This guide compares the top 9 LLM gateways for building, deploying, and managing AI agents. For teams focused on performance, security, and advanced tool use with protocols like MCP, Bifrost is the best overall choice for production agentic workflows.
As AI agents move from single-prompt applications to complex, multi-step workflows, the infrastructure that supports them must evolve. AI agents need to interact with external tools, APIs, and other agents, creating a complex web of communication that can be difficult to manage, secure, and observe. An LLM gateway, or agent gateway, provides a centralized control plane for this traffic, solving challenges around security, cost, and operational complexity.
This article reviews the best LLM gateways available today, with a focus on their suitability for agentic workflows. We will evaluate them based on their support for multi-provider models, reliability features like failover and load balancing, observability, and, most importantly, their native support for agent-specific protocols like the Model Context Protocol (MCP).
What is an LLM Gateway for AI Agents?
An LLM gateway is a proxy layer that sits between AI applications and the various services they interact with, including LLM providers, vector databases, and external tools. For agentic workflows, this role expands significantly. An "agent gateway" must not only manage LLM calls but also govern how agents discover and use tools, enforce access control on sensitive data, and provide a complete audit trail of every action an agent takes.
The Model Context Protocol (MCP) is a key standard in this ecosystem, defining a structured way for models to discover and interact with external tools. A gateway that natively understands and manages MCP traffic is essential for building scalable and secure agent-based systems.
The Top 9 LLM Gateways
Here is a breakdown of the best LLM gateways, ranked based on their capabilities for supporting production-grade AI agents.
1. Bifrost
Bifrost is a high-performance, open-source AI gateway written in Go, specifically designed for high-concurrency AI workloads. It unifies LLM provider routing, security, and observability with first-class support for MCP, making it the top choice for demanding agentic applications.
Its key advantage is performance. Bifrost adds only ~11 microseconds of overhead per request, making it one of the fastest gateways available for real-time agent interactions. This is critical for agents that need to make multiple tool calls in rapid succession.
Best for: Enterprises and teams building high-throughput, production-grade AI agents that require low latency, robust governance, and native support for both LLM and MCP traffic.
Key Features:
- High Performance: Built in Go, Bifrost is architected for high-concurrency workloads and minimal latency.
- Unified LLM and MCP Gateway: Manages both requests to over 20 LLM providers (OpenAI, Anthropic, Bedrock, etc.) and tool calls via MCP from a single control plane.
- Advanced Agent Modes: Features like "Code Mode" can reduce token costs for complex tool orchestration by up to 92% by having the model generate execution code instead of verbose JSON.
- Enterprise-Grade Governance: Offers virtual keys, fine-grained access control for MCP tools, audit logs, and security guardrails.
- Drop-in Integration: Fully OpenAI-compatible, allowing integration with existing SDKs and CLI agents like Claude Code and Codex CLI by changing only the base URL.
2. LiteLLM
LiteLLM is a popular open-source Python library and proxy server that provides a unified interface for over 100 LLM providers. It excels at abstracting away the differences between various model APIs, making it easy to switch providers without changing application code.
Best for: Development teams and startups that need maximum flexibility in experimenting with a wide variety of LLMs and want a simple, open-source solution.
Key Features:
- Broad Provider Support: The most extensive provider support of any gateway, making it ideal for testing and prototyping with diverse models.
- OpenAI-Compatible API: Simplifies integration by providing a consistent interface for all supported providers.
- Production Proxy: The self-hosted proxy offers features like virtual key management, cost tracking, and rate limiting.
- Community Driven: As an active open-source project, it evolves quickly and has strong community support.
3. Kong AI Gateway
Kong AI Gateway extends the well-known Kong API gateway with AI-specific capabilities. It is a strong choice for enterprises that have already standardized on Kong for their microservices architecture and want to apply similar governance to their AI traffic.
Best for: Large enterprises, especially those already using Kong for API management, that need to enforce consistent governance and security policies across both traditional APIs and new AI services.
Key Features:
- Unified Governance: Apply consistent policies for authentication, rate limiting, and observability across all API and AI traffic.
- Multi-LLM Orchestration: Route requests to different models based on latency, cost, or performance patterns.
- Advanced AI Features: Includes capabilities like semantic caching, PII sanitization, and automated RAG injection.
- Extensibility: Leverages Kong's extensive plugin ecosystem to add custom functionality.
4. Cloudflare AI Gateway
Cloudflare AI Gateway is a managed service that provides caching, rate limiting, and analytics for AI applications. Its biggest strength is leveraging Cloudflare's massive global network to reduce latency and provide insights into AI traffic patterns.
Best for: Teams building applications on Cloudflare's serverless platform (Workers AI) or those who want a simple, managed solution for caching and observing LLM requests at the edge.
Key Features:
- Global Caching: Caches responses on Cloudflare's edge network to reduce latency and cost for repeated queries.
- Real-time Analytics: Provides a dashboard for monitoring requests, users, costs, and errors.
- Easy Setup: As a managed service, it requires minimal configuration to get started.
- Provider Agnostic: Works with any LLM provider.
5. OpenRouter
OpenRouter is a managed API gateway that offers access to hundreds of different AI models through a single, unified API. It functions as a marketplace and router, allowing developers to find and use the best model for a given task without managing multiple API keys and billing relationships.
Best for: Developers and small teams building agentic applications that need access to a very wide range of models, including many open-source and fine-tuned variants, with simple, pay-as-you-go pricing.
Key Features:
- Massive Model Selection: Access to over 500 models from dozens of providers through one API key.
- Smart Routing: Can automatically route requests to the most cost-effective model that meets performance criteria.
- OpenAI Compatibility: Easy to integrate into existing applications with a simple base URL change.
- Developer-Focused SDKs: Provides SDKs to simplify integration with agent frameworks.
6. Databricks Unity AI Gateway
The Databricks Unity AI Gateway extends Databricks' Unity Catalog to provide governance for AI models and agents. It is deeply integrated into the Databricks ecosystem, making it a natural choice for organizations that use Databricks for their data and AI workloads.
Best for: Organizations that have standardized on the Databricks platform and need to govern the entire lifecycle of their data and AI assets, from data pipelines to production agent interactions.
Key Features:
- Unified Data and AI Governance: Manages access to models, agents, and tools alongside data assets within Unity Catalog.
- Centralized Monitoring: Tracks prompts, traces, and token usage, logging everything to auditable inference tables.
- Cost Management: Provides granular cost attribution by user, team, or use case.
- Ecosystem Integration: Connects with AI security and identity providers to enforce runtime policies.
7. Amazon Bedrock
While not a traditional gateway, Amazon Bedrock functions as a managed service that provides access to a curated selection of foundation models through a single API. For teams building exclusively within the AWS ecosystem, it offers a simplified and secure way to access models from providers like Anthropic, Meta, and Cohere, as well as Amazon's own Titan models.
Best for: AWS-native organizations that want a managed, secure, and compliant way to access a variety of popular foundation models without leaving the AWS network boundary.
Key Features:
- Managed Service: AWS handles the infrastructure for hosting and serving the models.
- Security and Compliance: Inherits AWS compliance certifications like SOC 2 and HIPAA, with all traffic staying within the AWS network.
- Single API: Provides a unified API for interacting with models from different providers.
- Integration with AWS Services: Natively integrates with other AWS services like S3 for data and IAM for access control.
8. Google Vertex AI Model Garden
Similar to Bedrock, Google's Vertex AI Model Garden is a managed platform that provides access to over 100 foundation models from Google and third parties. It serves as a centralized repository where teams can discover, test, and deploy models within the Google Cloud ecosystem.
Best for: Organizations building on Google Cloud Platform that want a unified platform to discover, customize, and deploy a wide range of first-party and open-source models.
Key Features:
- Vast Model Catalog: Offers access to Google's own models (like Gemini) alongside popular open-source models.
- Managed MLOps: Integrated with Vertex AI's MLOps tools for model deployment, scaling, and monitoring.
- Customization: Allows for easy fine-tuning of models with proprietary data.
- Simplified Deployment: One-click deployment to a managed Vertex AI endpoint.
9. NVIDIA NeMo Guardrails
NVIDIA NeMo Guardrails is an open-source toolkit focused on adding programmable safety controls to LLM applications. While not a full gateway, it can be integrated with one to enforce conversational safety. It allows developers to define guardrails using a specialized language called Colang to prevent undesirable behavior, such as off-topic conversations or unsafe actions.
Best for: Teams that need to implement fine-grained, programmable safety and security policies for conversational agents, often used in conjunction with a more comprehensive LLM gateway.
Key Features:
- Programmable Guardrails: Define specific conversational boundaries and behaviors.
- Topical, Safety, and Security Rails: Enforce rules to keep conversations on-topic, prevent harmful content, and block connections to unauthorized external tools.
- Open-Source and Extensible: Can be customized and integrated into various application stacks.
Conclusion
Choosing the right LLM gateway is a critical infrastructure decision for any team building serious AI agents. While simple routing and caching are table stakes, the demands of agentic workflows—with their reliance on external tools and autonomous interactions—require more. For teams building for performance, security, and scalability, a gateway with native MCP support, low latency, and robust governance is essential. Based on these criteria, Bifrost stands out as the most complete solution for production agentic workloads, combining high-throughput performance with the deep, protocol-aware governance needed to manage complex AI systems safely.
Top comments (0)