---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: 6f168ca97aa89d747281bfdbd4bb55e8039d8638d44c2414839040c5b74e1cfd
  pageDirectory: concepts
  sources:
    - examples-analyzing-traces-databricks-on-aws.md
  confidence: 0.85
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - trace-based-feature-flag-analysis
    - TFFA
  citations:
    - file: examples-analyzing-traces-databricks-on-aws.md
title: Trace-based Feature Flag Analysis
description: Comparing performance metrics of traces grouped by feature flag states (control vs treatment) by filtering traces on metadata tags with mlflow.search_traces().
tags:
  - mlflow
  - feature-flags
  - a-b-testing
  - performance
timestamp: "2026-06-19T10:26:11.648Z"
---

# Trace-based Feature Flag Analysis

**Trace-based feature flag analysis** is a technique for measuring the performance impact of a feature flag (or feature toggle) by comparing trace data collected from requests where the flag is enabled vs. disabled. By instrumenting applications with [MLflow Tracing](/concepts/mlflow-tracing.md) and tagging traces with the flag’s state, teams can produce objective, data-driven comparisons that inform rollout decisions. ^[examples-analyzing-traces-databricks-on-aws.md]

## How it works

The core approach uses `mlflow.search_traces()` to retrieve all traces belonging to a given experiment, filtering them by a metadata tag that records the feature flag’s value. The traces are split into two groups – control (flag off) and treatment (flag on). Standard performance metrics, such as average latency, are computed for each group. ^[examples-analyzing-traces-databricks-on-aws.md]

A typical implementation follows these steps:

1. **Tag traces with the flag state.** During the agent or pipeline execution, add a metadata attribute (e.g., `feature_flag_new_retriever`) set to `'true'` or `'false'` to each trace.  
2. **Query for control traces.** Use `mlflow.search_traces()` with a filter such as `metadata.feature_flag_{flag_name} = 'false'`.  
3. **Query for treatment traces.** Use the same filter with `'true'`.  
4. **Compute and compare metrics.** Calculate the average latency (or other key performance indicators) for each group and return the results. ^[examples-analyzing-traces-databricks-on-aws.md]

The source provides a concrete example function `analyze_feature_flag_performance()` that returns a dictionary with keys like `avg_latency_{flag_name}_off` and `avg_latency_{flag_name}_on`. ^[examples-analyzing-traces-databricks-on-aws.md]

## Benefits

- **Objective measurement.** Decisions about enabling a feature flag are grounded in real production data rather than intuition.  
- **Granular comparison.** Because traces capture the full execution path, teams can drill into specific span types (e.g., retriever, LLM call) to understand where the flag’s impact is concentrated.  
- **Non-disruptive.** The analysis runs offline on already-collected traces; it does not require extra instrumentation beyond adding the flag metadata.  
- **Supports A/B experimentation.** The same pattern can be extended to compare multiple flag variants, not just on/off. ^[examples-analyzing-traces-databricks-on-aws.md]

## Related concepts

- Feature flags – The mechanism for toggling behavior in production.  
- A/B testing – Broader methodology for comparing two or more variants.  
- Trace analysis – General techniques for examining GenAI execution traces.  
- [MLflow Tracing](/concepts/mlflow-tracing.md) – The instrumentation framework that captures trace data.  
- [Performance monitoring](/concepts/performance-monitoring-with-mlflow-traces.md) – Ongoing observation of latency, throughput, and error rates.  
- mlflow.search_traces() API|Search traces programmatically – The `mlflow.search_traces()` API used to filter trace data.

## Sources

- examples-analyzing-traces-databricks-on-aws.md

# Citations

1. [examples-analyzing-traces-databricks-on-aws.md](/references/examples-analyzing-traces-databricks-on-aws-ebc17f1a.md)
