# Compare Experiments

The **Compare Experiments** feature in RagaAI Catalyst enables users to analyze and contrast up to 3 experiments, helping identify differences and patterns across experiments. This feature is ideal for evaluating the performance and impact of changes across different experimental setups.

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**Benefits of Experiment Comparison**

* **Comprehensive Analysis**: Identify patterns and dependencies by comparing performance metrics and behaviors across experiments.
* **Cost Optimization**: Gain insights into cost breakdowns and opportunities for efficiency improvements.
* **Versioning Insights**: Understand how different code versions and configurations impact overall outcomes.

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**Steps to Compare Experiments**

1. **Navigate to the Dataset Page**
   * Inside the dataset view, locate the section displaying the selected dataset name.
2. **Select Compare Dataset**
   * Click on the **Compare Dataset** button to initiate the experiment comparison process.
3. **Choose Experiments**
   * Select up to 2 experiments you wish to compare.
4. **Start Comparison**
   * Click the **Compare Experiments** button to generate the **Diff View**.
5. **Baseline Experiment**
   * You can change the baseline experiment to analyze the comparison relative to a different experiment.

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**Features of the Diff View**

1. **Code Diff**
   * Displays a side-by-side comparison of the code versions or configurations used in the experiments.
   * Highlights differences, additions, and deletions.
   * Allows toggling between multiple code versions for in-depth comparison.
2. **Analysis Section**
   * **Tool Usage Count Bar Chart**:
     * Visualizes how often each tool is used across experiments.
     * Reveals patterns and dependencies in tool utilization.
   * **Time vs. Tool Calls Chart**:
     * Shows how tool execution times vary between experiments.
     * Helps identify bottlenecks or performance discrepancies.
   * **Token Consumption Analysis**:
     * Compares model interactions across experiments in terms of token usage.
     * Enables users to evaluate efficiency and resource allocation.
   * **Cost Analysis**:
     * Breaks down cost incurred in each experiment.
     * Helps guide optimization decisions by identifying high-cost components.

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**Why Compare Experiments?**

* **Data-Driven Insights**: Make informed decisions about model configurations, tool selection, and resource allocation.
* **Performance Benchmarking**: Establish baselines and measure improvements or regressions.
* **Experiment Optimization**: Refine workflows and configurations based on comparative data.