Once upon a time, only tech giants and academic institutions worried about AI. Fast forward to today, and the story has flipped: AI is no longer optional—it’s inevitable.
Whether you’re a healthcare provider using predictive models to improve patient outcomes, a bank optimizing fraud detection with real-time inference, or a retailer personalizing customer journeys with recommendation engines, AI is embedded into your core business. Even if you’re not building your own models, you’re running AI-powered tools, integrating with LLMs, and depending on third-party APIs that use AI under the hood.
In short, every company is now an AI company. But being AI-first isn’t just about deploying models or signing a licensing deal with OpenAI or NVIDIA. The real differentiator is something less flashy, more foundational—and absolutely essential:
Observability
Observability in the AI era is how you see, understand, and optimize your intelligence pipelines. It’s how you gain control over increasingly complex systems and ensure your AI delivers real value. Without it, you’re scaling in the dark.
So what do successful AI companies have in common? They invest early—and aggressively—in observability that’s purpose-built for AI.
Here are five best practices for observability to stay competitive and future-proof your AI transformation.
1. Treat GPUs and AI Infrastructure as First-Class Citizens
In traditional IT, infrastructure monitoring focuses on CPU usage, memory, disk I/O, and network throughput. But in AI, the GPU is the heartbeat of performance. Without real-time insight into GPU health and utilization, you’re essentially trying to win a Formula 1 race with your dashboard blacked out.
Key Observability Actions:
- Track GPU utilization by job, process, and user
- Visualize memory saturation, thermal thresholds, and compute/memory ratios
- Observe Multi-Instance GPU (MIG) partitions and resource fragmentation
- Correlate GPUs to pods, containers, and AI frameworks (e.g., PyTorch, TensorFlow)
Imagine a training job running for 72 hours, only to fail because of out-of-memory errors that could’ve been prevented with proactive observability. Or an inference service that silently degrades because one GPU is underperforming due to thermal throttling.
Why it matters: GPUs aren’t just a resource—they’re a bottleneck, a cost centre, and a competitive advantage. Visibility here is non-negotiable.
2. Observe the Behaviour of AI Models, Not Just Infrastructure Health
Your app is “up,” but the model inside it is confidently giving wrong answers. Sound familiar?
Traditional observability focuses on infrastructure uptime and application responsiveness. But AI models can fail silently—by degrading in accuracy, drifting from expected behavior, or generating biased or nonsensical results.
What to Monitor:
- Input/output patterns of models (e.g., prompt/response pairs for LLMs)
- Token usage trends and spikes
- Inference latency by request type
- Output variability, hallucination rates, or classification drift
Why it matters: You need more than uptime dashboards—you need cognitive dashboards that reflect how your AI thinks.
3. Unify Multi-Modal Telemetry Across the AI Stack
The modern AI pipeline isn’t a single system—it’s a stack of moving parts:
- Data pipelines (e.g., ingestion, ETL, feature engineering)
- Training platforms (e.g., distributed compute clusters)
- Serving endpoints (e.g., REST APIs for model inference)
- Business applications (e.g., chatbots, fraud detectors)
- External APIs (e.g., OpenAI, Anthropic, Hugging Face models)
Each layer generates telemetry—metrics, logs, traces, events. But the real magic happens when these signals are unified to tell a coherent story.
Unified Observability Approach:
- Correlate token usage spikes with customer traffic
- Trace slowness in an LLM chatbot to queue latency on specific GPUs
- Connect data quality degradation to model performance drops
- Link model response issues to upstream ETL or schema drift
Without end-to-end observability, your AI team plays a never-ending game of blame-ping-pong: Is it the model? The data? The API? The infrastructure?
Why it matters: Observability is your truth layer—the only way to make sense of a highly distributed AI system.
4. Define AI-Native SLIs, SLOs, and Alert Policies
In traditional observability, we talk about service-level indicators (SLIs) and service-level objectives (SLOs) like response time, error rate, and uptime.
In AI, your SLOs must evolve to reflect model-level outcomes and AI-specific risks.
Examples of AI-Native SLIs:
- Prompt latency percentiles (P95, P99)
- Inference throughput per second
- Output validity or hallucination rate
- Token consumption per query/user
- Model accuracy or precision in real-time predictions
You can then set corresponding SLOs and policies:
- Alert if average prompt latency exceeds 500ms for more than 5 minutes
- Trigger a rollback if hallucination rate exceeds 3% in customer-facing apps
- Send anomaly alerts when token usage deviates >20% from baseline
Why it matters: You can’t improve what you can’t measure. And in AI, your goals must match your algorithms.
5. Enable Explainability and Auditability at Every Layer
Trust in AI is earned. Especially in regulated industries like healthcare, finance, and government, AI cannot remain a black box.
That’s why observability must include explainability and traceability—so you can answer:
- What data was this prediction based on?
- Which model version made this decision?
- How has the model changed over time?
- Can I recreate the input/output sequence for this event?
Key Features to Build:
- Input-output logging for model APIs (with privacy controls)
- Version-controlled metadata on training sets and model weights
- Visual explanations of predictions
- Easy-to-understand dashboards for non-technical stakeholders
In the AI age, observability isn’t just for engineers—it’s also for legal teams, ethics committees, business owners, and customers.
Why it matters: AI decisions must be defensible. Transparency is not a nice-to-have—it’s a survival skill.
Final Thoughts: The AI-Observability Flywheel
As AI adoption accelerates, observability becomes your force multiplier. The more visibility you have, the better your:
- Model performance and tuning
- Resource efficiency (especially expensive GPUs)
- Issue detection and root cause analysis
- Regulatory compliance and trust
- Competitive advantage in time-to-market
- In short, observability gives you superpowers—not just to operate AI, but to scale it responsibly and successfully.
Every company is an AI company now.
But the ones who win will be the ones who can actually see what their AI is doing.
Are you one of them? If you are looking for a full stack AI observability solution, Virtana can help.
Book a demo with Virtana and unlock the full power of your AI Factory.
Author Bio
Meeta Lalwani is a director of product management professional leading the AI Factory Observability and GenAI Portfolio for Virtana Platform. She is passionate about modern technologies and their potential to positively impact human growth.
