Unlock Full-Stack Visibility for Your AI Factory
Virtana’s AI Factory Observability (AIFO) gives you real-time, infrastructure-to-AI workload insights—so you can eliminate blind spots, reduce downtime, and drive better outcomes from your AI investments.
40% Reduction in Idle GPU Time
Real-time visibility and optimization lowered GPU underutilization across environments.Global FSI Customer
60% Faster Root-Cause Diagnosis
AIFO cut MTTR in half by tracing AI performance issues to infrastructure bottlenecks.Healthcare Provider
15% Lower Power Usage
Energy analytics revealed throttled GPUs, enabling targeted optimization and cost savings.AI Lab – USA
Correlate Infrastructure with AI Performance
- Map AI jobs to the exact GPU, host, network, and storage resources they use.
- See every step in the AI pipeline—from data ingress to model inference.
- Integrates Virtana Container Observability, Infrastructure Observability, and third-party monitoring tools for seamless top-down visibility across layers.
Accelerate RCA for AI Training Failures
- Reduce MTTR by pinpointing the exact cause of training job failures.
- Surface hardware issues like port resets, GPU throttling, or switch outages.
- Save days of debugging time and avoid repeated training runs.
Monitor GPU Health and Performance at Scale
- Track utilization, thermal metrics, ECC errors, memory consumption, and tensor core activity.
- Gain visibility across heterogeneous environments, including NVIDIA.
- Identify resource contention before it impacts model performance.
Visualize Distributed AI Workloads and Bottlenecks
- Profile multi-node training jobs to uncover stragglers or sync delays.
- View end-to-end traces with OpenTelemetry to correlate app behavior and infra impact.
- Understand how infrastructure variations affect performance and cost.
Identify Storage and Network Issues Impacting AI Pipelines
- Detect hidden IO latency, packet loss, or switch port congestion.
- Monitor bandwidth and throughput tied directly to model input/output performance.
- Prevent pipeline degradation that can slow down inference or retraining.
Improve Resource Efficiency and Cost Control
- Detect idle and overutilized GPUs, misconfigured environments, or underutilized hosts.
- Optimize infrastructure allocation to avoid overprovisioning.
- Reduce cloud and data center spend without compromising performance.
Plan with Confidence Using Real-Time Telemetry
- Analyze usage trends for GPUs, network, and storage to inform capacity planning.
- Make proactive decisions with data-driven recommendations.
- Prepare infrastructure for evolving AI demands across hybrid environments.
