Granular LLM Monitoring for Tracking Token Usage and Latency per User and Feature

As LLM applications move into production, managing costs and ensuring performance against Service Level Objectives (SLOs) becomes paramount. Simply monitoring your overall API bill or average response time isn't enough. These operational metrics are a key part of understanding how to evaluate if your LLM outputs are satisfying users. To truly optimize, you need granular insights: how much token usage and latency is attributable to specific users or particular features within your application?

This article will explore the essential tools and architectural approaches required to monitor LLM token usage and latency, a topic we cover regularly on our blog.

Key Takeaways

• Granular monitoring requires capturing structured metadata (user_id, feature_name, token counts, request_duration) with every LLM interaction.
• The toolchain consists of instrumentation libraries (like OpenTelemetry) to capture data and observability platforms to analyze and visualize it.
• This detailed data allows you to define and track specific SLOs for latency (e.g., "99% of summarization requests under 2 seconds").
• Specialized LLM observability platforms often aim to simplify the process: providing automated instrumentation, dashboards, and alerting, though in practice, some manual instrumentation or configuration may still be required, especially for non-standard providers.

Achieving Granular Visibility: The Tools and Methods for LLM Monitoring

To effectively track LLM costs and performance at a granular level, you need a two-part system: a way to capture detailed data from your application and a platform to analyze that data.

1. The Foundation: Capturing the Right Data with Instrumentation

Before you can monitor anything, you need to collect the right data. This is done by "instrumenting" your application code. The common approach for this is OpenTelemetry, an open-source framework that captures traces and metrics.

For every LLM call, you typically capture two types of information:

• Metrics: The quantitative measurements you care about.
  
  • prompt_tokens and completion_tokens for cost tracking.
  • request_duration (latency) for performance tracking.
• Dimensions (Attributes): The context that allows you to filter and group your metrics.
  
  • user_id: To isolate a specific user's activity.
  • feature_name: To isolate a specific feature (e.g., "chatbot," "summarizer").
  • model_name: To compare costs and performance between models.

By using an OpenTelemetry-based library like OpenLLMetry, this rich data can be captured automatically for every LLM interaction.

2. The Tools: Analyzing and Visualizing the Data

Once you are capturing this data, you need a tool to send it to for analysis. There are two main categories of tools that can help.

• General-Purpose Observability Platforms: Tools like Datadog, New Relic, or open-source solutions like Grafana (with Prometheus) are excellent for ingesting, querying, and visualizing time-series data.
• Specialized LLM Observability Platforms: These platforms are purpose-built for the challenges of LLM monitoring. They provide an end-to-end solution that often includes pre-built instrumentation, dashboards, and alerting.

3. The Application: Tracking Against SLOs and Budgets

With the right data and tools, you can now proactively manage performance and costs.

• Tracking Performance Against SLOs: Granular latency data is essential for SLOs. For example, you can define an SLO: "99% of all requests to the feature_name = 'summarizer' must have a request_duration of less than 2,000ms over a 30-day period." Your observability tool can then track this metric and alert you if you are in danger of breaching your SLO.
• Controlling Costs: You can tag or slice metrics (e.g. by user_id or feature_name) so that you could monitor or alert on unusually high consumption. Whether this works out-of-the-box depends on your instrumentation and observability stack.

Building this entire monitoring pipeline from scratch can be a complex task. This is precisely where a specialized LLM observability platform like Traceloop provides critical value. Built on OpenTelemetry, it automatically instructs your LLM calls to capture all the essential metrics and dimensions. It provides pre-built dashboards for visualizing token usage and latency per user and per feature, allowing you to track costs and monitor SLOs from day one without the need for a complex setup.

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Frequently Asked Questions (FAQ)

1. What is an SLO, and how does this monitoring help meet them?

An SLO (Service Level Objective) is a target for a service's performance, often related to latency. By monitoring latency per feature, you can track whether a specific part of your LLM app is meeting its performance target (e.g., "the summarizer should respond in under 2 seconds 99% of the time").

2. Is it safe to log user IDs and prompts for monitoring?

This requires careful consideration of data privacy. Best practices include anonymizing user IDs where possible, redacting sensitive information from prompts, and ensuring your observability platform meets strict security standards.

3. Can I build this monitoring system myself?

Yes. You can create a DIY observability for LLMs with the 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 if my application has many microservices?

This is where distributed tracing shines. A trace ties all operations from multiple services into a single view. An observability platform like Traceloop can then show you the full journey of a request and attribute costs and latency across all services involved.

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Conclusion

Effective cost control and performance management for LLM applications demand granular visibility. By systematically capturing token usage and latency metrics enriched with dimensions like user ID and feature name, teams can move beyond high-level averages to pinpoint specific areas for optimization. This philosophy is central to the mission we're building at Traceloop. Leveraging an observability platform that automates this data collection is key to transforming your LLM applications into transparent, controllable, and cost-efficient systems.

Ready to gain granular control over your LLM costs and performance? Book a demo today.