Unique new capabilities help enterprises tame AI infrastructure complexity, boost resource efficiency, and bring predictability to industrial-scale AI operations.
Virtana, the recognized leader in hybrid infrastructure observability, introduced Virtana AI Factory Observability (AIFO) — a cutting-edge enhancement to its full-stack observability platform tailored specifically for the complex needs of AI infrastructure. By delivering comprehensive, real-time visibility into critical metrics such as GPU utilization, training bottlenecks, power consumption, and cost drivers, AIFO empowers enterprises to transform demanding AI environments into streamlined, scalable, and cost-effective operations.
This innovative launch reinforces Virtana’s reputation as the industry’s most expansive observability platform, covering AI, infrastructure, and applications across hybrid and multi-cloud environments.
“AI has the potential to be as transformative as the steam engine or the printing press—but only if enterprises can operationalize it at scale,” said Paul Appleby, CEO of Virtana. “Right now, too many teams are flying blind when it comes to AI infrastructure. Virtana AIFO gives them the visibility and control they need to treat AI not as an experiment, but as a core, strategic part of the business.”
In response to surging enterprise investments and heightened industry focus on scalable AI Factory infrastructure — driven by ecosystem leaders like NVIDIA — Virtana stands as the first to offer a full-stack observability solution purpose-built for AI Factory operations. As organizations shift from AI pilots into full production, the demand intensifies for platforms that provide deep, correlated insights spanning infrastructure, AI models, and cost factors — far beyond surface-level monitoring.
Industry analysts recognize this transformation as a pivotal trend: AI is no longer confined to research labs but is rapidly evolving into an essential operational foundation for businesses. Virtana’s AIFO is designed to meet this evolution head-on, enabling enterprises to manage AI infrastructure with the same precision, accountability, and discipline as traditional IT environments.
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As an official NVIDIA partner, Virtana offers native integration with NVIDIA GPU platforms, delivering detailed telemetry such as memory utilization, thermal profiles, and power consumption. This vendor-validated insight provides precise intelligence on the most performance-critical components of the AI Factory, supporting accurate and actionable decision-making at enterprise scale.
“AI workloads introduce an entirely different set of infrastructure challenges—from GPU saturation and training bottlenecks to unpredictable cost spikes,” said Amitkumar Rathi, Senior Vice President of Engineering, Product, and Support at Virtana. “We designed AIFO to address these realities head-on. It gives teams deep, correlated visibility across the full AI stack, enabling them to optimize performance, reduce waste, and scale AI with confidence.”
With this launch, Virtana tackles the escalating infrastructure challenges that hinder scalable AI adoption. As enterprises ramp up AI investments, they frequently face hidden inefficiencies such as idle GPUs driving up costs, inexplicable training job failures, and stalled inference pipelines caused by storage or network bottlenecks. AIFO is purpose-built to resolve these issues by providing real-time, correlated insights across every layer of AI infrastructure. The outcome is enhanced control over performance, spending, and scalability — transforming AI from a risky experiment into a strategic, high-impact capability.
Purpose-Built Observability for AI Infrastructure
Unlike traditional monitoring tools built for general IT workloads, Virtana AI Factory Observability (AIFO) is purpose-built to meet the demands of AI operations. It continuously collects telemetry across GPUs, CPUs, memory, network, and storage and then correlates that data with training and inference pipelines to provide clear and actionable insights.
Core capabilities include:
- GPU Performance Monitoring – Tracks per-GPU metrics such as memory, utilization, thermal load, and power draw across multiple vendors.
- Distributed Training Visibility – Identifies bottlenecks, synchronization issues, and stragglers across multi-node jobs.
- Infrastructure-to-AI Mapping – Correlates model-level performance directly to hardware-level behavior, including network and storage dependencies.
- Power and Cost Analytics – Exposes inefficiencies such as thermal throttling, idle GPU time, and overprovisioning resources.
- Root Cause Analysis – Diagnoses training failures and inference slowdowns faster by pinpointing the most likely infrastructure causes.
All capabilities are accessible via Virtana’s Global View dashboard, which unifies telemetry across hybrid and containerized AI environments—on-premises, cloud, or both.
