities from centralized analytics outwards to the data source, providing IT teams with absolute visibility and total observability of customer-impacting problems wherever they occur.
Moogsoft Observe ingests time-series and metrics data in real-time and applies AI to detect incidents at the source of the problem. Observe stores only anomalous and contextual data, giving IT teams highly-advanced, specific knowledge to improve their online services and applications while dramatically lowering data transport, ingestion, and storage costs. Without requiring data lakes or data scientists to operate, Observe can be easily used by IT generalists including site reliability engineers and DevOps.
“With Observe, we’re advancing the capabilities of AIOps to help IT teams better manage their services and applications in the face of a massive proliferation and decentralization of data,” said Phil Tee, CEO of Moogsoft. “Unlike other tools, Observe enables IT and DevOps teams to detect problems wherever their customers are, and allows them to identify the root cause for service disruptions sooner without the cost, complexity, and delays of collecting and analyzing all the data in a centralized location.”
Moogsoft Observe is powered by a new suite of patent-pending algorithms developed for the most complex IT use cases in the world’s largest enterprise environments. By ingesting and analyzing data at the source and storing only anomalous and contextual data centrally, Observe enables trend and historical analysis of relevant, meaningful data but at a lower cost and with significantly reduced latency — reducing the time to detect a problem by up to 60 percent and cutting nearly 90 percent of the storage and communication costs per bit of information.
Built for the enterprise, Observe is a single-agent technology that can operate across all environments, including cloud instances, workloads, and containers, as well as on-premises systems and applications.
Observe is an extension of the Moogsoft AIOps platform but can be deployed independently. An early access beta program will be available this November.
Additionally, Moogsoft announced that version 7.0 of the Moogsoft AIOps platform will be available this October. This release introduces Vertex Entropy and Situation Topology Visualization — two AI-based technologies that help IT teams understand the root cause of any customer-impacting problem through a visually rich system topology map. Together, Vertex Entropy and Situation Topology Visualization provide unparalleled insights into the root cause of any problem and full visibility into the complex relationships across IT processes, applications, and devices.
The Latest
In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...
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 ...