Building a High-Performance Open-Source LLM Gateway: Bifrost (54x Faster than LiteLLM)

#go #ai #opensource #chatgpt

If you're building LLM apps at scale, your gateway shouldn't be the bottleneck. That’s why we built Bifrost, a high-performance, fully self-hosted LLM gateway built in Go; optimized for raw speed, resilience, and flexibility.

Benchmarks (vs LiteLLM)

Setup:

single t3.medium instance
mock llm with 1.5 seconds latency

Metric	LiteLLM	Bifrost	Improvement
p99 Latency	90.72s	1.68s	~54× faster
Throughput	44.84 req/sec	424 req/sec	~9.4× higher
Memory Usage	372MB	120MB	~3× lighter
Mean Overhead	~500µs	11µs @ 5K RPS	~45× lower

Key Highlights

Ultra-low overhead: mean request handling overhead is just 11µs per request at 5K RPS.
Provider Fallback: Automatic failover between providers ensures 99.99% uptime for your applications.
Semantic caching: deduplicates similar requests to reduce repeated inference costs.
Adaptive load balancing: Automatically optimizes traffic distribution across provider keys and models based on real-time performance metrics.
Cluster mode resilience: High availability deployment with automatic failover and load balancing. Peer-to-peer clustering where every instance is equal.
Drop-in OpenAI-compatible API: Replace your existing SDK with just one line change. Compatible with OpenAI, Anthropic, LiteLLM, Google Genai, Langchain and more.
Observability: Out-of-the-box OpenTelemetry support for observability. Built-in dashboard for quick glances without any complex setup.
Model-Catalog: Access 15+ providers and 1000+ AI models from multiple providers through a unified interface. Also support custom deployed models!
Governance: SAML support for SSO and Role-based access control and policy enforcement for team collaboration.

Migrating from LiteLLM → Bifrost

You don’t need to rewrite your code; just point your LiteLLM SDK to Bifrost’s endpoint.

Old (LiteLLM):

from litellm import completion

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

New (Bifrost):

from litellm import completion

response = completion(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Hello GPT!"}],
    base_url="http://localhost:8080/litellm"
)