Best LLM Router for Enterprise AI: Bifrost vs LiteLLM

TL;DR

Enterprise AI teams need LLM gateways that go beyond basic proxy functionality. Bifrost, built in Go by Maxim AI, delivers 50x faster performance than LiteLLM with just 11 µs overhead at 5,000 RPS. While LiteLLM works well for prototyping with its 100+ provider support, it struggles under production load due to Python's GIL limitations. Bifrost offers enterprise-grade governance, adaptive load balancing, semantic caching, built-in guardrails, and native observability without requiring external dependencies. For teams shipping production AI, Bifrost is the more capable choice.

Why LLM Routing Matters for Enterprise AI

No single LLM handles every use case well. Enterprise teams routinely work across multiple providers: OpenAI for general tasks, Anthropic for nuanced reasoning, AWS Bedrock for compliance-sensitive workloads, and open-weight models via Groq or Ollama for cost optimization.

Managing these providers directly means dealing with fragmented APIs, inconsistent authentication, varying rate limits, and zero failover logic. An LLM gateway centralizes routing, failover, cost tracking, and governance into a single layer. The question is: which gateway can handle production-scale traffic without becoming the bottleneck?

LiteLLM: The Python-First Proxy

LiteLLM is an open-source Python SDK and proxy server providing a unified OpenAI-compatible interface to 100+ LLM providers.

What LiteLLM does well:

• Broad provider support. Compatibility across 100+ providers including OpenAI, Anthropic, AWS Bedrock, Google Vertex, Azure, Cohere, and HuggingFace.
• OpenAI-compatible API. All responses are standardized into OpenAI's output format, simplifying integration.
• Cost tracking. Spend tracking per virtual key and project for basic cost attribution.
• Observability integrations. Callbacks to tools like Langfuse, MLflow, and Helicone.
• Strong community. With 33,000+ GitHub stars, LiteLLM has active adoption and regular updates.

For early-stage teams prototyping multi-model setups in Python, LiteLLM is a practical starting point.

Where LiteLLM Hits Its Limits

Production workloads expose architectural constraints that don't surface during prototyping.

Performance ceiling. Python's Global Interpreter Lock prevents true parallelism. In benchmark tests, LiteLLM starts failing at 500 RPS with latency climbing past 4 minutes. At 5,000 RPS, it becomes impractical.

External dependencies. Production deployments often require Redis for caching, PostgreSQL for storage, and additional tooling for logging, adding operational complexity and failure points.

Limited governance. Virtual keys and spend limits exist, but hierarchical budget management, SSO, audit logs, and enterprise policy enforcement are missing.

Basic guardrails. LiteLLM offers keyword blocking and regex-based content filtering, but lacks real-time content moderation or integration with services like AWS Bedrock Guardrails.

Bifrost: Built for Production from Day One

Bifrost is an open-source LLM gateway written in Go, purpose-built for production AI infrastructure.

Performance That Scales

Bifrost adds just 11 µs of overhead per request at 5,000 RPS. That's 50x faster than LiteLLM on identical hardware. Go's native concurrency model handles thousands of simultaneous connections without the GIL bottleneck that limits Python-based proxies. At enterprise scale, even small per-request overhead compounds into measurable tail latency and infrastructure cost increases.

Enterprise Governance

Bifrost provides hierarchical governance designed for multi-team organizations: virtual keys with independent budgets, customer-level and team-level budget hierarchies, SSO via Google and GitHub, audit logs for SOC 2/GDPR/HIPAA/ISO 27001 compliance, and role-based access control with fine-grained permissions. This is the governance structure enterprise compliance teams actually need.

Built-in Guardrails

Bifrost integrates with AWS Bedrock Guardrails, Azure Content Safety, and Patronus AI for real-time output moderation. A custom plugin system allows teams to inject organization-specific safety logic as middleware.

Semantic Caching and Intelligent Routing

Semantic caching identifies similar queries and returns cached responses, cutting redundant API calls. Adaptive load balancing distributes requests based on real-time success rates, latency, and capacity. When a provider fails, automatic failover reroutes traffic with zero downtime.

Native Observability and MCP Support

Prometheus metrics, distributed tracing, and a built-in web UI provide monitoring without external dependencies. When paired with Maxim's observability suite, teams get full visibility across cost, latency, and output quality. Native MCP gateway support centralizes tool connections, governance, and auth for agentic AI applications.

Feature Comparison

Who Should Use What?

Choose LiteLLM if you're prototyping multi-model setups in Python, need access to niche model providers, and aren't yet dealing with high-throughput production traffic.

Choose Bifrost if you're running production AI workloads where latency, uptime, and governance matter. Teams handling 500+ RPS, operating in regulated industries, or building agentic systems requiring guardrails and MCP support will find Bifrost significantly more capable. Migration is a one-line change to your existing OpenAI or Anthropic SDK calls.

Gateway + Observability: The Complete Picture

An LLM gateway solves routing and reliability. Knowing whether your AI is actually performing well requires evaluation and observability at the application layer.

Bifrost integrates natively with Maxim AI's evaluation and observability platform, giving teams a unified workflow from gateway to production monitoring. Cost data, latency metrics, and model behavior feed directly into trace monitoring, evaluation workflows, and quality dashboards. Teams can test routing strategies, measure output quality, and optimize model selection based on real production data.

Getting Started

# Install and run locally
npx -y @maximhq/bifrost

# Or use Docker
docker run -p 8080:8080 maximhq/bifrost

Change one line in your existing SDK:

# Before
base_url = "<https://api.openai.com>"

# After
base_url = "<http://localhost:8080/openai>"

Explore the Bifrost documentation, check out the GitHub repository, or book a demo to see how Bifrost and Maxim work together for production AI infrastructure.