Virtana Launches AI Factory Observability to Enhance AI Infrastructure Management
Virtana introduces AI Factory Observability (AIFO), a new feature in its unified observability platform. AIFO helps operations teams manage AI infrastructure complexity by providing real-time telemetry across GPUs, networks, and storage. This allows teams to move beyond reactive incident handling and focus on proactive optimization as organizations scale AI from pilots to production.
From Reactive Response to Continuous Optimization
AIFO enables early detection of infrastructure issues before they impact AI model performance or increase costs. By correlating GPU usage, thermal data, network throughput, and storage latency with AI workloads, it surfaces the most relevant infrastructure signals. This supports ongoing improvement throughout the AI lifecycle instead of just fixing problems as they occur.
Powered by Virtana’s agent-based Behavior Analysis, AIFO learns patterns based on time and usage. It alerts teams to deviations, allowing quick fixes before users are affected. The platform builds live dependency maps from multiple data sources, dynamically optimizes resources, and identifies configuration problems that could slow data throughput during AI training or inference. Integrated AI agents can also automate policy enforcement, trigger remediation, notify site reliability engineers, or log tickets in tools like ServiceNow.
Supporting Compliance and Control in Regulated Environments
As a certified NVIDIA partner, Virtana offers native telemetry for GPU environments and integrates with Zenoss to connect AI infrastructure metrics with application and cloud service performance. This full-stack traceability is vital for multi-tenant and regulated settings where observability must meet audit, compliance, and cost control demands.
Operations teams need to understand not just when a model fails but why it happened, which infrastructure components contributed, and how to prevent it from recurring. Correlated telemetry and full workload lineage provide these insights. The platform supports SaaS or on-premises deployment with tenant-level data segregation and customer-managed large language models (LLMs), making it suitable for security-sensitive sectors.
With Zenoss integration, Virtana ingests data from diverse cloud services, correlating application insights with infrastructure telemetry to deliver business-level visibility. This helps organizations monitor experimentation costs, measure ROI, and detect unintended effects on production systems.
Enabling MSSPs to Operationalize AI Observability
Managed security service providers (MSSPs) benefit from AIFO’s API-driven, modular architecture. They can customize the platform to fit client environments, manage multi-tenant visibility, and implement policy-based alert responses. Integration with version-controlled code repositories supports alert remediation policies as code.
MSSPs can aggregate telemetry, provide curated client dashboards, and offer infrastructure optimization as a measurable service, improving scalability and client satisfaction.
A Unified Platform for Traditional IT and AI Workloads
As MSSPs monitor everything from legacy systems to containerized AI pipelines across hybrid and multi-cloud environments, a unified observability solution becomes essential. Combining AIFO with Zenoss, Virtana delivers a comprehensive view across traditional IT infrastructure and AI operations.
- Per-GPU performance metrics
- Distributed job profiling
- AI-to-storage mappings
These features are critical for managing complex, compute-intensive workloads. Delivered through a modular, API-first architecture, the Virtana Platform covers infrastructure monitoring, cloud service visibility, application insights, hybrid cost management, and now AI Factory Observability.
Being able to view AI applications, orchestration, and infrastructure in one place aligns multiple teams and speeds up business transformation. This reduces tool overlap, improves service accountability, and supports innovation across operations. MSSPs can leverage this to deliver scalable, client-facing insights across diverse environments.
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