AI workloads inherit all the complexity of cloud native systems—scale, cost, and distributed architectures—while adding new risks like GPU saturation, token economics, hallucinations, and model drift. Site reliability engineers now own AI incidents as well as infrastructure outages, making observability more critical than ever. The key is control: focusing on the signals that matter, setting clear SLOs for reliability, cost, and safety, and applying observability strategies tailored to the four major AI market segments (Model Builders, GPU Providers, AI-Natives, and Feature Builders).

This blog kicks off our 5-part series on observability for artificial intelligence (AI), offering practical guidance for leaders and engineers to stay in control of cost, complexity, and reliability as they scale AI workloads. This blog is part one of the series, explaining the critical need for and increasing complexity of observability for AI.

Part 1: 5 keys to AI observability, Part 1 (You are here)
Part 2: Model Builders: 5 keys to AI O11y, Part 2
Part 3: GPU providers: 5 keys to AI O11y, Part 3
Part 4: AI-Natives: 5 keys to AI O11y, Part 4 
Part 5: The Feature Builders: 5 keys to AI O11y, Part 5 (Coming on 11/13)

AI is moving fast. In fact, AI advancement and adoption are moving faster than any shift we’ve seen since cloud native. New models, new tools, and new use cases seem to appear every week. According to Gartner, by 2026, more than 80% of enterprises will have used GenAI in production environments. For teams running production systems, that pace means observability has to keep up.

The challenge? With AI, monitoring isn’t just about uptime and responsiveness, although they are still important. Most AI systems are built on a cloud native stack.

On top of the already massive scale, cost, and data problems that cloud native systems create, we now need to keep an eye on:

• Model behavior (hallucinations, drift, toxicity)
• Token economics (how much each answer costs)
• GPU infrastructure (queues, utilization, and throughput)

AI observability introduces a whole new set of telemetry to understand these new areas. In other words, in AI observability, both the challenges and scale evolve and compound. Now, more than ever, you need control of your AI observability telemetry to contain costs, improve performance, and troubleshoot faster.

Observability telemetry control is about maximizing value density: retaining the signals that deliver the most visibility per dollar spent. To achieve this, you need visibility into how your observability data is used relative to what it costs, so you can decide if it’s worth keeping. Control is about being able to understand usage and cost side by side.

AI introduces a slew of new telemetry along with a host of never before seen operational challenges that observability must help solve. SREs now find themselves owning AI and inference incidents, not just traditional infrastructure outages. Non-deterministic AI systems introduce new, high-visibility failure modes that make observability more critical than ever. As the stakes are raised, confidence in AI starts with observability and control.

This is why we’re kicking off a new five-part blog series on how we at Chronosphere think about observability for AI. We’ll start with an overview (this post) and then move into the four key use cases in the AI ecosystem, Model Builders, GPU Providers, AI-Natives, and Feature Builders, and we will show what observability success looks like for each.

Our goal with this series is simple: to share practical guidance on AI Observability that helps DevOps, SRE, and observability professionals navigate the AI wave with clarity, confidence, and control.

The Artificial Intelligence field has moved from decades of research and periodic AI winters to a breakneck cycle of investment and deployment. GPUs unlocked the parallel compute needed for modern AI, and generative models brought that capability into everyday products, driving adoption across industries. The net effect: an “AI arms race,” a rapidly expanding vendor landscape, and a step-change in data and application complexity for engineering teams to manage.

There are many branches of AI. Our focus here is Generative AI and specifically large language models (LLMs). LLMs are AI models which are trained on vast amounts of text to generate context-aware responses for interfaces like chat, code assistants, and support bots. That’s the surface area driving new reliability, safety, and cost concerns in production.

We look at AI + observability through two lenses:

• AI Observability: applying modern observability to AI workloads and use cases.
• AI-assisted observability: using AI inside the observability platform to speed investigation and outcomes.

Chronosphere is investing on both fronts. In terms of AI-assisted observability, we’ve soft-released an MCP server so customers can connect LLMs and agents to their tenant and achieve observability outcomes programmatically, and we have additional AI-assisted capabilities in R&D to ensure we’re shipping value, and not just AI-washing.

But today, we are going to be talking about AI Observability and the AI use cases that need it the most.

AI workloads don’t start from a clean slate. They inherit every hard problem we already wrestle with in cloud native systems:

• Massive scale with billions of requests
• Distributed architectures that are notoriously difficult to troubleshoot
• High cardinality that explodes label dimensions
• The ever-present cost pressure from storing and processing petabytes of telemetry data.

Cloud native observability was already a high bar to clear, demanding sophisticated tools, constant tradeoffs, and some way to control your observability telemetry for cost and performance reasons.

AI raises that bar even higher. On top of all the above, teams must now contend with GPU saturation and queuing, LLM-specific latency and throughput issues, and multi-step dependencies like RAG pipelines or agent chains that introduce new points of failure.

There’s also a new economic dimension: token accounting and the tight coupling of infrastructure usage to per-request costs. And unlike traditional systems, AI workloads introduce behavioral risks such as hallucinations, bias, drift, and toxicity that impact not just reliability but also trust and safety.

Observability Challenges for AI Workloads

This is where reliability, safety, and unit economics converge and where the observability challenge doesn’t just evolve, it grows in complexity and urgency.

Fortunately, open-source SDKs like OpenInference and OpenLLMetry simplify accessing the telemetry needed to understand and solve these AI-specific challenges. And they make this easier by providing insights in the industry-standard OpenTelemetry format. In addition, NVIDIA DCGM is able to export GPU performance and utilization metrics in Prometheus format which makes it simple to incorporate them into observability platforms.

The AI market clusters into four recurring use cases. Each demands a tailored observability approach:

This is the first of a five-part blog series. The subsequent four blogs will delve into each of these AI use cases, detailing how observability with control can contribute to their success.

For all AI use cases, a foundational strategy involves focusing on the workloads that matter, establishing crisp SLOs around user experience, cost, and safety, making the signals involved first-class through the use of OpenTelemetry, and optimize cost and performance by applying control techniques to your observability telemetry. That’s how you ship fast, contain spend, and keep trust high as AI adoption surges. Or said another way: apply observability where AI meets scale, because that’s where the engineering and business impact compound.

Observability for AI is the operating system for reliable, safe, and cost-efficient LLM, RAG, and GPU systems. Make it first-class with control, and the rest follows.

What is “observability for AI,” in one line?

Applying observability to AI workloads, including LLMs, RAG, agents, and GPUs, as well as the applications and infrastructure supporting and relying on these systems, enables teams to monitor latency, cost, dependencies, and model behavior with production-grade signals.

How is AI-assisted observability different?

It uses LLMs and agents inside the observability platform (e.g., summaries, anomaly guidance). Our MCP server is a concrete step toward agent-driven outcomes.

Where do we start with metrics?

Begin with LLM latency/throughput, retrieval health for RAG, GPU utilization/queuing, token usage tied to outcomes, and model behavior counters. Expand from there based on use case.

Do I need new tooling for token economics and evaluation?

Sometimes. We can help instrument and visualize, as well as integrate with specialists to help create value faster.

Why Chronosphere for AI Workloads?

AI apps are cloud native at heart. Our platform was built for cloud native scale and complexity, which maps directly onto AI’s throughput, cardinality, and multi-tenant demands.

Agentic Systems / Agents

Software programs (often built on top of LLMs) that can perform goal-oriented tasks, make decisions, and call tools or APIs—like ChatGPT agents, AutoGPT, or LangChain agents.

Agent Observability

Monitoring the internal behavior of AI agents, including tool usage, state transitions, failure paths, and task completion rates.

AI-Assisted Observability

The use of AI in your observability tooling to do things like Anomaly detection, AIOps, etc..

AI-Assisted Workloads

AI enhances existing products by adding features like summarization, chat, and automation. The core business logic remains separate from the AI model, which acts as an additional layer. Observability challenges, such as hallucinations, token costs, and blind spots, often arise unexpectedly, and teams new to inference monitoring typically rely on default or basic dashboards.

AI-Native Workloads

Applications or systems built with AI at their core—typically powered by LLMs, agents, and dynamic inference. They differ from AI-assisted or AI-enhanced systems by being fundamentally dependent on AI to operate.

AI Observability

Applying Observability to AI Workloads and Use Cases. I.e., Observability FOR AI.

AI Workloads

Any computational task or process involved in building, operating, or interacting with AI systems.

Embedding

A numeric representation of text that lets you measure similarity.

Evaluation

The process of systematically assessing the performance, reliability, and limitations of an AI model on a given dataset.

Evaluation Observability

The process of monitoring and measuring the performance, accuracy, bias, and behavior of AI models—especially important in model validation, deployment, and iteration phases.

Fine-Tuning

A method of adapting a pre-trained model (like an LLM) to specific domains or tasks by continuing its training on a smaller, specialized dataset.

GPU (Graphics Processing Unit)

Specialized hardware optimized for parallel computation, essential for training and running deep learning models. In AI workloads, GPUs are often a bottleneck and major cost driver.

Inference (Model Inference)

The process of using a trained model to make predictions, decisions, or generate new content based on new, unseen data, such as generating a response to a prompt in an LLM.

Inference Latency

The time taken by an AI model (e.g., an LLM) to generate an output after receiving an input. Critical in real-time and user-facing AI applications.

LLM (Large Language Model)

A deep learning model trained on massive corpora to understand and generate human-like language. Examples include GPT-4, Claude, and Mistral. LLMs are used in chatbots, agents, document summarization, and more.

MCP (Model Context Protocol)

A framework for standardizing communication between observability systems and AI agents, enabling better inter-agent coordination and micro-agent observability.

Prompt

The instructions you send to a model, like “Summarize this ticket”.

Prompt Engineering

The practice of designing and structuring prompts to improve the performance of an LLM for specific tasks. Poor prompt design often causes inference issues.

RAG (Retrieval-Augmented Generation)

A pattern combining LLMs with search or vector databases to fetch relevant context during inference—commonly used for question answering and chatbots.

Token

The fundamental unit of text that language models process, representing a word, part of a word, character, or punctuation. The process of breaking down text into these smaller pieces is called tokenization. Tokens are crucial because AI models analyze these units to understand relationships, enabling them to process, interpret, and generate human-like text.

Tokenization

The process of breaking down raw input data, such as text, into smaller, meaningful units called tokens.

Vector Database

A specialized database that stores embeddings (numerical representations of text or images) and allows similarity search. Examples include Pinecone, Weaviate, and FAISS.