Bifrost benchmark results

Benchmark summary

The benchmark compares Bifrost and LiteLLM using sustained load tests, gateway overhead measurements, high-throughput stress tests, and architecture-level differences.

Interactive charts use generated samples around the published benchmark values. Use the tables below for the fixed benchmark figures.

• Throughput. Bifrost processed 424 req/s and LiteLLM processed 44.84 req/s in the 500 RPS load test on AWS EC2. This is 9.5x higher throughput in the published data.
• Success rate. Bifrost completed 100% of requests and LiteLLM completed 88.78% of requests in the 500 RPS load test.
• P50 latency. Bifrost measured 804ms P50 latency and LiteLLM measured 38.65s P50 latency in the 500 RPS load test.
• P99 latency. Bifrost measured 1.68s P99 latency and LiteLLM measured 90.72s P99 latency in the 500 RPS load test. This is 54x lower P99 latency in the published data.
• Memory. Bifrost used 120 MB peak memory and LiteLLM used 372 MB peak memory in the 500 RPS load test. This is 68% lower peak memory in the published data.
• Gateway overhead. Bifrost measured 0.99ms gateway overhead and LiteLLM measured 40ms gateway overhead with a 60ms mock OpenAI upstream. This is 40x lower gateway overhead in the published data.

Performance at a glance

Test environment

These fields describe the 500 RPS Bifrost vs LiteLLM load test environment.

500 RPS load test

Gateway overhead

Gateway overhead excludes the 60ms mock OpenAI response and measures the proxy processing time added by the gateway.

High-throughput stress test

The 5,000 RPS stress test records Bifrost-only internal overhead on two AWS EC2 instance sizes.

• t3.medium. 59µs internal overhead at 5,000 RPS with 100% success rate.
• t3.xlarge. 11µs internal overhead at 5,000 RPS with 100% success rate.

Architecture comparison

Key insights

• Optimized architecture. Bifrost uses Go, efficient parsing, and memory-optimized data structures to reduce allocations.
• Native concurrency. Bifrost uses Go goroutines to handle concurrent connections without the Python GIL bottleneck.
• Efficient memory model. Bifrost uses Go memory management to maintain consistent performance under load while using less memory in the benchmark.

Full analysis

Read the full benchmark analysis for methodology, test configuration, and detailed performance discussion. [Read full benchmark analysis]

Open Source & Enterprise

Drop-in replacement for compatible AI SDKs

Change one line of code to point compatible SDKs at Bifrost. Works with OpenAI, Anthropic, LiteLLM, Google GenAI, LangChain, and Vercel AI SDK. [Gateway setup docs] [Drop-in replacement docs]

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ.get("OPENAI_API_KEY"),
    base_url="https://<bifrost_url>/openai",
)

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Hello!"}],
)

import os
from anthropic import Anthropic

anthropic = Anthropic(
    api_key=os.environ.get("ANTHROPIC_API_KEY"),
    base_url="https://<bifrost_url>/anthropic",
)

message = anthropic.messages.create(
    model="claude-3-5-sonnet-20241022",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Hello, Claude"}],
)

import litellm

# Set the base URL to your Bifrost deployment
litellm.api_base = "https://<bifrost_url>"

response = litellm.completion(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Hello!"}],
)

import google.generativeai as genai

genai.configure(
    api_key="YOUR_API_KEY",
    transport="rest",
    client_options={"api_endpoint": "<bifrost_url>/google"},
)

model = genai.GenerativeModel("gemini-pro")
response = model.generate_content("Hello!")

• Point the SDK base URL at your Bifrost deployment.
• Keep API keys in your environment or secret manager.
• See the docs for provider-specific configuration and deployment steps.

Trust

• Benchmark methodology. The full analysis documents methodology, test configuration, and detailed performance discussion for the benchmark data. [Full benchmark analysis]

FAQ

Related resources

• [Blog: Full benchmark analysis]
• [Migration: Migrating from LiteLLM]
• [Alternative: LiteLLM Alternative]
• [Docs: Bifrost docs]
• [GitHub: maximhq/bifrost]
• [Pricing: Bifrost pricing]
• [Enterprise: Bifrost enterprise]
• [Book a Demo: Bifrost demo]
• [Resources: Bifrost resources]