Best AI Gateways for LLM Workloads: A Buyer's Guide

Enterprises building on top of LLMs tend to hit the same wall within months: application teams integrate directly against provider SDKs, and there is no unified way to control spend, enforce access, fail over between providers, or trace a request across models. An AI gateway for LLM workloads solves this by sitting between applications and providers as a single control point for routing, governance, caching, and observability. Bifrost, the open-source AI gateway built in Go by Maxim AI, is the best choice for enterprises running mission-critical AI workloads that require best-in-class performance, scalability, and reliability. This buyer's guide covers what to evaluate, how the leading AI gateways compare, and how to choose one for enterprise LLM workloads.

Why Enterprises Building on LLMs Need an AI Gateway

An AI gateway for LLM workloads is a unified infrastructure layer that routes, authenticates, governs, and observes traffic to multiple LLM providers from a single API. For teams building on top of LLMs, it replaces scattered per-provider integrations with one control point that platform, security, and finance teams can all reason about.

The need grows with agentic adoption. Gartner predicts that 40% of enterprise applications will be integrated with task-specific AI agents by the end of 2026, up from less than 5% in 2025. Agentic workloads multiply the number of model and tool calls per task, which makes gateway-level control over cost, access, and reliability a production requirement rather than an optimization. A centralized governance layer gives teams request attribution, enforced budgets, automatic failover, and audit trails that individual provider integrations cannot provide.

What to Evaluate in an AI Gateway for LLM Workloads

A buyer's evaluation should weigh how a gateway behaves under real production load, not just its feature checklist. The criteria below matter most for enterprises building on top of LLMs:

• Latency overhead: the added latency per request under sustained throughput. Agents make many calls per task, so microseconds compound quickly.
• Provider and model breadth: first-class support for OpenAI, Anthropic, AWS Bedrock, Google Vertex AI, and others behind a single, consistent API.
• Reliability: automatic failover, weighted load balancing, and health-aware routing across providers and API keys.
• Governance and access control: per-team and per-user budgets, rate limits, virtual keys, role-based access control, and data-level scoping.
• Compliance and audit: immutable audit logs and controls that map to SOC 2, GDPR, HIPAA, and ISO 27001.
• Agentic support: native Model Context Protocol (MCP) handling for tools and agents.
• Deployment model: self-hosted, in-VPC, air-gapped, or fully managed, based on data-residency requirements.
• Total cost of ownership: license model, self-host versus managed pricing, and how much extra tooling you must build around the gateway.

The LLM Gateway Buyer's Guide maps these dimensions to a full capability matrix. Two criteria separate production-grade gateways from prototyping tools: measured latency under load, and whether governance is native or bolted on.

AI Gateway Comparison for LLM Workloads

The market includes open-source, self-hosted gateways and managed, hosted services. The comparison below covers five options enterprises building on LLMs commonly evaluate, with Bifrost first for enterprise-scale workloads.

A short read on where each fits:

• Bifrost is the open-source Bifrost gateway built in Go, with the lowest measured overhead and a complete governance and reliability stack, suited to enterprise and regulated workloads.
• LiteLLM is a Python-native proxy with a broad provider catalog, strongest for prototyping and lightweight services; teams typically add separate layers for compliance-grade governance and high-throughput performance, and often evaluate a drop-in LiteLLM alternative as they scale.
• Kong AI Gateway adds LLM routing as a plugin on Kong's API gateway, a fit for teams already running Kong, though AI-specific cost and governance are frequently handled outside the gateway.
• Cloudflare AI Gateway is a managed, edge-cached proxy that is quick to adopt for caching and analytics, with the trade-off that traffic transits a hosted control plane.
• OpenRouter offers single-API access to a large model catalog, optimized for breadth and experimentation rather than enterprise governance.

Bifrost: The Best AI Gateway for Enterprise LLM Workloads

Bifrost is built for enterprises running mission-critical AI workloads that require best-in-class performance, scalability, and reliability. It serves as a centralized AI gateway to route, govern, and secure all AI traffic across models and environments with ultra low latency. Bifrost unifies LLM gateway, MCP gateway, and Agents gateway capabilities into a single platform. Designed for regulated industries and strict enterprise requirements, it supports air-gapped deployments, VPC isolation, and on-prem infrastructure. It provides full control over data, access, and execution, along with robust security, policy enforcement, and governance capabilities.

In practice, that translates into a few concrete advantages for teams building on top of LLMs:

• Performance at scale: access to over 1,000 models through one OpenAI-compatible API, with 11 microseconds of overhead per request at 5,000 requests per second in sustained benchmarks.
• Low-friction adoption: use Bifrost as a drop-in replacement for existing SDKs by changing only the base URL.
• Agentic infrastructure: an MCP gateway that connects external tool servers and exposes tools to clients, with agent and code execution modes.
• Enterprise governance and deployment: virtual keys, budgets, rate limits, role-based access control, immutable audit logs, and in-VPC or air-gapped deployment for regulated environments.

Deployment and Procurement Considerations

Deployment model is often the deciding factor for enterprises building on LLMs, because it determines where model traffic and logs live. Managed, hosted gateways minimize operational work but route requests through a third-party control plane, which conflicts with data-residency, air-gapped, or strict compliance requirements. Self-hosted gateways keep data and execution inside your infrastructure at the cost of running the service yourself.

When comparing total cost of ownership, look past the sticker price:

• Build-around cost: a gateway with basic governance forces you to build access control, budgeting, and audit tooling separately.
• Performance headroom: interpreter-based runtimes add latency under sustained load, which can raise infrastructure spend as traffic grows.
• License and lock-in: open-source, self-hosted options such as the Bifrost platform avoid per-seat or per-request pricing and keep the deployment under your control.
• Compliance readiness: native audit logs and access controls shorten the path to SOC 2, GDPR, HIPAA, and ISO 27001 reviews.

For regulated or high-scale environments, clustering and high-availability and in-VPC deployment are baseline requirements, not add-ons.

How to Choose the Right AI Gateway

The right AI gateway for LLM workloads depends on where you are on the scale curve. For a single team prototyping, a lightweight proxy or a hosted routing service is often enough. For AI that spans teams, touches regulated data, or runs agents in production, prioritize latency under load, native governance, and a deployment model you control.

Use this checklist when evaluating options:

• Do you need self-hosted, in-VPC, or air-gapped deployment for compliance?
• Will agents and MCP tools be part of your workloads?
• Do you require per-user and per-team budgets, rate limits, and audit logs?
• What latency overhead can your workloads absorb at peak throughput?
• How much tooling would you have to build around the gateway to meet enterprise requirements?

Answering these against the criteria above, and against the governance and buyer resources for each option, points most enterprises building on top of LLMs toward a self-hosted, low-overhead gateway with native governance, aligned with recognized frameworks such as the NIST AI Risk Management Framework.

Getting Started with Bifrost

Choosing the best AI gateway for LLM workloads comes down to performance under load, native governance, and a deployment model that fits your compliance needs. Among the options in this buyer's guide, Bifrost leads on the dimensions that matter most for enterprises building on top of LLMs: low overhead, native agentic support, full governance, and self-hosted or in-VPC deployment. To see how the open-source Bifrost gateway fits your AI infrastructure, book a demo with the Bifrost team.