Virtana has launched AI Factory Observability (AIFO), a new addition to its unified observability platform, aimed at helping enterprises manage the growing complexity of AI infrastructure at scale. AIFO delivers real-time telemetry across GPUs, networks, and storage systems, enabling engineering and operations teams to move beyond reactive incident response and adopt a proactive optimization approach. As organizations shift from AI pilot projects to production environments, AIFO offers the insight required to optimize performance, contain costs, and ensure service reliability.
From Reactive Response to Continuous Optimization
“AIFO gives enterprises the technical foundation to shift left—identifying infrastructure issues before they affect model performance or costs,” said
Amit Subhedar, Director and Head of India Operations at Virtana. “By correlating GPU utilization, thermal metrics, network throughput, and storage latency with AI workloads, AIFO surfaces the infrastructure signals that matter most. This enables continuous improvement across the AI lifecycle, not just episodic troubleshooting.”
AIFO’s intelligence is powered by Virtana’s agent-based “Behavior Analysis,” which builds patterns based on time-of-day and usage type. It alerts teams when deviations occur, allowing for rapid remediation before user impact. Real-time correlation from multiple data sources enables the platform to build live dependency maps, dynamically optimize underutilized resources, and identify configuration issues that may affect data throughput during training or inference. Integrated AI agents also enable automated policy enforcement, from triggering remediation actions to notifying SREs or logging tickets in tools like ServiceNow.
Supporting Compliance and Control in Regulated Environments
As a certified NVIDIA partner, Virtana delivers native telemetry from GPU environments while leveraging its recent
Zenoss acquisition to connect AI infrastructure with application and cloud service performance. This integration supports full-stack workload traceability—essential for multi-tenant and regulated environments where observability must align with audit, compliance, and cost accountability requirements.
“It’s no longer enough to know a model failed,” Subhedar noted. “Teams need to know why, what part of the infrastructure contributed, and how to prevent recurrence. That’s where correlated telemetry and full workload lineage come into play.”
The platform is available in SaaS or on-premises deployments, with tenant-level data segregation and support for customer-managed LLM models, making it adaptable to security-sensitive sectors. With Zenoss, Virtana can ingest data from a wide range of cloud services and correlate application-level insights with infrastructure telemetry to provide business-level visibility—enabling organizations to track experimentation costs, ROI, and unintended impacts on production systems.
Enabling MSSPs to Operationalize AI Observability
Managed security service providers (MSSPs) can also benefit from AIFO’s architecture. The platform is API-driven and supports a modular deployment model, allowing MSSPs to tailor capabilities based on client environments. Its multi-tenant design enables policy-based visibility and alert response, while integration with version-controlled code repositories supports alert remediation policies as code.
“MSSPs can aggregate telemetry, expose curated dashboards to clients, and deliver infrastructure optimization as a measurable and scalable service,” said Subhedar.
A Unified Platform for Traditional IT and AI Workloads
As managed service providers (MSSPs) take on the challenge of monitoring everything from legacy systems to containerized AI pipelines across hybrid and multi-cloud environments, the need for a unified observability platform has become critical. By combining AIFO with Zenoss, Virtana offers MSSPs and enterprises a unified view of both traditional IT infrastructure and modern AI operations. This includes per-GPU performance metrics, distributed job profiling, and AI-to-storage mappings—all essential for tracking and optimizing complex, compute-intensive workloads.
Delivered through a modular, API-first architecture, the Virtana Platform supports a wide range of observability needs including infrastructure monitoring, cloud services visibility, application-level insights, hybrid cost management, and now, AI Factory Observability.
“Having the ability to understand your AI applications, AI orchestration, and AI infrastructure all from one place can help multiple application and infrastructure teams to be on the same page and accelerate transformation in the business,” said Subhedar.
The result is an end-to-end solution that reduces tool sprawl, enhances service-level accountability, and supports business-wide innovation—making it ideally suited for MSSPs seeking to deliver scalable, client-facing insights across a broad operational landscape.
