AI is transforming industries, but it’s also breaking infrastructure in the process.
As organizations scale their use of generative AI, large language models (LLMs), and inferencing pipelines, the demands on infrastructure have exploded. Hybrid environments are now juggling thousands of training jobs, real-time inferencing services, and massive data pipelines, all while relying on GPUs, high-speed storage, and network backbones to perform flawlessly.
Unfortunately, the tools we’ve relied on to monitor traditional applications weren’t built for this new world. The result? Missed SLAs, idle GPU clusters, costly overprovisioning, and weeks lost to root-cause analysis when training jobs fail.
This is exactly why we built Virtana AI Factory Observability (AIFO)—a purpose-built solution that gives engineering, operations, and infrastructure teams the real-time visibility and intelligent insights they need to run AI workloads at scale.
The Business Problem: Infrastructure Blind Spots Are Undermining AI Investments
AI infrastructure is expensive. Whether you’re running jobs in a public cloud, on-premises, or across hybrid deployments, the cost of GPUs, accelerators, and power-hungry AI workloads adds up fast. But what’s more dangerous than the cost itself is the waste caused by poor visibility.
Here’s what we’re seeing inside enterprise AI data centers:
- Underutilized GPUs: Teams often assume they need more GPU capacity when the real issue is throttling, idle time, or inefficient job placement.
- Training job failures: Multi-node, distributed training pipelines crash due to silent network or hardware failures—things traditional monitoring can’t detect.
- Latency spikes and SLA misses: Inferencing jobs run hot, but the real culprit may lie deep in the storage or network stack.
- Overprovisioned resources: Without real-time correlation between infrastructure and workloads, teams overcompensate by over-allocating compute and storage that waste both budget and energy.
These issues don’t just impact engineering teams—they stall business outcomes. Every failed job, idle GPU, or undiagnosed latency spike delays time to insight, slows product innovation, and drives up costs.
Why AI Factory Observability Requires a Different Approach
AI isn’t like traditional IT. AI workloads are dynamic, data-intensive, and deeply distributed. They span multiple layers—from containerized applications and orchestration engines to physical hardware like GPUs, NVMe arrays, and high-speed Ethernet fabrics.
To operationalize AI at scale, observability must be:
- Correlated: You need to connect app behavior to GPU performance, network congestion, and storage delays—all in one view.
- Real-time: Latency during training or inference can’t wait for postmortem analysis.
- Context-aware: It’s not enough to know something is slow—you need to know why, and where to fix it.
With AIFO, we deliver all of this through a unified observability fabric that spans the AI stack—from application requests to GPU telemetry and everything in between.
AI Is a Factory—And Needs to Be Run Like One
It’s no accident we call it AI Factory Observability. AI development isn’t just about algorithms and GPUs—it’s an end-to-end supply chain that turns raw data into intelligent decisions. Like any factory, it depends on the smooth coordination of inputs, processing, quality control, and distribution.
Your data pipelines are the raw materials. Your model training environments are the production lines. Inference is your finished product hitting the market. And just like in physical manufacturing, if one component—storage, compute, network, or orchestration—fails or slows down, the entire pipeline suffers.
In a traditional factory, operators rely on industrial telemetry to monitor throughput, detect breakdowns, optimize energy consumption, and ensure output quality. AI infrastructure needs the same level of operational oversight. Without it, you’re flying blind—overprovisioning resources, reacting to failures after the fact, and struggling to meet demand.
That’s why we built AIFO to provide complete transparency across the AI supply chain, so enterprises can treat AI not as an experiment, but as a production-grade system that demands efficiency, accountability, and precision.
Introducing Virtana AI Factory Observability (AIFO)
Virtana AIFO is a full-stack observability solution designed specifically for AI infrastructure. Here’s what makes it different:
Real-Time GPU Monitoring and Telemetry
We collect deep metrics on GPU utilization, memory bandwidth, ECC errors, temperature, power draw, and throttling. Whether you’re using NVIDIA or AMD, on-prem or in cloud, you can see exactly how your GPUs are performing—down to the pod and host level.
End-to-End Trace Analysis Across AI Pipelines
Using OpenTelemetry tracing, we monitor AI workloads as they move across services, nodes, and infrastructure layers. If an inference call is slow or a training job crashes, AIFO tells you not just what happened, but why—and where in the stack the issue occurred.
Cross-Layer Correlation and Root Cause Insights
AIFO correlates performance issues across compute, network, and storage—helping teams quickly identify whether the root cause lies in the application layer, container orchestration, or underlying hardware.
Predictive Insights and Optimization Recommendations
From idle GPU detection to power-aware workload placement, AIFO helps optimize both performance and cost. We’re already helping customers reduce GPU waste by 40%, improve MTTR by 60%, and lower power usage by 15%.
AI Infrastructure Observability Without Replacing What You Have
AIFO works with your existing Kubernetes environments, OpenTelemetry integrations, and infrastructure monitoring. Whether you’re running AI workloads on high-end GPU racks or repurposed CPU clusters, AIFO delivers value.
Use Case Spotlight: Diagnosing a Slow AI Service
Imagine your AI summarization service is suddenly lagging. A traditional monitoring tool might alert you to high latency, but it stops there. With AIFO, you get the full picture:
- Trace-level alert in Global View: AIFO detects that a particular inference trace is taking longer than expected.
- Correlate to Kubernetes pod: See which pod, GPU, and host are responsible—and whether the issue is local or distributed.
- Jump to infrastructure view: Inspect GPU temperature, power draw, network throughput, and switch health—all tied to the same time window.
- Root cause revealed: In this case, a competing job triggered GPU contention, leading to throttling and higher latency.
- Actionable insights: The system recommends scheduling the jobs to avoid overlap or adjusting GPU allocation to balance the load.
This is just one of many real-world scenarios where AIFO helps teams move from guesswork to resolution in minutes.
Why Most AI Observability Tools Fall Short
As AI adoption surges, we’re seeing a wave of new entrants and traditional monitoring vendors rebranding themselves for AI. But most of these tools only tell part of the story.
Some focus solely on the AI application layer, tracking model outputs, inference latency, or pipeline errors, but they have no visibility into what’s happening underneath. Others come from traditional infrastructure monitoring backgrounds but weren’t designed to understand AI-specific workloads like distributed training, GPU contention, or model synchronization.
What’s missing is the AI orchestration layer—where Kubernetes, job schedulers, and containerized workloads manage the dynamic movement of training and inference jobs across the environment. Without visibility here, teams are blind to resource allocation issues, scheduling conflicts, and runtime inefficiencies that directly impact performance and cost.
Even fewer tools can correlate issues across all three layers—application, orchestration, and infrastructure—to provide a true root cause. Even among tools that claim infrastructure insights, the scope is often narrow and limited to GPU or CPU server metrics. But AI workloads don’t operate in isolation. Performance issues are just as likely to originate in backend storage systems, compute fabrics, or the high-speed networks that move data between nodes. Few platforms monitor the full topology, including storage arrays, IOPS and bandwidth, PCI bus saturation, switch performance, and both in-band and out-of-band management networks. Without this complete picture, these tools miss the subtle bottlenecks and configuration mismatches that silently erode AI performance and reliability. That’s where Virtana stands apart.
Virtana AIFO was purpose-built to connect these layers in real time, giving you a unified view of your AI operations from metal to ephemeral. It’s not just observability—it’s operational control for AI factories at scale.
Designed for Scale, Built for Impact
Virtana AIFO isn’t just for enterprises with hyperscale AI labs. Whether you’re experimenting with small models on CPUs or managing massive GPU clusters, AIFO is designed to help you:
- Prevent training and inference failures
- Maximize existing infrastructure investments
- Optimize energy usage and reduce carbon footprint
- Improve service delivery and business outcomes
From healthcare and financial services to manufacturing and government, we’re helping customers operate their AI infrastructure more intelligently and efficiently—without overhauling their existing environments.
Looking Ahead
This is just the beginning. We’re building toward deeper automation, tighter integrations, and smarter recommendations that not only surface problems, but fix them.
Our long-term goal? To give every enterprise the observability they need to run AI like a factory: efficient, resilient, and fully accountable.
If your team is pushing the limits of what AI can do, it’s time to take control of what’s powering it.
Are you ready to learn more? Click here to request a demo or click here to explore Virtana AI Factory Observability.
Amit Rathi
SVP of Product and Engineering
