Virtana

Virtana is the leader in hybrid cloud and AI infrastructure observability. As enterprises race to build AI Factories, GPUs, distributed infrastructure, and LLM pipelines are creating operational systems too complex for traditional monitoring to explain. GPU dashboards may show utilization, but they rarely reveal why models stall, costs spike, or performance degrades under real workloads.

Virtana AI Factory Observability (AIFO) exposes what is actually happening inside modern AI systems. By correlating infrastructure, GPU activity, and AI workload behavior across hybrid environments, Virtana provides the operational context required to run AI infrastructure at scale.

Through integration with Nutanix Enterprise AI (NAI), Virtana connects Nutanix clusters and NVIDIA GPUs directly to the behavior of AI workloads running on them. Autonomous agents and natural language query allow teams to ask questions about system behavior and instantly surface root causes, eliminating manual troubleshooting and enabling faster, more reliable AI operations.

Benefits of the Partnership with Nutanix to End-Users

The Problem: Enterprises building AI factories often find that infrastructure metrics appear healthy while AI workloads fail, stall, or burn through budgets. GPU utilization alone does not explain model performance, token demand, or system bottlenecks. Without system-level context, teams spend hours manually correlating telemetry across infrastructure, orchestration layers, and AI pipelines just to understand what is happening.

The Solution: Virtana AIFO and Nutanix Enterprise AI close the operational visibility gap by connecting infrastructure performance with AI workload behavior and enabling AI-driven investigation.

• Unified Visibility: Observe GPUs, infrastructure, AI workloads, and LLM token consumption together instead of isolated metrics.

• Natural Language Investigation: Ask questions about system behavior and instantly surface insights across the stack.

• Autonomous Root Cause Analysis: AI agents trace issues across infrastructure and AI pipelines to pinpoint bottlenecks.

• Confident Scaling: Plan AI capacity and growth with clear insight into efficient utilization, demand, and performance.

• Proactive Control: Correlate token spikes with infrastructure saturation to catch bottlenecks before they breach SLAs.