Open Source LLM Gateways: How to Choose the Right One

An open source LLM gateway is a self-hostable proxy that routes, authenticates, governs, and observes traffic to multiple LLM providers through a single API. Teams adopt one when a single provider is no longer enough: 78% of companies now use two or more LLM families in production, according to a 2025 Menlo Ventures market update, and the share running three or more models climbed from 36% to 59% in a single quarter. Choosing among open source LLM gateways comes down to a small set of measurable criteria: latency overhead, provider coverage, governance, MCP support, deployment options, observability, and licensing. Bifrost, the open-source AI gateway built in Go by Maxim AI, is the best overall choice for enterprise teams that need all of these without trading performance for control. This guide provides the evaluation framework and shows where Bifrost leads.

What Is an Open Source LLM Gateway

An open source LLM gateway is a self-hosted routing layer that sits between your applications and LLM providers, exposing a unified API while adding failover, load balancing, caching, governance, and observability. Unlike a closed SaaS proxy, the source is public, the deployment runs in your own infrastructure, and the data path stays under your control.

The core value is decoupling application code from provider specifics. Instead of writing separate integrations for OpenAI, Anthropic, and AWS Bedrock, teams point every request at one endpoint and switch models by changing a string. Bifrost unifies access to 20+ providers and 1000+ models through a single OpenAI-compatible API, which means existing SDK code works as a drop-in replacement by changing only the base URL. That single-endpoint model is what lets teams switch providers without rewriting integrations.

Key Criteria for Evaluating Open Source LLM Gateways

When comparing open source LLM gateways, evaluate each candidate against seven criteria. These map to the operational risks that show up once a gateway carries production traffic.

• Performance and latency overhead: How many microseconds does the gateway add per request, and does that hold under sustained load?
• Provider coverage: How many providers and models are reachable through one API, and how quickly are new ones added?
• Governance: Can you enforce budgets, rate limits, and access control per team, project, or user?
• MCP support: Does the gateway act as a Model Context Protocol client and server for agentic tool use?
• Self-hosting and deployment: Can it run in a VPC, on-prem, air-gapped, or clustered for high availability?
• Observability: Does it emit OpenTelemetry traces and Prometheus metrics out of the existing stack?
• Community and licensing: Is the license permissive, the repository active, and the roadmap public?

The sections below expand each criterion and show how Bifrost performs against it.

Performance and Latency Overhead

Latency overhead is the first criterion because a gateway sits in the hot path of every request. A gateway that adds tens of milliseconds erases the benefit of routing to a faster model. The number that matters is per-request overhead measured under sustained concurrency, not a single-call demo.

Bifrost adds 11 microseconds of overhead per request at 5,000 requests per second in sustained benchmarks, with a 100% request success rate under that load. The published benchmarks run on standard AWS EC2 instances and are reproducible with the open-source benchmarking suite. Weighted API key selection resolves in roughly 10 nanoseconds, so key rotation does not become the bottleneck at scale. Because Bifrost is written in Go with worker pools and pre-allocated object pools, throughput stays flat as request volume rises rather than degrading.

Provider Coverage and Routing

Provider coverage determines how much of your model strategy a single gateway can serve. A gateway limited to two or three providers forces you to run parallel integrations for everything else, which defeats the purpose of a unified layer.

Bifrost supports 20+ providers, including OpenAI, Anthropic, AWS Bedrock, Google Vertex AI, Azure OpenAI, Google Gemini, Groq, Mistral, Cohere, Cerebras, and Ollama, through one interface. See the full supported providers matrix for per-operation coverage. Routing capabilities matter as much as raw coverage:

• Automatic fallbacks: When a primary provider fails after exhausting retries, Bifrost moves to the next provider in the fallback chain, each with its own retry budget.
• Load balancing: Weighted distribution across multiple API keys and providers spreads traffic and works around per-key rate limits.
• Retries with backoff: Transient 5xx errors trigger exponential backoff with jitter; permanent per-key failures rotate keys without waiting.

This routing behavior directly addresses the multi-provider reality that more than three-quarters of organizations now route tasks across models based on cost, latency, and complexity.

Governance and Access Control

Governance is where open source LLM gateways diverge sharply. A gateway without native budgets and access control pushes cost tracking and key management into application code, where it is inconsistent and hard to audit.

In Bifrost, virtual keys are the primary governance entity. Each virtual key carries its own access permissions, budgets, and rate limits, and can be scoped to specific providers, models, and API keys. Governance is hierarchical, so budgets apply at the virtual key, team, and customer levels together. The governance resource page covers how teams allocate spend and enforce limits across an organization. For larger deployments, Bifrost adds role-based access control and OIDC identity provider integration with Okta and Microsoft Entra, so gateway access maps to your existing directory.

MCP Support for Agentic Workflows

MCP support is a newer but increasingly important criterion as teams build agents. The Model Context Protocol, introduced by Anthropic in November 2024 and since adopted across the industry, is the open standard for connecting models to external tools. A gateway with native MCP support becomes the control point for every tool call, not just every model call.

Bifrost acts as both an MCP client and server: it connects to external tool servers and exposes those tools to clients such as Claude Desktop. Two modes matter for agent builders:

• Agent Mode: Autonomous tool execution with configurable auto-approval, so agents run multi-step workflows under policy.
• Code Mode: The model writes Python to orchestrate multiple tools in one execution, which reduces token usage.

Tool access is governed the same way model access is: MCP tool filtering runs per virtual key, so a given key only reaches the tools it is allowed to. Teams evaluating agent infrastructure can review the MCP gateway resource page, which details access control, cost governance, and the 92% lower token costs that Code Mode delivers at scale.

Self-Hosting and Deployment Options

Self-hosting is the reason many teams choose an open source LLM gateway over a hosted proxy in the first place. The deployment question is not just "can I run it," but "can I run it the way my compliance requirements demand."

Bifrost is positioned for enterprises and large teams with strict deployment needs. It supports in-VPC deployments across AWS, GCP, Azure, and Cloudflare, keeping the data path inside your private network. For high availability, clustering uses a peer-to-peer architecture with gossip-based state synchronization, automatic service discovery, and zero-downtime rolling updates. Regulated industries that require air-gapped or on-prem infrastructure can review the Bifrost Enterprise options, including audit logs for SOC 2, GDPR, HIPAA, and ISO 27001 compliance.

Observability

Observability determines whether you can debug a gateway once it carries real traffic. A gateway that is a black box turns every latency spike or cost anomaly into a guessing exercise. The criterion is whether the gateway emits standard telemetry that plugs into tools you already run.

Bifrost provides built-in real-time request monitoring plus native OpenTelemetry (OTLP) tracing that follows GenAI semantic conventions. It also exposes native Prometheus metrics for scraping and Push Gateway. Traces and metrics flow into Grafana, Datadog, New Relic, and Honeycomb without a separate agent, so LLM operations correlate with the rest of your application telemetry.

Community and Licensing

Community and licensing are the long-horizon criteria. A gateway is infrastructure you will depend on for years, so the health of its repository and the terms of its license carry as much weight as any feature.

Bifrost is fully open source and developed in the open on GitHub, where the codebase, issues, and roadmap are public. An active repository means security fixes and new provider support land quickly, and a permissive posture means you can audit, fork, and extend the code. For teams that need organization-specific behavior, Bifrost supports custom Go and WASM plugins in its middleware pipeline.

How Bifrost Compares to LiteLLM

Teams evaluating open source LLM gateways often start with LiteLLM, a widely used Python-based proxy. The main tradeoffs are performance and enterprise depth. Bifrost is built in Go for low overhead under sustained concurrency, adding 11 microseconds per request at 5,000 RPS, and ships governance, clustering, MCP support, and OIDC-backed access control as native capabilities. Teams migrating from a Python proxy can review the full comparison on the LiteLLM alternatives page, and existing LiteLLM SDK code can point at Bifrost with minimal changes.

Applying the Framework

The seven criteria give you a repeatable way to score any open source LLM gateway. In practice, the highest-scoring option is the one that holds low latency overhead under load while still offering deep governance and flexible deployment, since those rarely come together.

• Performance: Bifrost adds 11µs overhead at 5,000 RPS with a 100% success rate.
• Provider coverage: 20+ providers and 1000+ models through one OpenAI-compatible API.
• Governance: Virtual keys with hierarchical budgets, rate limits, RBAC, and OIDC.
• MCP support: Native MCP client and server with Agent Mode and Code Mode.
• Deployment: In-VPC, clustered, air-gapped, and on-prem options.
• Observability: Native OpenTelemetry and Prometheus.
• Licensing: Fully open source with an active public repository.

For a deeper capability matrix across gateways, the LLM gateway buyer's guide walks through each criterion in detail, and the Bifrost resources hub collects benchmarks, governance guides, and deployment references for evaluating open source LLM gateways.

Choosing the Right Open Source LLM Gateway

The right open source LLM gateway is the one that adds negligible latency, reaches every provider your teams use, governs access and spend natively, supports MCP for agentic workflows, deploys where your compliance rules require, and stays observable and open. Bifrost, the open-source Bifrost gateway built for enterprise AI workloads, meets all seven criteria without forcing a tradeoff between performance and control. To see how it fits your infrastructure, book a demo with the Bifrost team.