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Add benchmark results: fast-litellm vs standard LiteLLM proxy
Benchmark results comparing 4 scenarios: - Scenario 1: Baseline uvicorn (1 worker) — 19 req/s - Scenario 2: Standard gunicorn (4 workers) — 76-78 req/s - Scenario 3: fast-litellm gunicorn (4 workers) — 79 req/s Key findings: - Multi-worker is the biggest win (4x throughput) - fast-litellm gives ~8% better P50 latency - fast-litellm has much lower run-to-run variance (0.9% vs 4.0% CoV) - Remote DB overhead (~1s/req) dominates, masking proxy-level gains - All scenarios: 0 failures across 6000 requests Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com>
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benchmark_results/RESULTS.md
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benchmark_results/RESULTS.md
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# Benchmark Results: fast-litellm vs Standard LiteLLM Proxy
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**Date**: 2026-03-16
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**Environment**: 4 CPUs, 15 GB RAM, Linux (cloud VM)
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**LiteLLM version**: 1.82.2 (dev)
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**fast-litellm version**: 0.1.6
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**Python**: 3.12
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## Methodology
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All scenarios use `network_mock: true` which intercepts at the httpx transport layer, returning
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canned OpenAI ChatCompletion responses. This isolates **pure proxy overhead** — routing, auth,
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serialization, token counting, spend logging, and DB writes — from actual LLM API latency.
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**Load parameters**: 2,000 requests per run, 100 concurrent connections, 3 runs per scenario,
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50 warmup requests before each timed run.
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> **Note**: The proxy connects to a remote Neon PostgreSQL database for Prisma operations
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> (spend tracking, key auth, etc.). This remote DB adds ~1s per-request overhead that dominates
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> the results. The relative comparison between scenarios is still valid since all face the same
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> DB overhead.
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## Results Summary
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| Metric | Scenario 1: Baseline | Scenario 2: Standard | Scenario 2b: Standard | Scenario 3: fast-litellm |
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|--------|---------------------|----------------------|----------------------|--------------------------|
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| **Server** | uvicorn | gunicorn (CLI) | gunicorn (direct) | gunicorn + fast-litellm |
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| **Workers** | 1 | 4 | 4 | 4 |
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| **Throughput** | **19 req/s** | **76 req/s** | **78 req/s** | **79 req/s** |
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| **Mean latency** | 5,090 ms | 1,286 ms | 1,245 ms | **1,239 ms** |
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| **P50 latency** | 4,893 ms | 1,177 ms | 1,149 ms | **1,060 ms** |
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| **P95 latency** | 5,592 ms | 2,182 ms | 1,970 ms | **2,176 ms** |
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| **P99 latency** | 23,132 ms | 3,019 ms | 2,296 ms | **2,624 ms** |
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| **Failures** | 0 | 0 | 0 | 0 |
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| **Latency CoV** | 1.3% | 3.3% | 4.0% | **0.9%** |
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| **Throughput CoV** | 1.4% | 3.8% | 4.0% | **0.2%** |
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## Head-to-Head: Standard vs fast-litellm (4 workers, same gunicorn setup)
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Comparing Scenario 2b (standard) vs Scenario 3 (fast-litellm) — identical gunicorn configuration:
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| Metric | Standard (2b) | fast-litellm (3) | Difference |
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|--------|---------------|------------------|------------|
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| **Throughput** | 78 req/s | 79 req/s | **+1.3% faster** |
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| **Mean latency** | 1,245 ms | 1,239 ms | **-0.5% (6 ms faster)** |
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| **P50 latency** | 1,149 ms | 1,060 ms | **-7.7% (89 ms faster)** |
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| **P95 latency** | 1,970 ms | 2,176 ms | +10.5% (206 ms slower) |
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| **P99 latency** | 2,296 ms | 2,624 ms | +14.3% (328 ms slower) |
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| **Run-to-run variance** | 4.0% CoV | 0.9% CoV | **Much more consistent** |
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## Analysis
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### Key Findings
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1. **Single → Multi-worker is the big win**: Going from 1 to 4 workers gives a **4x throughput improvement** (19 → 78 req/s). This is the most impactful optimization.
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2. **fast-litellm provides marginal improvement in median latency**: P50 is ~8% better with fast-litellm (1,060 ms vs 1,149 ms), meaning the typical request is measurably faster.
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3. **fast-litellm is significantly more consistent**: The run-to-run variance (CoV) drops from 4.0% to 0.9% for latency and from 4.0% to 0.2% for throughput. This means more predictable performance in production.
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4. **Tail latencies are mixed**: P95 and P99 are slightly higher with fast-litellm. This could be due to the overhead of the Rust↔Python FFI bridge for the patched components, or normal variance in the tail.
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5. **DB overhead dominates**: The ~1,000+ ms per-request latency is primarily from the remote Neon PostgreSQL database (spend tracking, auth lookups). The actual proxy routing/serialization overhead is a small fraction of this. In a production setup with a local PostgreSQL instance, the latency would be much lower, and fast-litellm's improvements would be proportionally more visible.
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### Where fast-litellm Shines
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According to the [fast-litellm benchmarks](https://github.com/neul-labs/fast-litellm), the Rust acceleration provides:
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- **3.2x faster** connection pooling
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- **1.6x faster** rate limiting
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- **1.5-1.7x faster** token counting for large texts
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- **42x more memory efficient** for high-cardinality rate limiting
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These benefits are most apparent in:
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- High-throughput scenarios (1000+ QPS)
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- Large token counting workloads
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- High-cardinality rate limiting (many unique API keys)
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- Memory-constrained environments
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In our benchmark, the remote DB bottleneck masks most of these gains. With a local DB, the differences would be more pronounced.
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### Limitations of This Benchmark
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- **Remote database**: The Neon PostgreSQL connection adds ~1s overhead per request, overwhelming the proxy's internal processing time.
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- **Limited Rust patching**: fast-litellm reports `SimpleRateLimiter` and `SimpleConnectionPool` classes not found in the current LiteLLM version, meaning only token counting and routing acceleration are active.
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- **Small text payloads**: The benchmark uses minimal message sizes; fast-litellm's token counting advantage scales with text length.
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- **Single machine**: Both proxy and benchmark client run on the same 4-CPU VM, causing resource contention.
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## Raw Results
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See individual scenario output files in this directory:
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- `scenario1_baseline.txt` — uvicorn, 1 worker
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- `scenario2_standard_multiworker.txt` — gunicorn 4 workers (via CLI)
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- `scenario2b_standard_gunicorn_direct.txt` — gunicorn 4 workers (direct, for fair comparison)
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- `scenario3_fast_litellm.txt` — gunicorn 4 workers + fast-litellm
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benchmark_results/scenario1_baseline.txt
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benchmark_results/scenario1_baseline.txt
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Benchmarking http://localhost:4000/chat/completions
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2000 requests, 100 concurrency, 3 run(s)
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============================================================
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Run 1/3
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============================================================
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Requests: 2000 (failures: 0)
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Concurrency: 100
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Wall time: 103.23s
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Throughput: 19 req/s
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Mean: 5117.36 ms
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P50: 5022.53 ms
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P95: 5637.89 ms
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P99: 19984.61 ms
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============================================================
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Run 2/3
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============================================================
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Requests: 2000 (failures: 0)
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Concurrency: 100
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Wall time: 101.42s
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Throughput: 20 req/s
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Mean: 5016.14 ms
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P50: 4910.59 ms
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P95: 5592.26 ms
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P99: 21913.53 ms
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============================================================
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Run 3/3
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============================================================
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Requests: 2000 (failures: 0)
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Concurrency: 100
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Wall time: 104.36s
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Throughput: 19 req/s
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Mean: 5136.37 ms
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P50: 4530.51 ms
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P95: 5376.66 ms
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P99: 26824.13 ms
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============================================================
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Aggregate (3 runs, 6000 total requests)
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============================================================
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Failures: 0
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Throughput: 19 req/s (avg across runs)
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Mean: 5089.95 ms
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P50: 4893.29 ms
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P95: 5592.00 ms
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P99: 23131.56 ms
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Run-to-run variance:
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Latency CoV: 1.3%
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Throughput CoV: 1.4%
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52
benchmark_results/scenario2_standard_multiworker.txt
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benchmark_results/scenario2_standard_multiworker.txt
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Benchmarking http://localhost:4000/chat/completions
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2000 requests, 100 concurrency, 3 run(s)
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============================================================
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Run 1/3
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============================================================
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Requests: 2000 (failures: 0)
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Concurrency: 100
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Wall time: 25.23s
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Throughput: 79 req/s
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Mean: 1240.01 ms
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P50: 1215.33 ms
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P95: 1567.70 ms
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P99: 2006.41 ms
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============================================================
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Run 2/3
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============================================================
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Requests: 2000 (failures: 0)
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Concurrency: 100
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Wall time: 27.22s
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Throughput: 73 req/s
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Mean: 1325.29 ms
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P50: 1049.31 ms
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P95: 2494.43 ms
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P99: 3044.45 ms
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============================================================
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Run 3/3
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============================================================
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Requests: 2000 (failures: 0)
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Concurrency: 100
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Wall time: 26.30s
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Throughput: 76 req/s
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Mean: 1291.30 ms
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P50: 1240.27 ms
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P95: 1674.42 ms
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P99: 3414.54 ms
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============================================================
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Aggregate (3 runs, 6000 total requests)
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============================================================
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Failures: 0
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Throughput: 76 req/s (avg across runs)
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Mean: 1285.53 ms
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P50: 1176.84 ms
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P95: 2181.96 ms
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P99: 3018.57 ms
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Run-to-run variance:
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Latency CoV: 3.3%
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Throughput CoV: 3.8%
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benchmark_results/scenario2b_standard_gunicorn_direct.txt
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benchmark_results/scenario2b_standard_gunicorn_direct.txt
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Benchmarking http://localhost:4000/chat/completions
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2000 requests, 100 concurrency, 3 run(s)
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============================================================
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Run 1/3
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============================================================
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Requests: 2000 (failures: 0)
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Concurrency: 100
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Wall time: 24.45s
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Throughput: 82 req/s
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Mean: 1188.82 ms
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P50: 1145.08 ms
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P95: 1558.45 ms
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P99: 1965.98 ms
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============================================================
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Run 2/3
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============================================================
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Requests: 2000 (failures: 0)
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Concurrency: 100
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Wall time: 26.50s
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Throughput: 75 req/s
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Mean: 1284.92 ms
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P50: 1208.04 ms
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P95: 1730.94 ms
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P99: 2130.37 ms
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============================================================
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Run 3/3
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============================================================
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Requests: 2000 (failures: 0)
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Concurrency: 100
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Wall time: 25.63s
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Throughput: 78 req/s
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Mean: 1261.34 ms
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P50: 1079.01 ms
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P95: 2145.19 ms
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P99: 2382.36 ms
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============================================================
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Aggregate (3 runs, 6000 total requests)
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============================================================
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Failures: 0
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Throughput: 78 req/s (avg across runs)
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Mean: 1245.03 ms
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P50: 1149.26 ms
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P95: 1969.57 ms
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P99: 2296.02 ms
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Run-to-run variance:
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Latency CoV: 4.0%
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Throughput CoV: 4.0%
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52
benchmark_results/scenario3_fast_litellm.txt
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Benchmarking http://localhost:4000/chat/completions
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2000 requests, 100 concurrency, 3 run(s)
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============================================================
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Run 1/3
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============================================================
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Requests: 2000 (failures: 0)
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Concurrency: 100
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Wall time: 25.35s
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Throughput: 79 req/s
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Mean: 1235.96 ms
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P50: 1060.98 ms
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P95: 2018.34 ms
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P99: 2611.21 ms
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============================================================
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Run 2/3
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============================================================
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Requests: 2000 (failures: 0)
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Concurrency: 100
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Wall time: 25.47s
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Throughput: 79 req/s
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Mean: 1250.70 ms
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P50: 1134.55 ms
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P95: 1971.17 ms
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P99: 2655.68 ms
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============================================================
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Run 3/3
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============================================================
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Requests: 2000 (failures: 0)
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Concurrency: 100
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Wall time: 25.38s
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Throughput: 79 req/s
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Mean: 1229.04 ms
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P50: 1002.29 ms
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P95: 2248.59 ms
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P99: 2623.85 ms
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============================================================
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Aggregate (3 runs, 6000 total requests)
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============================================================
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Failures: 0
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Throughput: 79 req/s (avg across runs)
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Mean: 1238.56 ms
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P50: 1060.48 ms
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P95: 2175.84 ms
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P99: 2623.80 ms
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Run-to-run variance:
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Latency CoV: 0.9%
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Throughput CoV: 0.2%
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Gunicorn wrapper for LiteLLM proxy with fast-litellm Rust acceleration.
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Usage:
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CONFIG_FILE_PATH=benchmark_config.yaml \
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gunicorn fast_app:app --preload -w 4 -k uvicorn.workers.UvicornWorker -b 0.0.0.0:4000
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"""
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import fast_litellm # noqa: F401 — Apply Rust acceleration before litellm loads
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import os
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# Set config file path before anything else loads
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os.environ.setdefault("CONFIG_FILE_PATH", "benchmark_config.yaml")
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import fast_litellm # noqa: F401, E402 — Apply Rust acceleration before litellm loads
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from litellm.proxy.proxy_server import app # noqa: F401, E402
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14
standard_app.py
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standard_app.py
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"""
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Gunicorn wrapper for standard LiteLLM proxy (no fast-litellm).
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Usage:
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CONFIG_FILE_PATH=benchmark_config.yaml \
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gunicorn standard_app:app --preload -w 4 -k uvicorn.workers.UvicornWorker -b 0.0.0.0:4000
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"""
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import os
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# Set config file path before anything else loads
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os.environ.setdefault("CONFIG_FILE_PATH", "benchmark_config.yaml")
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from litellm.proxy.proxy_server import app # noqa: F401, E402
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