---
language:
- en
license: mit
tags:
- mcp
- benchmark
- gradio
- fastmcp
- api-performance
pretty_name: MCP Server Benchmark Results
size_categories:
- n<1K
---

# 🔬 Gradio vs FastMCP Benchmark Report

**Generated:** 2026-03-04T10:26:47.242725

**Total scenarios:** 12

## Executive Summary

- **echo**: Gradio wins (151.4 vs 126.2 RPS, 1.2x difference)
  - Gradio best config: concurrency_limit=1.0
- **async_sleep**: Gradio wins (126.6 vs 43.9 RPS, 2.88x difference)
  - Gradio best config: concurrency_limit=1.0

## Throughput (Requests/Second)

|                     |   ('fastmcp', 'mcp_streamable') |   ('gradio', 'mcp_streamable') |
|:--------------------|--------------------------------:|-------------------------------:|
| ('async_sleep', 1)  |                           17.54 |                          16.57 |
| ('async_sleep', 10) |                           43.87 |                         126.56 |
| ('echo', 1)         |                          126.23 |                          54.28 |
| ('echo', 10)        |                           75.02 |                         151.45 |

## Latency p50 (ms)

|                     |   ('fastmcp', 'mcp_streamable') |   ('gradio', 'mcp_streamable') |
|:--------------------|--------------------------------:|-------------------------------:|
| ('async_sleep', 1)  |                          62.46  |                         62.939 |
| ('async_sleep', 10) |                         146.304 |                         78.523 |
| ('echo', 1)         |                           7.506 |                          0     |
| ('echo', 10)        |                         115.045 |                         62.953 |

## Gradio concurrency_limit Scaling

How does Gradio's throughput change as concurrency_limit increases?

|                     |    1.0 |
|:--------------------|-------:|
| ('async_sleep', 1)  |  8.425 |
| ('async_sleep', 10) | 64.665 |
| ('echo', 1)         | 27.14  |
| ('echo', 10)        | 77.105 |

## Protocol Overhead: HTTP API vs MCP

Comparing latency of the same tool called via REST API vs MCP protocol:

|                            |   mcp_streamable |
|:---------------------------|-----------------:|
| ('fastmcp', 'async_sleep') |         104.382  |
| ('fastmcp', 'echo')        |          61.2755 |
| ('gradio', 'async_sleep')  |        2104.85   |
| ('gradio', 'echo')         |        1050.52   |

## Error Rates

All scenarios completed with 0% error rate. ✅


## Resource Usage

| server   |   ('mean', 'avg_cpu_pct') |   ('mean', 'avg_memory_mb') |   ('mean', 'peak_memory_mb') |   ('max', 'avg_cpu_pct') |   ('max', 'avg_memory_mb') |   ('max', 'peak_memory_mb') |
|:---------|--------------------------:|----------------------------:|-----------------------------:|-------------------------:|---------------------------:|----------------------------:|
| fastmcp  |                         0 |                       5.655 |                        5.66  |                        0 |                       5.67 |                        5.67 |
| gradio   |                         0 |                       4.955 |                        4.955 |                        0 |                       5.68 |                        5.68 |

## Benchmark Charts

![Throughput Comparison](plots/throughput_comparison.png)

![Latency Comparison](plots/latency_comparison.png)

![Gradio Concurrency Limit Scaling](plots/gradio_cl_scaling.png)

![Protocol Overhead](plots/protocol_overhead.png)

## Methodology

- Both servers use identical tool implementations (imported from shared_tools.py)
- Each scenario runs in an isolated server subprocess
- Warmup period excluded from measurements
- Load generated by async httpx workers (not external tools)
- MCP tests use full protocol lifecycle (initialize → call_tool)
- System metrics sampled every 1s via psutil

---

*Benchmarks generated by [mcp-server-bench](https://github.com/Mandark-droid/mcp-server-bench)*