---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: 5f180decfb6badca5b9448efd8917b8582a1c3ae22ee6de012ee7b7d6d19a486
  pageDirectory: concepts
  sources:
    - debug-and-analyze-your-app-with-tracing-databricks-on-aws.md
  confidence: 0.9
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - trace-based-performance-and-cost-monitoring
    - Cost Monitoring and Trace-Based Performance
    - TPACM
  citations:
    - file: debug-and-analyze-your-app-with-tracing-databricks-on-aws.md
title: Trace-Based Performance and Cost Monitoring
description: Using MLflow Tracing to capture and monitor latency, cost, and resource utilization at each step of GenAI application execution to identify bottlenecks and optimize efficiency.
tags:
  - performance
  - cost-optimization
  - monitoring
timestamp: "2026-06-18T15:10:50.174Z"
---

# Trace-Based Performance and Cost Monitoring

**Trace-Based Performance and Cost Monitoring** refers to the practice of using execution traces — detailed records of each step in a GenAI application's request-response cycle — to observe and optimize operational metrics such as latency, cost, and resource utilization. By instrumenting an application once with [MLflow Tracing](/concepts/mlflow-tracing.md), teams can capture these metrics consistently in both development and production environments, enabling data-driven decisions about performance improvements and cost efficiency. ^[debug-and-analyze-your-app-with-tracing-databricks-on-aws.md]

## Key Metrics Captured

[MLflow Tracing](/concepts/mlflow-tracing.md) records operational metrics at each intermediate step of an application’s execution — including retrieval, tool calls, and LLM interactions — as well as for the overall request-response cycle. The specific metrics captured are:

- **Latency** at each step, which helps identify performance bottlenecks within complex pipelines.
- **Cost** per step, providing visibility into where tokens or compute resources are consumed.
- **Resource utilization**, enabling teams to monitor whether infrastructure is used efficiently.

^[debug-and-analyze-your-app-with-tracing-databricks-on-aws.md]

## How It Works

Traces are generated automatically when an application is instrumented with [MLflow Tracing](/concepts/mlflow-tracing.md). Each trace captures the complete execution flow, inputs, outputs, and metadata for every intermediate step. In production, traces also include error information and the operational metrics above. This unified approach eliminates the need to switch between different debugging tools or sift through unstructured logs. ^[debug-and-analyze-your-app-with-tracing-databricks-on-aws.md]

## Using Traces to Optimize Performance and Cost

With trace data available, teams can:

- **Identify performance bottlenecks**: Pinpoint which step in a multi-step pipeline (e.g., retrieval, tool call, LLM inference) contributes most to end-to-end latency.
- **Monitor resource utilization**: Ensure that compute resources are neither underutilized nor over-provisioned.
- **Optimize cost efficiency**: Understand exactly where tokens or API calls are spent, and adjust prompts, model selection, or caching strategies accordingly.
- **Diagnose production issues in real time**: Traces capture errors alongside latency and cost data, enabling rapid root-cause analysis.

^[debug-and-analyze-your-app-with-tracing-databricks-on-aws.md]

## OpenTelemetry Integration

[MLflow Tracing](/concepts/mlflow-tracing.md) is compatible with **OpenTelemetry**, the industry-standard observability specification. This compatibility allows teams to export trace data to existing observability stacks (e.g., Datadog, Grafana, Splunk) without changing their instrumentation. The export capability is documented under [OpenTelemetry Export](https://docs.databricks.com/aws/en/mlflow3/genai/tracing/integrations/open-telemetry). ^[debug-and-analyze-your-app-with-tracing-databricks-on-aws.md]

## Analyzing Traces with Genie Code

[Genie Code](/concepts/genie-code.md) provides a natural-language interface for exploring trace data. Instead of writing custom queries or navigating multiple UI pages, users can ask questions such as:

- *"Are there any error traces in this experiment?"*
- *"What's the P95 latency for my traces?"*

Genie Code has read access to traces, sessions, evaluation runs, scorers, datasets, labeling sessions, and more, enabling a single conversation to go from a high-level question to detailed root-cause analysis. See Genie Code for agent observability and evaluation for the full list of capabilities. ^[debug-and-analyze-your-app-with-tracing-databricks-on-aws.md]

## Benefits

- **Unified dev/prod experience**: Instrument once; traces work in notebooks, IDEs, and production dashboards.
- **Actionable insights**: Metrics at every step help prioritize performance improvements and cost reduction.
- **Seamless export**: OpenTelemetry compatibility allows integration with existing observability tools.
- **Natural-language querying**: Genie Code lets non-experts explore trace data conversationally.

^[debug-and-analyze-your-app-with-tracing-databricks-on-aws.md]

## Related Concepts

- [MLflow Tracing](/concepts/mlflow-tracing.md) — The core tracing framework that captures and stores trace data.
- OpenTelemetry — Open standard for observability data, enabling trace export.
- [Genie Code](/concepts/genie-code.md) — Natural-language interface for analyzing traces and evaluation data.
- [Agent Observability](/concepts/genai-agent-observability.md) — Broader practice of monitoring GenAI agent behavior using traces and feedback.
- Cost Optimization — Strategies for reducing inference and infrastructure costs.
- Performance Bottleneck — A step in the pipeline that limits overall throughput or latency.

## Sources

- debug-and-analyze-your-app-with-tracing-databricks-on-aws.md

# Citations

1. [debug-and-analyze-your-app-with-tracing-databricks-on-aws.md](/references/debug-and-analyze-your-app-with-tracing-databricks-on-aws-d9c92247.md)
