Understanding LLM Observability

If you're building applications with Large Language Models (LLMs), you must know what's happening inside them. Are they giving accurate responses? Are they using the correct context? How much are they costing you? LLM observability helps answer these questions by adapting traditional monitoring approaches to handle the unique challenges of language models.

This guide explores how teams monitor their LLM applications, from the basic principles to practical implementation details. We'll look at the key differences from traditional application monitoring, examine the specific challenges of tracking AI agents, and explore the tools that make this possible.

Understanding observability is crucial for building reliable AI applications, whether you're just starting with LLMs or already running them in production. Let's dive in.

Observability for LLM applications builds upon traditional observability principles while adapting them to meet the unique challenges of language model applications. While core observability concepts like metrics, logs, and traces remain fundamental, teams must extend these approaches to handle LLMs' non-deterministic nature and complex interaction patterns.

At its core, LLM observability provides teams with powerful insights to keep LLMs on track, ensuring they perform accurately, stay aligned with business goals, and serve users effectively. This visibility is crucial across development and production environments, helping teams understand how their LLM applications behave in real-world scenarios.

With the right observability tools, teams can monitor LLM behavior in real-time, A/B test different LLMs in production, easily detect performance shifts, and preemptively address issues before they impact the business or user experience.

While traditional observability focuses on telemetry data like metrics, logs, and traces, LLM observability must adapt to the unique characteristics of language model applications. The fundamental difference lies in LLMs' non-deterministic nature—where traditional applications produce consistent outputs for given inputs, LLMs may generate varying responses even with identical prompts.
This unpredictability requires a different approach to monitoring and debugging.

Success criteria also differ significantly. Traditional applications typically have binary success/failure states based on exceptions or error codes, but LLM applications often deal with subjective quality measures. A technically successful API call might still produce an inappropriate or low-quality response, necessitating more sophisticated evaluation methods that can assess semantic correctness and contextual appropriateness.

Cost analysis is of new importance in LLM observability. While traditional observability might focus on resource usage like CPU and memory, LLM applications must carefully track API costs, token usage, and model-specific pricing. This economic dimension becomes crucial to the observability stack, often directly influencing architectural decisions.

LLM observability comprises several essential components that work together to provide comprehensive visibility. At its foundation is execution tracing, which tracks the complex chain of interactions in LLM applications. This includes monitoring decision flows, logging agent actions, and visualizing multi-step processes that are common in modern LLM applications.

Input/output monitoring forms another crucial component, capturing the prompts and completions and tracking token usage and context windows. For applications using retrieval-augmented generation (RAG), this extends to logging the retrieved content that influences the model's responses.

Quality assessment becomes more sophisticated in LLM observability, combining user feedback collection with automated evaluation metrics. A critical aspect is detecting hallucinations - where LLMs generate plausible-sounding but incorrect or unsupported responses. This is particularly important in retrieval-augmented generation (RAG) applications, where responses should be grounded in specific source documents or context. Teams must monitor whether responses are well-formed and accurately reflect the provided context and source material.

Beyond hallucination detection, quality assessment might include LLM-based scoring systems, output validation, and continuous monitoring of response quality. These tools help teams understand whether their LLM applications provide accurate, relevant, and helpful responses to users. Session management adds another layer, tracking conversations and preserving user context across multiple interactions to ensure consistent and contextually appropriate responses over time.

Performance and cost metrics tie everything together, providing insights into latency, token usage, and resource utilization. These metrics help teams optimize their applications while maintaining cost efficiency. Debug tooling completes the picture, offering capabilities for prompt inspection, intermediate step analysis, and version control for prompts and models.

LLM observability provides several critical benefits for teams building and maintaining LLM applications. It enables effective debugging of complex LLM applications by giving visibility into execution chains and decision flows, helping teams identify issues early and understand application behavior in detail. This visibility is essential for debugging issues like infinite loops, excessive token usage, or unexpected application behavior.

Cost optimization is another key benefit. Teams can track and optimize their LLM usage costs through comprehensive monitoring and analytics. This visibility becomes particularly valuable in production environments where understanding cost patterns can directly impact business decisions and help identify opportunities for optimization.

Through LLM observability, quality assurance becomes more systematic. Teams can track and measure output quality over time, implement automated evaluations, and understand how application changes affect response quality. This systematic approach helps ensure consistent performance and enables data-driven improvements.

The practical impact of these benefits is well illustrated in LangChain's experience building LangSmith, as detailed in their blog post "Why we Choose ClickHouse to Power LangSmith." Their platform demonstrates how observability enables teams to "measure the impact of changes to prompts and models" and perform "A/B testing and online evaluations," providing concrete evidence of how these benefits manifest in real-world applications.

Implementing effective LLM observability presents several unique challenges. The non-deterministic nature of LLM outputs makes it difficult to establish consistent quality metrics and evaluation criteria. Unlike traditional applications where success can be measured through clear pass/fail criteria, LLM applications require more nuanced evaluation approaches.

Complex execution flows present another significant challenge. LLM applications typically involve multiple chained API calls, parallel operations, and sometimes sophisticated decision trees. This complexity creates technical challenges in capturing and presenting the full context of each execution while maintaining system performance.

LangChain's experience with LangSmith documents the technical challenges of implementing LLM observability at scale. Initially using Postgres, they "quickly realized that people wanted to log a large percentage of their production data to perform specific actions such as tracing and creating datasets, running evaluation jobs, A/B testing, and monitoring performance."

Their transition from Postgres to ClickHouse highlights how traditional databases struggle with the volume and velocity of observability data, requiring specialized infrastructure decisions to handle high-throughput data ingestion and fast analytical queries.

The need for real-time insights adds another layer of complexity. Traditional approaches like materializing statistics ahead of time often prove insufficient, as they limit the ability to perform flexible, ad-hoc analysis of observability data. Teams need solutions that can provide real-time insights while maintaining performance and reliability.

Choosing the right database becomes crucial as organizations scale their LLM applications and their observability needs grow. The unique characteristics of LLM observability data - from high-volume trace ingestion to complex analytical queries - create specific requirements for the underlying storage system.

ClickHouse has emerged as a particularly well-suited solution for LLM observability data, offering a powerful combination of performance, scalability, and ease of use. Here are several key reasons why ClickHouse stands out as a practical choice for LLM observability:

LLM observability requires logging large production data volumes, including prompts, completions, and execution traces. ClickHouse's architecture is designed to handle high-throughput ingestion while maintaining query performance, making it ideal for applications that must log a significant percentage of their production traffic.

LLM observability tools need to support both real-time monitoring and historical analysis. ClickHouse enables fast filtering and aggregation across large datasets, allowing teams to drill down into charts and metrics without pre-aggregation or materialized views. This flexibility is crucial for investigating issues and understanding application behavior.

LLM applications must often retrieve specific traces or execution chains for detailed analysis. ClickHouse's sorting keys and materialized views can be optimized to support broad analytical queries and efficient lookups of individual traces, providing the performance needed for monitoring dashboards and debugging workflows.

As LLM applications generate increasing volumes of observability data, the ability to scale cost-effectively becomes crucial. ClickHouse's columnar storage and compression capabilities help manage storage costs while maintaining query performance, allowing users to retain and analyze more historical data.

The effectiveness of ClickHouse for LLM observability is demonstrated by its adoption across multiple observability platforms in the space.

Langfuse, an open-source LLM engineering platform, leverages ClickHouse to provide comprehensive observability and evaluation capabilities. Their platform demonstrates how ClickHouse can handle everything from basic logging to complex analytical queries needed for LLM application monitoring.

Laminar takes a unique approach by combining ClickHouse with other specialized databases in their architecture. Their platform uses ClickHouse specifically for analytics while incorporating Postgres for storage and Qdrant for semantic search. This architecture, powered by Rust, shows how ClickHouse can effectively integrate into a larger observability stack, mainly for handling complex execution traces and OpenTelemetry spans in sophisticated LLM applications.

Helicone, another open-source platform in the space, uses similar architectural principles to handle billions of logs while providing real-time insights. These platforms demonstrate how ClickHouse's capabilities align perfectly with the needs of LLM observability - handling high-volume data ingestion, providing real-time analytics, and scaling effectively as applications grow.

The emergence of these platforms, all choosing ClickHouse as a core component of their architecture, validates its position as a leading choice for LLM observability data storage and analytics.

At its core, LLM observability can be implemented using OpenTelemetry (OTel), the open standard for observability data. For LLM applications, this typically involves creating spans with flattened attributes that capture key GenAI-specific data such as:

• Prompt content and tokens
• Model information and parameters
• Completion content and tokens
• Cost and latency metrics
• Context or retrieved documents for RAG applications
• Function calls and tool usage for agents

This basic approach allows teams to start capturing LLM interactions while leveraging their existing observability infrastructure and expertise.

Several open-source tools have emerged to simplify implementation, all building on OpenTelemetry's foundations:

1. OpenInference provides conventions and plugins specifically designed for AI applications. It works with any OpenTelemetry-compatible backend and offers standardized ways to trace AI-specific operations.
2. OpenLLMetry focuses on non-intrusive tracing for LLM applications, making it easy to monitor and debug LLM app execution. Teams can export these traces to specialized LLM observability platforms or their existing observability stack.
3. OpenLIT takes a comprehensive approach by providing automatic OpenTelemetry instrumentation across various LLM providers, frameworks, and vector databases. This broader coverage helps teams monitor LLM interactions and the surrounding infrastructure and dependencies.

When implementing LLM observability, teams need to make several key decisions. First, they must choose between a manual OpenTelemetry implementation or leveraging automated tools like OpenInference, OpenLLMetry, or OpenLIT. This choice often depends on their specific needs for customization and control. Teams must also decide whether to use their existing observability stack or adopt specialized LLM platforms. This decision should be guided by their current tooling, team expertise, and specific monitoring requirements.

Additionally, teams must carefully consider what metrics and traces are most important for their use case, how to handle sensitive data in traces and determine appropriate sampling rates for production traffic. These decisions will shape the effectiveness and efficiency of their observability implementation.

The growing ecosystem of tools and standards makes it increasingly straightforward to implement robust LLM observability, allowing teams to focus on using the insights rather than building the infrastructure.