An Introduction to LLM Observability

What is LLM Observability?

LLM Observability refers to the structured collection, correlation, and analysis of all relevant signals from an AI system in productive operation, with the aim of making its behavior explainable, controllable, and verifiable. It not only determines that a system is functioning, but also provides insight into why it behaves in a certain way, thus forming the basis for quality assurance, risk minimization, and cost control.

The functionality is based on the continuous collection of different data levels: These include inputs and outputs, runtime and latency values, token consumption, model versions, tool and API calls, error messages, and user interaction patterns. This data is collected centrally, linked together, and contextualized via evaluations, dashboards, and alerts to reveal deviations, inefficient processes, or security-critical patterns. In addition, procedures are used to evaluate the quality of responses, compare prompt versions, or detect unwanted outputs.

This provides a robust overall picture of how the system behaves in real-world usage scenarios, where problems arise, and what factors influence them. Since this concept takes into account not only system states but also semantic and content-related aspects of processing, it goes far beyond classic monitoring of technical metrics and forms the basis for distinguishing it from traditional monitoring.

LLM Observability vs LLM Monitoring

While LLM observability aims to analyze causes and understand systems, LLM monitoring focuses on detecting deviations during operation. Monitoring primarily answers the question of whether a system is functioning as expected, while observability clarifies why it is or is not doing so.

Monitoring is usually based on predefined metrics, thresholds, and alert rules, such as for response times, error rates, resource consumption, or unusual access patterns. If these thresholds are exceeded, the system triggers an alert. This makes monitoring particularly suitable for early problem detection and operational use, but it remains limited to known and measurable symptoms.

Observability correlates system data, requests, responses, configurations, and contextual information to reveal the internal relationships within an LLM system. Using a chatbot as an example, the following differentiation can be made: If the response quality decreases, this is reported in the monitoring system by an alarm. Observability then enables root cause analysis, for example by detecting a changed prompt template, an outdated retrieval index, a changed model configuration, or an increased proportion of critical user queries.

The key difference therefore lies in the field of application: monitoring is state-oriented and reactive, while observability is explanatory and analytical. Only the interaction of both concepts allows not only the detection of malfunctions, but also their systematic analysis and sustainable resolution.

Below you will find a complete overview of the differences between observability and monitoring:

Types of LLM Observability Signals

There are three key metrics or signals we can use when implementing observability: system performance, model behavior, and resource utilization performance.

System Performance Metrics

When analyzing system performance, we check whether the LLM system behaves similarly in the production environment as it did in the development or test phase. This includes, in particular, comparing important runtime metrics, such as whether the response latency corresponds to the expected values or whether the time to first token (TTFT) is within the limits measured in staging environments. The following are key metrics for evaluating system performance:

• Latency: Measures the total time from when a user sends a request to when the full response is received. In production, we generally want latency to stay within the range observed in staging.
• Time to First Token (TTFT): Captures the delay before the model starts generating its first output token. This metric is closely related to latency: if TTFT increases, overall latency would rise as well. TTFT is useful for assessing model responsiveness, identifying latency bottlenecks, and improving user experience.
• Throughput: Measures how many requests the LLM system can process per second. In other words, it reflects how many queries the system can handle concurrently without degrading latency. For example, if the system can handle 60 requests per second on a single A100 GPU in staging but drops to 30 in production under similar load, we may need to investigate issues like autoscaling, load-balancer, or throttling.
• Error Rate: Represents the percentage of user requests that fail or time out. A healthy system typically maintains a failure or timeout rate below 0.1% of total requests. This metric provides an overall view of our system’s reliability.

Model Behavior Metrics

The collection of metrics on model behavior serves to assess whether the generated responses meet the technical and qualitative requirements and where there is potential for optimization. Which metrics are useful depends on the specific use case.

In a RAG (retrieval-augmented generation) application, for example, the focus is on content quality, measured in terms of context relevance, response relevance, and soundness of the outputs. In other use cases, more general quality indicators such as technical correctness or user interactions are used to evaluate the performance of the model.

• Correctness: This is the foundational metric for evaluating model behavior. It measures how closely the model’s output matches the expected ground truth. In most cases, we prepare ground-truth data so we can compute scores such as ROUGE, BLEU, or similar metrics that estimate the LLM’s performance.
• Context Relevance: Particularly important in RAG pipelines, this metric evaluates how relevant the retrieved contexts are to the user’s query. The state-of-the-art approach is to use LLM-as-a-judge, where a separate model assigns a context relevance score given the user’s request and the retrieved passages.
• Answer Relevance: This measures how directly the generated response addresses the query, regardless of factual correctness. Similar to context relevance, we can apply the LLM-as-a-judge approach, for example by prompting a judge model with:
  
   “Rate how directly this answer responds to the question on a scale from 0 to 1.”
• Groundedness: Also key in RAG systems, groundedness measures whether the claims in the final response are supported by the retrieved context. Again, using LLM-as-a-judge, we might prompt:
  
   “Given the contexts and the answer, rate whether the answer can be inferred from the contexts on a scale from 0 to 1.”
• User Engagement: This provides a more subjective measure of response quality, using signals such as thumbs-up/down ratios, session length, or the rate of follow-up queries. High engagement can indicate that responses are not only accurate but also useful and satisfying to users.

Resource Utilization Metrics

The analysis of resource utilization metrics shows how efficiently an LLM system uses computing power and infrastructure. The goal is to identify bottlenecks and optimize performance, stability, and cost structure, for example in terms of throughput, latency, or error rates.

If, for example, it is found that GPU utilization during inference is significantly below the maximum possible, this indicates untapped potential. In such cases, efficiency gains can be achieved through customized batch processing, optimized memory management, or parallelized data pipelines to make better use of the available hardware.

• GPU/CPU Utilization: Measures how much of the available compute resources are being used by the LLM. Low utilization often suggests inefficiencies in task distribution or batching, while sustained high utilization may point to bottlenecks or the need for scaling.
• Memory Usage: Tracks how efficiently both GPU and system memory are used during inference or training. Observing this helps prevent out-of-memory errors, identify memory leaks, and ensure that memory allocation aligns with workload demands.
• Token Usage: Represents the number of tokens processed or generated per request (or over time). Since token usage directly impacts cost and efficiency, observing it enables teams to optimize prompts, manage API usage, and detect anomalies such as inefficient queries or misuse.
• Disk I/O (Input/Output): Measures the rate of data read and written to disk, particularly important in RAG workflows or when handling large datasets. High disk I/O may indicate slow storage performance or excessive data transfer between components.

LLM Observability Solutions

As mentioned above, the rise of LLM applications across industries has made LLM observability highly important for ensuring smooth and reliable deployment in production environments. Fortunately, there are several observability platforms available today that make it much easier for us to monitor, evaluate, and debug LLM systems.

Below are some commonly used LLM observability platforms:

Arize AI

• Area of Application: Best suited for LLM and agent observability, model evaluation, drift detection, and root-cause analysis. Arize AI also provides comprehensive support for popular LLM and agent frameworks such as LangChain, LlamaIndex, and DSPy.
• Strengths: Arize AI is an enterprise-grade observability solution that extends mature ML observability capabilities, such as trace playback, RAG/LLM evaluation, and prompt analysis. It’s built for teams that need a unified view to observe multiple models and agents across environments.
• Limitations: Arize AI follows an enterprise pricing model, which means advanced evaluation and annotation features require a paid plan.

Fiddler AI

• Area of Application: Fiddler AI is an enterprise platform for LLM observability and monitoring, similar to Arize AI. It’s particularly suited for enterprise-grade environments that emphasize security, policy compliance, and governance within their MLOps workflows.
• Strengths: Fiddler AI provides mature ML observability features adapted for LLMs. It’s especially valuable for organizations operating in regulated industries, thanks to its strong focus on policy and governance integration.
• Limitations: Like Arize AI, Fiddler AI uses enterprise pricing, so access to advanced features requires a paid subscription.

LangSmith

• Area of Application: LangSmith is a closed-source observability platform offering comprehensive tracing and evaluation capabilities for LLM and AI systems. Developed by the LangChain team, it integrates seamlessly with LangChain-based applications.
• Strengths: LangSmith is tightly integrated with LangChain, making it an excellent choice if your LLM or agent workflow is built using LangChain or LangGraph. It’s purpose-built for tracing multi-step agents, including actions, tool calls, and detailed step-by-step traces.
• Limitations: As a closed-source product, self-hosting requires a commercial license. If you self-host LangSmith, the recommended minimum resources are quite significant: e.g. 16 GB RAM + 4 CPUs for the app. This makes self-hosting expensive and potentially difficult for smaller teams.

Helicone

• Area of Application: Helicone is an open-source, proxy-based observability platform supporting major LLM providers such as OpenAI, Anthropic, and Google Gemini. It focuses on simplicity and ease of setup, which enables users to log and analyze LLM outputs as well as collect user’s feedback via the Helicone Feedback API.
• Strengths: Helicone is designed for simplicity and quick deployment. Its proxy-based design allows for features like caching, security checks, and API key management. Being open source, it can be self-hosted for greater control and cost efficiency.
• Limitations: Due to its open-source nature, self-hosting Helicone requires advanced setup due to its distributed architecture. Some advanced features (like custom rate limiting, caching) require deeper integration, which might be not easy for simpler teams to set up.

LangFuse

• Area of Application: LangFuse offers comprehensive tracing, analytics, evaluation, testing, and annotation capabilities for LLM and AI systems, similar to LangSmith. However, unlike LangSmith, it is open source.
• Strengths: LangFuse provides most of the benefits of LangSmith, including LangChain integration, while also supporting other LLM frameworks. Because it’s open source, LangFuse can be self-hosted without licensing fees, giving teams more flexibility and control.
• Limitations: The open-source model means we as users are responsible for scaling and managing the deployment themselves. Some enterprise-level features are available only in the paid tier.

Conclusion

Implementing LLM observability is no longer optional, it’s a necessary process for ensuring that LLM-based systems remain reliable, efficient, and trustworthy in production. As companies increasingly rely on LLMs to power customer-facing applications, the ability to trace, analyze, and understand model behavior becomes critical to maintaining quality and preventing costly errors.

Observability provides insight into why something happened, giving teams the visibility needed to debug complex, non-deterministic workflows of LLM-based systems.