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Moogsoft Announces Focus on Cloud AIOps Architecture to Deliver Agility and Scale

AIOps pioneer drives vision for the future of "AIOps Everywhere" with rapid-deployment and self-service model

Moogsoft announced its new cloud AIOps architecture and product vision, focused on delivering agile and scalable AIOps to companies of all sizes.

As part of this vision, the company is unveiling the results of its investment in a rapid-deployment and self-service model for delivering advanced AI and ML at scale to existing and new customers. This includes the creation of an entirely new architecture based around the latest developments in modern microservices and cloud-based technologies.

As every business embraces a fully digital future, organizations of all sizes increasingly rely on digital infrastructure, and in turn, on DevOps and SRE teams to operate business-critical digital services. To best serve this accelerated evolution towards a digital economy, Moogsoft will increase its focus towards a scalable, self-service architecture and model that allows both new and existing customers to quickly deploy and see value from observability using AIOps.

“Moogsoft recently introduced the first dedicated DevOps solution in the AIOps market,” said Dennis Drogseth, VP, Enterprise Management Associates. “This innovation helps enable DevOps and SRE teams across organizations of any size to gain greater visibility and control over service assurance, and ultimately to spend more time developing innovative services.”

Moogsoft is empowering customers to accelerate the adoption of AIOps by allowing them to self-provision and self-service, including the ability to build their own integrations to anything, anywhere. This is required for any self-servicing AIOps platform to realize rapid value in minutes and hours, rather than months and years.

“Every business is currently accelerating its digital transformation, and seeking solutions like AIOps to help tackle the complexity and scale of operating digital services, while continuing to innovate,” said Moogsoft Founder and CEO Phil Tee. “It’s clear from this shift that the market needs a highly-scalable and agile observability and AIOps platform. We have addressed this need by accelerating our roadmap to deliver DevOps and SRE teams a self-service solution from which to rapidly deploy and automate observability across all their services.”

Tee concluded, “I am excited to see the realization of a project two years in the making to re-platform our 50 patents and leading edge technology for the new age. This move will send shockwaves through the AIOps community.”

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Moogsoft Announces Focus on Cloud AIOps Architecture to Deliver Agility and Scale

AIOps pioneer drives vision for the future of "AIOps Everywhere" with rapid-deployment and self-service model

Moogsoft announced its new cloud AIOps architecture and product vision, focused on delivering agile and scalable AIOps to companies of all sizes.

As part of this vision, the company is unveiling the results of its investment in a rapid-deployment and self-service model for delivering advanced AI and ML at scale to existing and new customers. This includes the creation of an entirely new architecture based around the latest developments in modern microservices and cloud-based technologies.

As every business embraces a fully digital future, organizations of all sizes increasingly rely on digital infrastructure, and in turn, on DevOps and SRE teams to operate business-critical digital services. To best serve this accelerated evolution towards a digital economy, Moogsoft will increase its focus towards a scalable, self-service architecture and model that allows both new and existing customers to quickly deploy and see value from observability using AIOps.

“Moogsoft recently introduced the first dedicated DevOps solution in the AIOps market,” said Dennis Drogseth, VP, Enterprise Management Associates. “This innovation helps enable DevOps and SRE teams across organizations of any size to gain greater visibility and control over service assurance, and ultimately to spend more time developing innovative services.”

Moogsoft is empowering customers to accelerate the adoption of AIOps by allowing them to self-provision and self-service, including the ability to build their own integrations to anything, anywhere. This is required for any self-servicing AIOps platform to realize rapid value in minutes and hours, rather than months and years.

“Every business is currently accelerating its digital transformation, and seeking solutions like AIOps to help tackle the complexity and scale of operating digital services, while continuing to innovate,” said Moogsoft Founder and CEO Phil Tee. “It’s clear from this shift that the market needs a highly-scalable and agile observability and AIOps platform. We have addressed this need by accelerating our roadmap to deliver DevOps and SRE teams a self-service solution from which to rapidly deploy and automate observability across all their services.”

Tee concluded, “I am excited to see the realization of a project two years in the making to re-platform our 50 patents and leading edge technology for the new age. This move will send shockwaves through the AIOps community.”

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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 ...