LiteLLM alternatives

Gateway overview

What LiteLLM Is

LiteLLM is an open-source, Python-based LLM proxy that provides a unified OpenAI-compatible API for routing requests across multiple LLM providers.

LiteLLM is useful for prototyping and multi-provider experimentation. Higher-scale production workloads can require lower overhead, simpler operations, stronger governance, native MCP controls, and built-in guardrails. [Migrating from LiteLLM] [LiteLLM alternative resource]

Published comparison metrics

Bifrost Performance At A Glance

Faster throughput

9.5x More requests processed per second in the Bifrost vs LiteLLM comparison. [Benchmarks]

Lower P99 latency

54x Lower P99 latency in the Bifrost vs LiteLLM comparison.

Less memory

68% Lower memory usage in the Bifrost vs LiteLLM comparison.

Less overhead

40x Lower gateway processing overhead in the Bifrost vs LiteLLM comparison.

LiteLLM Strengths

• Unified provider access. LiteLLM provides a single API for multiple LLM providers with an OpenAI-compatible interface.
• Self-hosted and open source. LiteLLM can be self-hosted and gives teams control over deployment and data flow.
• Broad provider catalog. LiteLLM supports a broad set of LLM APIs across major and niche providers.
• Strong community. LiteLLM is widely used across developer communities and has active open-source adoption.

LiteLLM Production Challenges

• Python GIL bottleneck. Python runtime limits can create concurrency bottlenecks under high load.
• Async overhead. Asyncio context switching and event-loop management can add overhead as request concurrency grows.
• Database dependency. Production LiteLLM deployments commonly require PostgreSQL and Redis, adding operational complexity.
• Limited enterprise governance. Teams may need additional engineering for native RBAC, workspaces, audit logs, and granular budget controls.

Bifrost vs LiteLLM Feature Comparison

Migration Path

Bifrost is a migration path for teams that need higher throughput, lower overhead, simpler deployment, native observability, governance, MCP support, and guardrails. [Gateway setup docs] [Drop-in replacement docs] [Migrating from LiteLLM resource]

1. 01Install Bifrost. Start Bifrost with NPX, Docker, or the Go SDK.

   # Option 1: NPX
   npx -y @maximhq/bifrost
   
   # Option 2: Docker
   docker run -p 8080:8080 maximhq/bifrost
   
   # Option 3: Go SDK
   go get github.com/maximhq/bifrost/core@latest

2. 02Configure providers. Use the web UI to add provider keys, configure models, set fallback chains, and define routing behavior.

   open http://localhost:8080

3. 03Update the endpoint. Point your compatible SDK or application base URL at Bifrost. The request format can remain OpenAI-compatible.

   curl http://localhost:8080/v1/chat/completions \
     -H "Content-Type: application/json" \
     -d '{"model":"openai/gpt-4o-mini","messages":[{"role":"user","content":"Hello!"}]}'

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

• Open Source. Bifrost is open source under the Apache 2.0 License. [GitHub]
• Publisher. Bifrost is published by H3 Labs Inc. and Maxim AI.
• Compliance. The site references SOC 2 Type II, GDPR, HIPAA, and ISO 27001 signals. [Enterprise deployment]
• Deployment. Enterprise resources cover VPC, on-premise, air-gapped, and multi-cloud use. [Enterprise deployment]

FAQ

Related resources

• [Alternatives hub]
• [Performance benchmarks]
• [LLM gateway buyer's guide]
• [LiteLLM migration]
• [MCP Gateway]
• [Governance]
• [Guardrails]
• [Pricing]
• [Docs]
• [GitHub]