Migrating from LiteLLM to Bifrost

Performance at a Glance

Lower P99 Latency

54x Consistently fast response times

Uptime SLA

99.999% Automatic failover & circuit breakers

Providers

1000+ models supported natively

Migration Time

15 min Drop-in OpenAI-compatible API

Why Migrate to Bifrost?

While LiteLLM works well for prototyping, teams scaling to production need infrastructure that doesn't become a bottleneck.

• Performance54x Faster Performance. Built in Go with just 1.68s overhead at 500 RPS compared to 90.72s for Python-based solutions (a ~54x lower P99 latency). Your gateway stops being the bottleneck.
• ReliabilityProduction-Ready Reliability. 99.999% uptime SLA with automatic failover, circuit breakers, and intelligent retry logic. No more multi-minute latency spikes at high load.
• Cost controlCost Optimization. Semantic caching reduces costs and latency on repeated queries. Adaptive load balancing ensures efficient resource utilization.
• SecurityEnterprise Security. Virtual keys with budgets, RBAC, audit logs, and in-VPC deployments. Full control over your AI infrastructure.
• ObservabilityNative Observability. Built-in Prometheus metrics, OpenTelemetry support, and integration with Maxim's evaluation platform. No sidecars needed.
• MigrationDrop-in Replacement. OpenAI-compatible API means zero code changes. Point your existing LiteLLM integration to Bifrost and you're done.

Performance Comparison

Tested on identical AWS t3.xlarge instances. Bifrost delivers consistent, predictable performance under load.

Feature Comparison

Setup Steps

• Zero code changes. OpenAI-compatible API means your existing integrations work as-is.
• LiteLLM SDK compatible. You can even point the LiteLLM Python SDK at Bifrost as a proxy.
• Provider prefix routing. Use the openai/gpt-4o format for explicit provider control.

1. 01Install Bifrost. Choose your preferred method. Bifrost starts immediately with zero configuration needed.

   # Option 1: NPX (fastest)
   npx -y @maximhq/bifrost
   # Option 2: Docker
   docker pull maximhq/bifrost
   docker run -p 8080:8080 maximhq/bifrost

2. 02Configure providers. Add your LLM provider API keys via the web UI at localhost:8080 or a configuration file.

   # navigate to http://localhost:8080
   # click "Providers" in the sidebar
   # add API keys for OpenAI, Anthropic, etc.
   # configure models and fallback chains

3. 03Update base URL. Change one line in your application. Bifrost's OpenAI-compatible API means zero other code changes.

   # Before (LiteLLM)
   # base_url="http://localhost:4000"
   # After (Bifrost)
   base_url="http://localhost:8080"

One Line Change

import openai

client = openai.OpenAI(
    api_key="your-litellm-key",
    base_url="http://localhost:4000"
)

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

import openai

client = openai.OpenAI(
    api_key="your-bifrost-key",
    base_url="http://localhost:8080"
)

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

Common Migration Scenarios

# LiteLLM virtual keys for team budgets map directly
# to Bifrost's equivalent functionality.
curl -X POST http://localhost:8080/api/keys \
  -H "Content-Type: application/json" \
  -d '{
    "name": "team-engineering",
    "budget": 1000,
    "rate_limit": 100,
    "models": ["openai/gpt-4o",
      "anthropic/claude-sonnet-4-20250514"]
  }'

# Use the standard OpenAI SDK pointed at Bifrost.
import openai

client = openai.OpenAI(
    base_url="http://localhost:8080",
    api_key="your-key"
)

# Use the LiteLLM Python SDK with Bifrost as the proxy backend.
import litellm

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

You Should Migrate If

• Scaling beyond prototyping. Performance matters at production traffic levels.
• Building multi-step agent architectures. Overhead compounds with each LLM call.
• Need enterprise governance. Budget management, access control, and audit trails.
• Want integrated observability. Maxim platform provides unmatched visibility.
• Experiencing reliability issues. Timeout spikes, memory issues, or unpredictable latency.
• Need better cost control. Semantic caching and adaptive load balancing.

Open Source & Enterprise

FAQ

Related resources

• [Benchmark: Bifrost vs LiteLLM]
• [Alternative: LiteLLM Alternative]
• [Source: Migrating From Litellm]
• [Docs: Bifrost docs]
• [GitHub: maximhq/bifrost]
• [Pricing: Bifrost pricing]
• [Enterprise: Bifrost enterprise]
• [Book a Demo: Bifrost demo]
• [Resources: Bifrost resources]