Visualizing LLM Performance with OpenTelemetry Tools for Tracing Cost and Latency

Monitoring traditional applications often focuses on system health: CPU usage, error rates, and basic latency. But for LLM applications, these metrics are insufficient. To understand performance, manage costs effectively, and ensure reliability, you need deep visibility into the LLM calls themselves. Ultimately, performance and cost impact whether your LLM outputs are satisfying users. This requires tracking metrics like token usage and detailed latency breakdowns, ideally within a unified dashboard, a topic we cover often on our blog.

OpenTelemetry provides the standard for capturing this data via traces and spans. This article explores the types of OpenTelemetry-compatible tools that can trace your LLM calls end-to-end and visualize critical performance indicators like latency and cost.

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Key Takeaways

• Effective LLM monitoring requires capturing LLM-specific metrics (token counts, cost estimates, detailed latency) alongside standard application traces.
• OpenTelemetry is the industry standard for capturing this data through traces and spans enriched with attributes.
• The toolchain includes instrumentation libraries to capture data and observability backends/platforms to store, analyze, and visualize it.
• Specialized LLM Observability Platforms offer the most integrated solution, providing both OpenTelemetry instrumentation and pre-built dashboards tailored for LLM metrics.

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Tools for OpenTelemetry-Based LLM Tracing and Visualization

Achieving end-to-end visibility for your LLM application using OpenTelemetry involves two key parts: capturing the right data and then visualizing it effectively. Different tools play roles in this process.

1. Instrumentation: Capturing LLM Data with OpenTelemetry

The foundation is collecting detailed data about each LLM call. OpenTelemetry uses traces (representing a full request) composed of spans (representing individual operations). To get LLM visibility, these spans must be enriched with LLM-specific attributes, following the OpenLLMetry semantic conventions:

• Token Counts: Attributes such as gen_ai.usage.prompt_tokens and gen_ai.usage.completion_tokens capture token usage for each request.
  
  
• Model Information: Attributes like gen_ai.request.model and gen_ai.system (vendor) identify which LLM and provider were used.
  
  
• Latency: Each span’s duration automatically captures request latency, providing a precise measure of model performance.
  
  
• Cost Data (Custom): Many teams extend OpenTelemetry by adding a custom span attribute for cost, calculated from token counts and the model’s pricing schema. While not part of the core OpenLLMetry spec, this practice is common and supported by OpenTelemetry’s flexible attribute model.

Tools for Instrumentation:

• OpenTelemetry SDKs: The standard libraries for manually adding tracing to your code.
• Specialized OTel Extensions: Libraries like OpenLLMetry automatically instrument popular LLM frameworks and libraries, capturing these LLM-specific attributes with minimal code changes.

2. Visualization: Dashboards for Latency, Cost, and Performance

Once the data is captured via OpenTelemetry, you need a backend tool to receive, store, and visualize it.

• General-Purpose Observability Backends: Tools like Grafana (using backends like Prometheus for metrics and Tempo/Jaeger for traces) are OpenTelemetry-compatible. You can send your LLM trace data to these systems and build custom dashboards to visualize latency and token counts. However, creating LLM-specific visualizations often requires significant custom configuration. 
• Specialized LLM Observability Platforms: This category provides the most seamless, end-to-end solution. These platforms are designed specifically for LLM workflows and typically offer:
  
  • Bundled OTel Instrumentation: Often provides easy-to-use wrappers around OpenTelemetry (like OpenLLMetry) for quick setup.
  • Automatic Data Enrichment: Automatically calculate estimated costs based on token counts and model pricing.
  • Pre-built Dashboards: Offer out-of-the-box dashboards tailored for visualizing LLM latency, cost per request/user/feature, token usage patterns, and trace visualizations showing the full request lifecycle.

Building the full pipeline, instrumentation, data enrichment, storage, and visualization—requires significant effort. This is precisely where a specialized, OpenTelemetry-native platform like Traceloop provides critical value. It uses OpenLLMetry for easy instrumentation, automatically captures and enriches LLM traces with cost and performance data, and provides integrated, pre-built dashboards. This allows teams to get end-to-end visibility and visualize latency, cost, and performance metrics in one place without needing to manually configure multiple disparate tools.

Frequently Asked Questions (FAQ)

1. What is OpenTelemetry? OpenTelemetry (OTel) is an open-source observability framework providing standards, APIs, SDKs, and tools for instrumenting applications to generate telemetry data (traces, metrics, logs). It allows you to send data to various compatible backend tools, avoiding vendor lock-in.

2. Can I use only Grafana and Prometheus for LLM observability? You can use Grafana and Prometheus to visualize LLM metrics like latency and token counts if you instrument your application to send that data using OpenTelemetry. However, you'd typically need a separate system for trace visualization, and you would need to build the cost calculation logic and LLM-specific dashboards yourself.

3. Can I build this monitoring system myself? Yes. You can create a DIY observability for LLMs with OpenTelemetry solution. However, building and maintaining the full data pipeline, storage, and visualization layer can be complex, which is why many teams opt for a managed platform.

4. What is OpenLLMetry? Traceloop’s OpenLLMetry is an open-source set of extensions built on top of OpenTelemetry, specifically designed for instrumenting LLM applications. It provides automatic instrumentation for popular LLM providers, vector databases, and frameworks, capturing the LLM-specific attributes needed for observability. It's maintained by Traceloop.

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Conclusion

Gaining deep visibility into LLM performance requires moving beyond traditional monitoring. By leveraging the OpenTelemetry standard to capture detailed traces enriched with LLM-specific attributes like token counts, and utilizing a platform capable of visualizing this data, teams can effectively track latency, manage costs, and debug issues. This philosophy is central to the mission we're building at Traceloop. While general-purpose tools offer flexibility, specialized LLM observability platforms provide the most integrated and efficient solution, offering pre-built instrumentation and dashboards tailored for the unique challenges of LLM applications.

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