What Is LLM Observability and Monitoring?

Large language models (LLMs) are currently at the center of many modern software experiences. They power search, chatbots, assisted features, agentic workflows, RAG pipelines, summarization, and much more. But things can get weird with LLMs in production. You can’t just check CPU or memory metrics and call it a day. Your system might be technically fine, but then outputs can start to look strange. Your LLM-powered feature starts to hallucinate, latency starts spiking without a clear cause, and token usage starts ballooning after a minor prompt change.

Engineering teams need more than just metrics and logs. They need observability for LLMs—the ability to see inside their system, understand its behavior under varied inputs and loads, detect issues early, debug failures, and iterate on prompts, models, and infrastructure. This post defines what LLM observability means, how it differs from monitoring, key dimensions to track, and what to look for in a platform built to handle AI workloads.

What Is LLM observability?

LLM observability is the discipline of monitoring, tracing, and analyzing every stage of using an LLM in production to understand not just if something is wrong, but why, where, and how to fix or improve it. LLM observability exposes insights into inputs, the LLM model itself, outputs, user feedback, and downstream effects.

An effective LLM observability setup includes:

1. Telemetry data—such as latency, token usage, prompt variations, output errors, validation or parsing failures, and drift.
2. Contextual metadata—such as which prompt was used, which model version or configuration was used, and what external retrieval or pre-/post-processing occurred.
3. And feedback signals—such as output evaluation and comparing expected vs actual behavior, schema validation results, and scoring of LLM responses.

Together, these layers of information help teams debug issues, optimize prompts, and continuously improve quality under unpredictable conditions in production. LLM observability means giving engineering teams granular insights into LLM behavior in production so they can troubleshoot failures faster, identify and mitigate hallucinations, optimize prompts and models, and continuously improve quality in conditions they couldn’t fully simulate ahead of time.

For example, an engineering team with LLM observability can observe when an output violates a defined schema, they can track costs, know how many retrieved documents are relevant vs noise, and how retrieval latency contributes to overall response time.

Building reliable AI systems with LLM observability and monitoring

LLMs aren’t just transforming the end-user experience; they’re also changing how developers build software. Successful LLM deployment requires careful attention to AI system monitoring and ML observability across four layers:

1. The model: the LLM itself, often a third-party API with variable performance.
2. Data and retrieval: embeddings, indexes, or RAG pipelines that supply relevant context.
3. Prompt logic: the instructions and structure guide model output.
4. Infrastructure: the systems that support reliable performance and scaling.

Developing with LLMs can be tricky since each of these layers can fail or degrade in subtle ways. To better understand these issues, we’ve compiled a list of common issues with LLM applications.

Common issues with LLM applications

1. Hallucinated or incorrect outputs

Hallucination is when an LLM returns responses that sound confident but aren’t factually correct. It usually happens when the model fills in gaps rather than admitting it doesn’t know the answer. In production, this can lead to misinformation, broken user trust, and even compliance risks.

1. Latency, cost, and third-party dependencies

Most teams are using hosted LLM APIs or third-party model providers. That means you inherit the performance, rate limits, and cost structures. Spikes in latency or token usage can appear without warning and at scale that can impact the user experience and budgets.

1. Prompt injection and manipulation

Prompt hacking, also known as prompt injection, is like social engineering for LLMs. It happens when individuals or entities craft inputs that bypass defined guardrails. It tricks your system into revealing or generating unintended content.

1. Security and data privacy risks

Because LLMs process arbitrary input text, they can inadvertently expose or reproduce sensitive data. Combined with opaque model behavior, this creates unique data privacy and security risks.

1. Variance in prompts and model responses

LLMs aren’t deterministic. Two identical prompts can yield differing responses, which makes debugging and regression testing tricky.

LLM analytics and strong observability practices give teams the ability to run LLMs in production with confidence and manage these layers effectively to deliver a consistent, reliable AI-powered solution. Our full-stack observability blog covers more details about OpenTelemetry and monitoring.

LLM observability vs LLM monitoring

These two terms often get used interchangeably, but they are not the same.

• LLM monitoring tells you that something is wrong. It relies on predefined thresholds and alerts (e.g., latency > 2s).
• LLM observability tells you why it's happening. It lets you explore the system in real time, across any dimension of data.

LLM systems are tricky, and with them, you need both. Monitoring helps you catch anomalies fast, while observability helps you understand and fix them. In other words, monitoring helps you answer questions you already know to ask. Observability helps you answer the ones you didn’t know you’d need to ask.

When your LLM starts producing odd outputs, observability helps you trace the root cause, whether it’s a subtle prompt change, a new input source, or a shift in model behavior.

Key dimensions of LLM observability

LLMs are probabilistic systems. Their responses can vary based on input phrasing, retrieval data, and non-deterministic randomness. Observability lets you capture this variability and understand the levers that influence performance. Key dimensions to track for LLMs should include:

• Latency and performance: track how long requests take, where the bottlenecks occur, and how the system scales under load
• Token usage: input/output token counts, costs, and trends across different prompt or model versions
• Prompt variations: compare how small changes in structure or context affect responses.
• Output evaluation: validate responses against expected formats, schemas, or user scoring metrics.
• Model drift: Detect shifts in model behavior or accuracy over time that might require prompt updates or RAG pipeline changes.

LLM observability platforms expose these dimensions to help engineers explore their insights interactively rather than relying on static dashboards.

What to look for in an LLM observability platform

Not all observability platforms are built for AI. LLM workloads introduce unique challenges: unstructured text outputs, unpredictable latency, and often heavy reliance on external APIs. When evaluating LLM observability tools, look for platforms that can:

• Support real-time debugging and exploration without rigid schemas.
• Correlated data across model layers, prompts, and infrastructure.
• Integrate easily with OpenTelemetry for standardized instrumentation.
• Handle high-cardinality, high-dimensionality data at scale.

Observability for LLMs: Why Honeycomb leads the way

Honeycomb doesn’t treat LLM observability as a gimmick or add-on. We believe it’s the next evolution of how systems should be built and understood in the era of AI. What many call “AI observability” is really a more demanding, context-rich form of full-stack observability. This is a shift Honeycomb has been preparing for since well before the AI boom.

From metrics to rich context

The old model of observability relied heavily on metrics, logs, and dashboards. Useful but limited abstractions that often eliminates context, aggregates away outliers, and treats data points in isolation. In observability 1.0, you might see “latency is high,” but not why for a particular user, prompt, or model version.

In contrast, observability 2.0 treats every event as first-class so that they’re full of context and you can see relationships between datapoints. AI demands that your observability tool preserves context, enables ad-hoc queries, and stitches together chains of causality rather than just surfacing trending aggregates. When applied to LLMs, this means capturing every prompt, input, retrieval path, model version, and user signal, then making that data explorable.

Lessons from Honeycomb’s own LLM journey

We didn’t arrive at this philosophy purely in theory. We built it through experience. We’ve instrumented and improved our own LLM-based features, including our Query Assistant. Our takeaways:

• Test in prod (but with structure): with LLMs, you can’t simulate every possible input. Real user interactions reveal the most interesting edge cases. Using trace data can help uncover prompt misbehavior and hallucinations.
• Trace and create feedback loops: We embedded signals like prompt templates and user correctness feedback into the same trace to zoom in on a bad response and see every step that contributed to it.
• Increment and instrument where it matters: As we iterated on LLM features, we kept adding more observability layers, starting with tracing and then adding prompt-level context and error validation.
• Use OpenTelemetry’s standards for Generative AI as a baseline, but add custom attributes where it makes sense.

These lessons aren’t just for building better AI observability tools. They work for any team looking to instrument their LLM stacks.

What Honeycomb brings at scale

Because of our philosophy and experience with LLMs, Honeycomb offers several capabilities that others struggle to match:

• Performant, easy-to-use event querying, which allows engineers to filter and query by prompt version, user segments, and more without performance collapse.
• Real-time trace inspection, where you can jump into a live or recent request’s flow, gaining insights across prompts, retrievals, and downstream systems.
• Unified observability across AI and classic software layers, so you don’t treat your LLMs as a silo but as an integral part of your system in the context of databases, APIs, UIs, and model logic.

Modern observability means you don’t just watch your system; you understand it. Explore Honeycomb’s platform to learn how it supports AI and LLM observability in production.