Blazeio vs. FastAPI vs. Robyn: Benchmarking Reveals 86x Performance Difference

4 points by anonyxbiz a day ago

Blazeio is an ultra-fast asynchronous real-time streaming web framework crafted for high-performance backend applications. Built on Python's asyncio, it delivers non-blocking operations, minimal overhead, and lightning-quick request handling.

The Benchmark Setup

Hardware & Testing Environment

· Platform: Google Colab v5e-1 TPU Instance · Testing Tool: wrk with 1 thread, 10-second duration · Connection Loads: 1,000, 3,000, 5,000, and 10,000 concurrent connections · Payload: Identical "Hello world" response with full security headers (HSTS, CSP, X-Frame-Options, etc.)

Framework Configurations

All three frameworks were tested with identical:

· Security headers and policies · Keep-alive connections enabled · Same response payload · Identical testing methodology

The Results: Complete Performance Annihilation

Throughput Massacre Across All Load Levels

Requests Per Second Comparison:

Connections Blazeio Robyn FastAPI Blazeio Advantage 1,000 79,388 RPS 8,685 RPS 4,151 RPS 19.1x vs FastAPI 3,000 57,519 RPS 8,014 RPS 4,387 RPS 13.1x vs FastAPI 5,000 44,782 RPS 7,519 RPS 3,411 RPS 13.1x vs FastAPI 10,000 39,157 RPS 7,186 RPS 3,086 RPS 12.7x vs FastAPI

Transfer Rate Comparison:

Connections Blazeio Robyn FastAPI Blazeio Advantage 1,000 50.88 MB/s 1.05 MB/s 0.59 MB/s 86.2x vs FastAPI 3,000 36.86 MB/s 0.97 MB/s 0.62 MB/s 59.5x vs FastAPI 5,000 28.70 MB/s 0.91 MB/s 0.48 MB/s 59.8x vs FastAPI 10,000 25.09 MB/s 0.87 MB/s 0.43 MB/s 58.4x vs FastAPI

Blazeio's worst-case scenario outperforms everyone else's best-case scenario: · Blazeio at 10,000 connections (39,157 RPS) vs FastAPI at 1,000 connections (4,151 RPS): 9.4x faster · Blazeio at 10,000 connections vs Robyn at 1,000 connections: 4.5x faster

Latency Scaling: The Architectural Divide

Average Latency Under Load:

Connections Blazeio Robyn FastAPI 1,000 29.72ms 113.66ms 135.89ms 3,000 33.54ms 362.94ms 345.99ms 5,000 57.07ms 631.26ms 879.33ms 10,000 93.83ms 1.23s 1.41s

Blazeio's latency increased only 3.2x from 1K to 10K connections, while others increased 10-15x!

Total Request Capacity

Requests Served in 10 Seconds:

Connections Blazeio Robyn FastAPI 1,000 797,765 87,438 41,769 3,000 575,932 80,393 43,896 5,000 449,687 75,821 34,362 10,000 393,401 72,300 31,171

Blazeio served more requests at 10,000 connections than FastAPI served at 1,000 connections.

The Architecture Behind the Numbers

Why Blazeio Achieves Revolutionary Performance

1. Zero-Copy Architecture: Data moves directly from kernel buffers to network without Python-level copying 2. Connection-Level Coroutines: One coroutine handles all requests on a connection, eliminating per-request overhead 3. Protocol-Level Backpressure: Natural flow control prevents buffer bloat and memory exhaustion 4. Minimal Abstraction: Raw socket access with clean abstractions, not framework magic

The 86x transfer rate advantage and consistent sub-100ms latency at 10,000 concurrent connections demonstrate that traditional web framework architectures have been leaving massive performance on the table.

All tests were conducted on identical hardware with identical payloads and security configurations.

Has anyone else achieved similar performance with different architectural approaches? What's your experience scaling Python web applications to 10,000+ concurrent connections?