Best AI Gateways with Multi-LLM Support for Enterprises

Production AI applications running across multiple LLM providers require a unified gateway to handle routing, failover, access governance, and cost controls without adding fragile custom code to each service. Bifrost, an open-source AI gateway built in Go by Maxim AI, routes requests across 1,000+ models from 23+ providers through a single OpenAI-compatible API, adding only 11 microseconds of overhead at 5,000 requests per second. Bifrost is available on GitHub, and the full documentation covers deployment and configuration in detail.

This post examines four enterprise AI gateways with multi-LLM support: Bifrost, LiteLLM, Cloudflare AI Gateway, and Kong AI Gateway. For each, the key capabilities, deployment model, and fit are covered so engineering and platform teams can evaluate the right option for their stack.

Why Enterprises Need an AI Gateway for Multi-LLM Deployments

An AI gateway is a routing and governance layer that sits between application services and LLM provider APIs. Unlike a generic API proxy, an enterprise AI gateway understands token-based pricing, streaming responses, and provider-specific rate limits.

The core infrastructure gaps that drive enterprise adoption:

• Provider reliability: No LLM provider guarantees 100% uptime. A gateway with automatic failover routes around outages without changes to application code.
• Cost control: Model pricing varies significantly across providers and tiers. Semantic caching and cost-based routing reduce spend on repeated or similar queries.
• Access governance: Enterprise teams need per-team and per-application API key management, budget caps, rate limits, and audit trails, particularly in regulated industries.
• Multi-provider flexibility: Switching providers or adding new models should not require application code changes. A unified OpenAI-compatible API abstracts provider differences at the gateway layer.
• Agentic workloads: Teams running AI agents that call external tools need a gateway that handles the Model Context Protocol, not just HTTP to LLM endpoints.

Gartner projects that 70% of organizations building multi-LLM applications will adopt AI gateway capabilities by 2028, citing unified access control and observability as primary drivers.

Top Enterprise AI Gateways with Multi-LLM Support

1. Bifrost

Bifrost is an open-source AI gateway engineered for production AI workloads at enterprise scale. Written in Go, it delivers approximately 11 microseconds of gateway overhead at 5,000 requests per second, documented in published performance benchmarks. It connects to 20+ providers including OpenAI, Anthropic, AWS Bedrock, Azure OpenAI, Google Vertex AI, Groq, Mistral, Cohere, and Ollama, giving teams access to 1,000+ models through a single endpoint.

Multi-LLM routing and failover

Automatic failover routes requests to the next available provider or model when a provider returns errors or exceeds rate limits. Routing rules support weighted provider distribution, latency-based selection, and custom metadata-driven routing logic. Adaptive load balancing adjusts distribution dynamically using real-time health signals from each provider endpoint, rather than relying on static weights.

Semantic caching

Semantic caching identifies semantically similar queries and serves cached responses, reducing both costs and latency for workloads with repeated or near-duplicate inputs. This is particularly effective in RAG pipelines and customer-facing AI applications where query patterns repeat across users.

MCP gateway

Bifrost functions as an MCP gateway, acting as both an MCP client and server. It connects AI models to external tools and data sources via the Model Context Protocol, with OAuth 2.0 authentication and tool filtering per virtual key. Code Mode reduces token usage by approximately 50% and latency by 40% by having the model generate Python to orchestrate multi-tool workflows instead of calling each tool sequentially. Teams building on coding agents such as Claude Code, Cursor, Codex CLI, and Gemini CLI can route those agents through Bifrost for cost governance and access controls without reconfiguring each tool individually.

Enterprise governance

Virtual keys are the primary governance entity in Bifrost. Each virtual key enforces a defined set of access permissions, budget caps, and rate limits for a team, service, or application. Budget management supports hierarchical cost controls at the virtual key, team, and customer levels.

Audit logs produce immutable request trails for SOC 2, GDPR, HIPAA, and ISO 27001 compliance. Vault support integrates with HashiCorp Vault, AWS Secrets Manager, Google Secret Manager, and Azure Key Vault for secure credential storage. In-VPC deployments and high-availability clustering meet regulated-industry and air-gapped deployment requirements. For enterprises evaluating compliance posture in detail, the Bifrost Enterprise page covers deployment architecture and security controls.

Observability

Bifrost exports Prometheus metrics natively, supports OpenTelemetry (OTLP) for distributed tracing, and integrates with Grafana, Datadog, New Relic, and Honeycomb. Log exports send request data to storage systems and data lakes on a configurable schedule.

SDK compatibility

Bifrost works as a drop-in replacement for the OpenAI, Anthropic, AWS Bedrock, Google GenAI, and LiteLLM SDKs by changing only the base URL. LangChain and PydanticAI integrations are also supported. Custom Go and WASM plugins extend the gateway with organization-specific middleware logic.

Best for: 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.

2. LiteLLM

LiteLLM is an open-source Python library and proxy server providing a unified OpenAI-compatible interface across 100+ LLM providers. It is widely adopted for development and prototyping workflows due to its broad provider support and straightforward setup.

Core capabilities include virtual key management, per-key spend tracking, basic load balancing, and retry/fallback logic. LiteLLM supports both a Python SDK for direct library integration and a proxy server mode for centralized routing.

Limitations become relevant at production scale. Written in Python, LiteLLM introduces higher per-request overhead compared to Go-based gateways. Teams running high-throughput workloads or requiring predictable sub-millisecond gateway latency encounter this as a constraint. MCP gateway support, native compliance audit logging, vault-backed credential management, and in-VPC clustering are not available.

For teams already using the LiteLLM SDK and evaluating a migration path to a production-grade gateway, Bifrost supports the LiteLLM SDK as a drop-in integration. A detailed LiteLLM alternative comparison is available for teams mapping the transition.

Best for: Python-heavy teams in active development and early-stage prototyping who need broad provider access with minimal configuration.

3. Cloudflare AI Gateway

Cloudflare AI Gateway routes LLM traffic through Cloudflare's global edge network. It supports major providers including OpenAI, Anthropic, Google, Groq, and xAI, covering roughly 350 models across six providers.

Key capabilities include edge caching to reduce latency for geographically distributed users, DDoS protection, rate limiting, and a basic analytics dashboard covering per-model usage and error rates. Cloudflare AI Gateway integrates naturally with teams running on Cloudflare Workers and the broader Cloudflare platform.

Compared to dedicated enterprise AI gateways, governance tooling is minimal. There is no virtual key system with per-team budget enforcement, no native MCP gateway functionality, no compliance-grade audit logging, and no support for in-VPC or air-gapped deployments. Provider coverage is narrower, at six providers versus 20+ in purpose-built gateways.

Best for: Teams already running on Cloudflare infrastructure who need basic multi-LLM routing with edge caching and minimal operational overhead.

4. Kong AI Gateway

Kong AI Gateway extends the Kong API platform with LLM-specific capabilities. It provides a provider abstraction layer, routing policies, and access to the Kong plugin ecosystem for teams already standardized on Kong for API management.

Core features include provider abstraction across major LLM APIs, traffic routing and fallback policies, multi-cloud deployment support, and integration with Kong's existing security and observability plugins. RAG pipeline automation support is also included.

Kong AI Gateway suits teams with an existing Kong deployment who want to add LLM routing without introducing a separate gateway product. LLM-specific capabilities such as semantic caching, native MCP gateway support, token-aware hierarchical governance, and built-in compliance audit trails are more limited compared to gateways built specifically for AI workloads.

Best for: Teams with an existing Kong API management deployment who want to extend it with LLM routing capabilities using familiar Kong tooling.

Platform Comparison

How to Choose the Right Enterprise AI Gateway

The right gateway depends on the production requirements your team needs to satisfy:

Performance at scale: If request volume is high and gateway latency matters, the runtime language and architecture set the performance ceiling. Bifrost's Go-based design adds roughly 11 microseconds per request under sustained load. The Bifrost benchmark results document throughput and overhead in detail.

Compliance and governance: SOC 2, HIPAA, and GDPR requirements need more than request logging. Immutable audit trails, vault-backed credential storage, RBAC, and in-VPC deployment options are baseline requirements in regulated industries. The LLM Gateway Buyer's Guide covers a full governance capability matrix for evaluating gateway options.

Agentic workloads and MCP: Teams deploying AI agents that call external tools need a gateway that handles the Model Context Protocol natively, including tool filtering, OAuth, and access governance per consumer. The Bifrost MCP gateway is purpose-built for this, supporting both client and server roles in MCP workflows.

Existing infrastructure fit: Teams already committed to Kong API management or Cloudflare Workers will see faster initial adoption with those platforms, at the cost of more limited AI-specific features.

Migration from LiteLLM: Teams outgrowing LiteLLM can migrate to the Bifrost AI gateway with minimal code changes. The LiteLLM alternative comparison covers the feature-by-feature migration path in detail.

Start with Bifrost

Bifrost deploys without configuration files and connects to 20+ LLM providers through a single API endpoint. For teams working through a structured gateway evaluation, the LLM Gateway Buyer's Guide provides a detailed capability matrix. To see how Bifrost fits your production requirements, book a demo with the team.