Moogsoft has advanced observability in the cloud to meet the demands of a software-defined world with the release of Moogsoft Observability Cloud.
Available today, the Moogsoft Observability Cloud delivers DevOps practitioners and Site Reliability Engineers (SREs) self-service intelligent observability capabilities to begin surfacing actionable insights and performing advanced event management across their digital infrastructure in the time it takes to make a cappuccino.
Companies of all sizes must become agile enough to easily grow and evolve their business at the pace of customer demand, and to constantly deliver customers competitive value. However, teams suffer from unmanageable toolsets laid out in an attempt to understand what’s happening and why, with no real intelligence. The Moogsoft Observability Cloud empowers DevOps practitioners and SRE teams to continue innovating by providing deeper insight and automation across the process of monitoring and event management.
The Moogsoft Observability Cloud extends AI-based intelligence to raw observability data. It turns metric and event data into actionable insights by automating anomaly detection, surfacing important alerts and correlating everything together. This provides teams better visibility about their services, advanced warning of potential outages, and context about the incidents which cause them. With this information, teams can more efficiently collaborate, learn, improve, and innovate their services.
“Old-fashioned monitoring solutions, including many that claim to be new, lead to expensive investments that take months to deliver any results,” said Moogsoft Founder and CEO Phil Tee. “Meanwhile, businesses continue to rely on SREs for manual event management, and on their customers to alert them of an outage. The Moogsoft Observability Cloud empowers DevOps and SRE teams with a platform that deploys in the time it takes to wait for their cappuccino, but leverages the most advanced AI to redefine observability.”
Available today as a free trial, the Moogsoft Observability Cloud includes:
- A simple onboarding process with guided tours that provide valuable results in minutes, not days or weeks.
- A native collector that ingests time-series metric data directly from sources such as Amazon EC2, Docker, MongoDB, Redis and more. Other metrics can also be ingested via a user-definable metrics API.
- The ability for users to build their own integrations in order to ingest even their trickiest event and metric payloads.
- Correlation across all metric and event sources.
- Workflows that help teams easily reduce the noise generated by existing monitoring tools, allowing users to get to the root of an issue faster.
- Integrations with collaboration tools such as PagerDuty, and the ability to send data to an endpoint of the user’s choosing with an open webhook API.
The Moogsoft Observability Cloud delivers noise reduction, correlation, anomaly detection and more in just a few quick steps. Users simply ingest their observability and monitoring data and start optimizing their digital experience based on the insights surfaced. Organizations can leverage the Moogsoft Observability Cloud for IT Ops, DevOps, site reliability engineering as well as specialized uses such as hybrid cloud monitoring.
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 ...