Virtana announced AI Factory Observability for Nutanix Agentic AI environments, extending system-aware observability across Nutanix Cloud Infrastructure and Nutanix Enterprise AI.
Virtana is expanding AI Factory Observability from Nutanix Cloud Infrastructure into Nutanix Enterprise AI, extending visibility and control from the infrastructure layer into AI platforms and model-driven workloads. This integration provides infrastructure and platform teams with a shared operational foundation for managing Nutanix Agentic AI environments in production.
“As enterprises adopt the Nutanix Agentic AI platform to build and run intelligent, distributed AI systems, understanding how those workloads behave across infrastructure and services becomes critical,” said Luke Congdon, VP of Product Management at Nutanix. “Virtana’s extension of observability into Nutanix Enterprise AI helps provide that visibility, enabling organizations to operate AI factories with greater performance, efficiency, and control.”
“Enterprises have proven they can stand up AI infrastructure,” said Paul Appleby, CEO of Virtana. “The challenge now is operating agentic AI environments where systems reason, adapt, and act across distributed resources. These are dynamic systems that demand full-stack visibility and control to optimize GPU utilization, manage cost efficiency, and support thousands of concurrent agents with the performance and governance required for production at scale.”
Nutanix Agentic AI establishes a broader platform for building and operating AI factories. Within that architecture, Nutanix Enterprise AI is the layer where models, agent services, and enterprise AI workflows are deployed, connected, and scaled. As these environments move into production, understanding how AI services behave across the full stack becomes the defining operational challenge, spanning inference performance, GPU consumption, infrastructure contention, and system reliability.
“AI workloads are no longer static. They are increasingly agentic, continuously adapting how they consume infrastructure,” said Amitkumar Rathi, Chief Product Officer at Virtana. “By extending AI Factory Observability into Nutanix Enterprise AI, we give organizations end-to-end visibility and control across the layer where AI services are built and operated, while connecting that activity back to the infrastructure supporting it. Platform teams can manage performance, reliability, and cost with greater precision, and data teams gain the operational context required to run AI in production with confidence.”
Virtana AI Factory Observability spans Nutanix Cloud Infrastructure into Nutanix Enterprise AI in a single operational view, giving infrastructure and platform teams the visibility they need to understand and manage the full environment powering agentic AI. Teams can correlate AI workload behavior with the underlying compute, GPU, storage, and orchestration resources required to run it, connecting signal across every layer into a coherent picture of system performance.
Virtana addresses this new operational requirement by delivering:
- Real-time GPU telemetry, including utilization, memory, power draw, temperature, and health across distributed clusters
- Detection of idle and underutilized GPUs to reduce waste and cost and improve AI infrastructure efficiency
- Workload-to-GPU correlation across training, inference, and agent-driven workflows, connecting AI service behavior to infrastructure usage and cost
- Token-level visibility into throughput, latency, and resource demand so teams can better understand cost and performance under concurrency
- Early identification of thermal, power, and reliability risks before they affect production AI services
- Performance analysis for multi-node, multi-GPU environments supporting dynamic agentic workloads
- A unified operational view across Nutanix AHV, Nutanix Enterprise AI, Kubernetes orchestration, NVIDIA GPU clusters, and distributed AI workflows, enabling end-to-end observability across the AI factory
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