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Kuldeep Paul
Kuldeep Paul

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LLM Gateway Comparison: Bifrost vs LiteLLM (2025)

#ai #comparison #opensource #llm

The Landscape

If you're building with LLMs in production, you need a gateway. It's not optional. You need:

  • Multi-provider routing
  • Failover and retry logic
  • Rate limiting
  • Observability
  • Cost tracking

Two main options exist: LiteLLM (Python) and Bifrost (Go, open source).

Let's compare them honestly.

Feature Comparison

Feature Bifrost LiteLLM
Performance (P99 @ 500 RPS) 520 ms 28,000 ms
Gateway Overhead 11 μs ~600 μs
Memory Usage 1.4 GB 4.3 GB
Max Stable RPS 5,000+ <1,000
Language Go Python
License MIT (Open Source) MIT (Open Source)
Multi-provider routing ✅ ✅
Load balancing ✅ Adaptive ✅ Basic
Streaming ✅ ✅
Plugin system ✅ Go plugins ✅ Python
Built-in observability ✅ Zero overhead ✅ Optional
Tool calling ✅ ✅
In-VPC deployment ✅ ✅
Prometheus metrics ✅ Native ✅ Via plugin

Performance Deep Dive

We ran identical benchmarks on AWS t3.xlarge instances:

At 500 RPS:

  • Bifrost P99: 520ms
  • LiteLLM P99: 28,000ms

At 1,000 RPS:

  • Bifrost P99: 1.2s (stable)
  • LiteLLM: Crashes (memory exhaustion)

Full benchmarks on GitHub

When to Choose LiteLLM

LiteLLM is better if you:

  • Already have Python infrastructure
  • Need rapid prototyping (Python is faster to write)
  • Traffic is <100 RPS
  • Team has no Go experience
  • Need extensive Python library integrations

When to Choose Bifrost

Bifrost is better if you:

  • Run production traffic >500 RPS
  • Need P99 latency <1 second
  • Want minimal memory footprint
  • Require enterprise-grade performance
  • Need adaptive load balancing
  • Want zero-overhead observability

Architecture Differences

LiteLLM:

  • Python FastAPI framework
  • Async/await concurrency model
  • Database for proxy state
  • Extensive dependency tree

Bifrost:

  • Native Go HTTP server
  • Goroutine concurrency
  • Stateless by design
  • Minimal dependencies

Cost Implications

Scenario: 1,000 RPS sustained traffic

With LiteLLM:

  • Need 3x t3.xlarge instances (memory constraints)
  • Cost: ~$500/month
  • Still seeing elevated P99 latencies

With Bifrost:

  • Single t3.large instance sufficient
  • Cost: ~$60/month
  • P99 latency <1s

Savings: $440/month ($5,280/year)

Observability

LiteLLM:

  • Optional integration with LangSmith, others
  • Adds latency overhead
  • Requires additional setup

Bifrost:

  • Built-in observability
  • Zero latency impact (async logging)
  • Native Prometheus metrics
  • Real-time dashboard included

Plugin Systems

LiteLLM:

python

def pre_call_hook(request):
*# Custom Python logic*
return request

Bifrost:

go

func PreHook(ctx context.Context, req *Request) error {
*// Custom Go logic*
return nil
}

Both are extensible. Python is more flexible for rapid iteration, Go is faster for production.

Deployment Options

Both support:

  • Docker / Kubernetes
  • In-VPC deployment
  • Cloud platforms (AWS, GCP, Azure)
  • On-premises

Bifrost additionally offers:

  • Single binary deployment (no dependencies)
  • Smaller container images (50MB vs 500MB)
  • Lower CPU/memory requirements

Load Balancing

LiteLLM:

  • Round-robin or weighted round-robin
  • Static weights
  • Manual configuration

Bifrost:

  • Adaptive load balancing
  • Performance-based weight adjustment
  • Automatic degraded key detection
  • Real-time weight updates

Community & Support

LiteLLM:

  • Larger community (older project)
  • More Stack Overflow content
  • Active Slack/Discord

Bifrost:

Migration Path

Switching is straightforward. Both use OpenAI-compatible APIs:

Before (LiteLLM):

python

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

After (Bifrost):

python

response = openai.ChatCompletion.create(
api_base="http://bifrost:8080/v1",
model="gpt-4",
messages=[{"role": "user", "content": "Hello"}]
)

Just point to Bifrost's endpoint. No code changes.

The Honest Take

For prototypes and low-traffic apps: LiteLLM is fine. Python is easier to iterate on.

For production at scale: Bifrost is objectively faster, more efficient, and more reliable.

The 54x P99 latency difference isn't marketing. It's measured, reproducible, and matters.

Try Both

LiteLLM:

bash

pip install litellm
litellm --model gpt-4

Bifrost:

bash

git clone https://github.com/maximhq/bifrost
cd bifrost && docker compose up

Run your own benchmarks. See the difference.

The Bottom Line

Criteria Winner
Performance Bifrost (54× faster P99)
Memory efficiency Bifrost (68% less)
Ease of setup Tie (both are easy)
Python ecosystem LiteLLM
Production reliability Bifrost
Cost efficiency Bifrost (8× cheaper at scale)

⭐ Star Bifrost on GitHub

Read the full comparison →

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