Artificial Intelligence (AI) is reshaping observability, and observability is becoming essential for AI. This is a two-way relationship that is increasingly relevant as enterprises scale generative AI.

On one side, observability platforms are embedding AI capabilities to help engineers make sense of overwhelming telemetry. From anomaly detection to natural-language copilots, AI is accelerating how quickly teams can triage and resolve issues.

On the other side, AI applications themselves introduce new observability challenges. Large language models (LLMs) depend on GPUs, where utilization and memory must be tracked for stable performance. At the model level, latency and throughput metrics reveal inference efficiency. And at the application layer, especially in retrieval-augmented generation (RAG) systems, teams need tracing that shows how retrieval and generation affect both response time and answer quality.

This dual role makes AI and observability inseparable. In this blog, I cover more details of each side.

AI for Observability

Observability has always involved finding meaning in massive amounts of telemetry. AI is now helping reduce that burden.

Machine Learning Features

Machine learning has long been embedded in observability tools. Common capabilities include:

• Anomaly detection - highlighting unusual patterns in large volumes of telemetry data.
• Root cause analysis - correlating signals across distributed systems to suggest likely failure points.
• Alert optimization - reducing alert fatigue and enabling more intelligent, proactive, and less noisy incident management.
• Log clustering - turning millions of log lines into a handful of recognizable patterns. Highlighting potential issues, trends for quick understanding.

These features have become foundational, allowing teams to react more quickly to system changes.

Conversational AI Assistants

The newer wave is LLM-powered assistants. By understanding natural language, they can:

• Explain telemetry in plain English.
• Suggest follow-up queries to guide investigation.
• Convert natural language into a platform's query syntax.

This reduces the learning curve for complex query languages and shortens the cycle from "signal observed" to "action taken."

Scope and Limitations

LLM copilots are valuable, but they do not replace human expertise. They may lack system context, generate plausible but wrong explanations, or increase costs if over-used. The best way to view them today is as an augmentation layer that accelerates engineers' judgment.

Cost Considerations

Embedding LLM copilots inside observability platforms comes with a usage-based cost, since most models are billed per token. Organizations should treat this as an observability signal, tracking how often copilots are used, how many tokens are consumed per incident, and whether costs are proportional to the value provided.

Observability for AI

If AI improves how we observe, AI workloads demand new ways of being observed. Enterprises deploying LLMs and RAG-based systems quickly discover that traditional metrics are not enough.

Monitoring GPUs

GPUs are the backbone of LLM inference, performing the parallel matrix operations that make real-time responses possible. Key metrics include:

• Utilization - to detect both under-use (waste) and saturation (bottlenecks).
• Power draw and temperature - to prevent instability or throttling.
• Memory usage - to avoid out-of-memory errors, especially with long prompts.
• Active sessions - to understand contention in multi-tenant setups.

Monitoring these metrics helps correlate infrastructure behavior with application performance.

Monitoring LLMs

At the model level, important metrics include:

• Time to First Token (TTFT) - responsiveness from the user's perspective.
• Token throughput and latency - how efficiently the model serves requests.
• Request throughput - overall system capacity.

These provide a direct view into whether a model can serve queries at scale without degradation.

Monitoring RAG Applications

RAG systems introduce their own observability needs because they combine multiple steps — retrieval, embedding, ranking, and generation. Useful signals include:

• Pipeline metrics - vector database query latency, embedding generation time, retrieval hit ratio.
• End-to-end traces - linking the user query through retrieval to the final generated response.
• User-centric signals - session completion rates, repeated queries, or feedback scores that reveal quality from the end user's perspective.

Variability and Drift

Unlike traditional apps, AI systems can behave inconsistently, producing variable outputs even for identical inputs. Teams need to monitor for variability in responses or gradual quality drift. Vendors are beginning to add features that detect embedding drift, prompt/output shifts, and regression in model behavior, all of which should trigger further analysis.

Cost of Observing AI Workloads

Telemetry itself can become a cost center when observing AI applications. High-cardinality signals such as token-level traces, vector embeddings, and RAG pipeline metrics can grow quickly in both storage and processing. Some teams already track "cost observability" dashboards alongside performance, to avoid overspending while still retaining useful visibility.

The Road Ahead

Concerns such as response inconsistencies, drift, and telemetry cost are no longer optional add-ons. They are becoming core requirements for AI observability. Meanwhile, AI capabilities within observability platforms will continue to evolve, not replacing engineers but giving them faster ways to interpret, explore, and act.

As organizations continue adopting AI, observability will play a dual role:

• AI enhancing how we observe.
• Observability ensuring AI can be trusted, optimized, and scaled responsibly.

Enterprises that prepare for both sides together will be better positioned to build resilient systems in the AI era.