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Virtana Announces Support for Sovereign Cloud Operations

Virtana announced comprehensive support for sovereign cloud operations, extending its system-aware agentic observability platform to organizations that require complete control over where data resides, how infrastructure is operated, and how mission-critical services are governed. 

Virtana provides a sovereign-by-design architecture that combines deep full-stack observability with system-aware agentic AI, enabling organizations to understand, optimize, and autonomously operate complex sovereign cloud environments across public cloud, private cloud, on-premises infrastructure, and air-gapped deployments.

“Cloud sovereignty is being reshaped by forces that extend well beyond data residency,” said Paul Appleby, CEO of Virtana. “As AI scales alongside growing enterprise complexity, organisations must demonstrate not only where data resides, but how AI systems operate, who governs them, and whether they perform reliably across hybrid and multi-cloud environments. In the UK and Europe, operational accountability has become the defining measure of cloud sovereignty.”

Virtana provides a unified operational platform that preserves sovereign control while enabling autonomous operations through system-aware intelligence. Rather than layering AI on top of disconnected telemetry, Virtana’s agentic AI reasons across the complete operational context, identifying constraints, proving root cause, recommending corrective actions, and helping operators maintain compliance and service continuity without sacrificing sovereignty.

Virtana deploys across sovereign, private, hybrid, public cloud, and air-gapped environments, keeping sensitive operational data within approved jurisdictions while delivering full-stack agentic observability across compute, storage, networking, virtualization, Kubernetes, applications, and AI infrastructure. Continuous visibility into workload placement, policy adherence, and operational governance means compliance is auditable in real time, not reconstructed after the fact. Policy-driven administration, in concert with role-based access control solutions, extends that governance into secure operational workflows. Organizations running sovereign infrastructure alongside commercial cloud environments manage both from a single system-aware platform, with agentic AI continuously identifying operational risk, predicting emerging constraints, and accelerating root cause analysis before services are impacted.

“Agentic AI is only as effective as the operational context it understands,” said Amitkumar Rathi, Chief Product Officer at Virtana. “Our platform gives AI agents complete visibility across sovereign infrastructure, applications, services, and AI workloads while respecting data residency, governance, and security boundaries. This enables organizations to move from reactive operations to intelligent autonomous operations without relinquishing control.” 

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For decades, traditional observability tools have forced IT professionals to sift through endless dashboards, turning a flood of alerts into actionable insight and driving widespread burnout. While enterprises adopted AI to eliminate this friction, most still deploy it reactively rather than proactively ... As systems become more complex and IT moves to the center of business strategy, a reactive posture is unsustainable ...

Ask an operations team what they monitor and you will hear a familiar list: latency, errors, throughput, uptime. Those signals still matter for AI, but they no longer answer the most important question. As Krishna Sai, CTO of SolarWinds, puts it: "Traditional observability tells you a system is running. AI observability tells you whether it's still reasoning soundly. Those are completely different questions" ...

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Virtana Announces Support for Sovereign Cloud Operations

Virtana announced comprehensive support for sovereign cloud operations, extending its system-aware agentic observability platform to organizations that require complete control over where data resides, how infrastructure is operated, and how mission-critical services are governed. 

Virtana provides a sovereign-by-design architecture that combines deep full-stack observability with system-aware agentic AI, enabling organizations to understand, optimize, and autonomously operate complex sovereign cloud environments across public cloud, private cloud, on-premises infrastructure, and air-gapped deployments.

“Cloud sovereignty is being reshaped by forces that extend well beyond data residency,” said Paul Appleby, CEO of Virtana. “As AI scales alongside growing enterprise complexity, organisations must demonstrate not only where data resides, but how AI systems operate, who governs them, and whether they perform reliably across hybrid and multi-cloud environments. In the UK and Europe, operational accountability has become the defining measure of cloud sovereignty.”

Virtana provides a unified operational platform that preserves sovereign control while enabling autonomous operations through system-aware intelligence. Rather than layering AI on top of disconnected telemetry, Virtana’s agentic AI reasons across the complete operational context, identifying constraints, proving root cause, recommending corrective actions, and helping operators maintain compliance and service continuity without sacrificing sovereignty.

Virtana deploys across sovereign, private, hybrid, public cloud, and air-gapped environments, keeping sensitive operational data within approved jurisdictions while delivering full-stack agentic observability across compute, storage, networking, virtualization, Kubernetes, applications, and AI infrastructure. Continuous visibility into workload placement, policy adherence, and operational governance means compliance is auditable in real time, not reconstructed after the fact. Policy-driven administration, in concert with role-based access control solutions, extends that governance into secure operational workflows. Organizations running sovereign infrastructure alongside commercial cloud environments manage both from a single system-aware platform, with agentic AI continuously identifying operational risk, predicting emerging constraints, and accelerating root cause analysis before services are impacted.

“Agentic AI is only as effective as the operational context it understands,” said Amitkumar Rathi, Chief Product Officer at Virtana. “Our platform gives AI agents complete visibility across sovereign infrastructure, applications, services, and AI workloads while respecting data residency, governance, and security boundaries. This enables organizations to move from reactive operations to intelligent autonomous operations without relinquishing control.” 

The Latest

For decades, traditional observability tools have forced IT professionals to sift through endless dashboards, turning a flood of alerts into actionable insight and driving widespread burnout. While enterprises adopted AI to eliminate this friction, most still deploy it reactively rather than proactively ... As systems become more complex and IT moves to the center of business strategy, a reactive posture is unsustainable ...

Ask an operations team what they monitor and you will hear a familiar list: latency, errors, throughput, uptime. Those signals still matter for AI, but they no longer answer the most important question. As Krishna Sai, CTO of SolarWinds, puts it: "Traditional observability tells you a system is running. AI observability tells you whether it's still reasoning soundly. Those are completely different questions" ...

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...