[A comparison of the top 10 AI gateways for platform engineering teams, focusing on performance, reliability, and enterprise features. This guide reviews tools like Bifrost, LiteLLM, and others to help teams choose the right solution for managing production AI workloads.]
An AI gateway is a centralized entry point that routes, secures, and observes traffic to large language models (LLMs) from multiple providers. For platform engineering teams, deploying a gateway is a critical step in managing the complexity, cost, and risk of production AI applications. A gateway standardizes access, enforces governance, and provides resilience against provider outages, making it an essential piece of modern AI infrastructure.
This article reviews the 10 best AI gateways available today, evaluated from the perspective of a platform engineering team responsible for scalability, reliability, and security. The analysis covers open-source and managed solutions, with a focus on features that support enterprise requirements.
Key Criteria for Evaluating AI Gateways
Platform teams should assess AI gateways on several core dimensions:
- Performance: The gateway's latency overhead and throughput under load. High-performance gateways add minimal latency (measured in microseconds) to each request.
- Reliability: Features like automatic provider failover, load balancing, and retries are essential for maintaining application uptime.
- Unified API: A single, consistent API endpoint for accessing a wide range of models from providers like OpenAI, Anthropic, Google, and AWS. An OpenAI-compatible API is the industry standard.
- Governance and Security: The ability to enforce access controls, budgets, and rate limits using virtual keys, and apply security policies like guardrails.
- Deployment Flexibility: Support for various deployment targets, including Kubernetes, in-VPC, on-premise, and air-gapped environments.
- Observability: Integrations with standard monitoring tools like Prometheus, OpenTelemetry, and Datadog for visibility into performance and usage.
The Top 10 AI Gateways
Based on these criteria, here is a detailed review of the leading AI gateways for platform engineering teams.
1. Bifrost
Bifrost is a high-performance, open-source AI gateway from Maxim AI, written in Go. It is designed for mission-critical enterprise workloads where performance and reliability are non-negotiable.
Bifrost's architecture adds only 11 microseconds of overhead per request at 5,000 requests per second, making it one of the fastest gateways available. It provides a unified, OpenAI-compatible API for over 20 providers, including all major public clouds and open-source model hosts like Ollama. Key features for platform teams include automatic fallbacks, weighted load balancing, and semantic caching.
For governance, Bifrost uses a system of virtual keys to manage access, budgets, and rate limits per user, team, or application. Its enterprise version adds features like high-availability clustering, RBAC with OIDC integration, and security guardrails. A unique capability is its native support for the Model Context Protocol (MCP), allowing it to function as a full-fledged MCP gateway for agentic applications. The platform's governance and security can be extended to employee devices with Bifrost Edge, which governs AI usage in desktop and web apps, providing a complete solution for both infrastructure and endpoint security.
- Best for: Enterprises and platform teams that require best-in-class performance, comprehensive governance, and flexible deployment options for mission-critical AI applications. Its unified LLM, MCP, and Agents gateway capabilities make it a strong foundation for complex AI systems.
2. LiteLLM
LiteLLM is a popular open-source library that provides a unified interface to call over 100 LLM APIs. It can be deployed as a lightweight proxy server, offering a simple way to standardize model access.
Its core strength is its broad provider support and ease of use. Teams can quickly set up a proxy to route requests to different models and manage API keys centrally. LiteLLM includes features like retries, fallbacks, and a basic caching implementation. It also offers a UI for managing keys and viewing usage logs. While it is highly flexible for development and small-scale projects, platform teams may find its production features, such as observability and high-availability deployments, require more manual setup compared to more integrated solutions.
- Best for: Teams looking for a simple, developer-friendly, and highly extensible open-source solution to unify access to a wide variety of LLM providers.
3. Kong AI Gateway
Kong AI Gateway is a component of the broader Kong API gateway platform. It leverages Kong's established infrastructure for traffic management, security, and observability and applies it to AI services.
Platform teams already using Kong will find it a natural extension. The AI Gateway offers features like prompt engineering plugins, AI-specific access controls, and analytics. It can manage credentials, enforce rate limits, and provide a unified API for multiple LLM providers. A key benefit is the ability to manage both AI and non-AI services through a single, familiar control plane.
- Best for: Organizations already invested in the Kong ecosystem who want to manage AI services with the same battle-tested infrastructure they use for other APIs.
4. Cloudflare AI Gateway
Cloudflare AI Gateway is a managed service that provides caching, analytics, and rate limiting for AI applications. It sits within Cloudflare's global network, offering low-latency access for distributed teams.
The gateway allows users to connect to various model providers while gaining visibility into requests, costs, and errors through a central dashboard. Its primary features are analytics and caching; it can cache responses to reduce costs and latency for repeated queries. It also provides a persistent logs view for debugging. While it is easy to set up, it offers less control over routing logic and deployment environments compared to self-hosted solutions.
- Best for: Teams that prioritize ease of use, managed infrastructure, and global performance, especially those already using Cloudflare for other services.
5. OpenRouter
OpenRouter is a hosted service that aggregates a massive number of LLMs, including new and fine-tuned models, and makes them available through a single API. It normalizes pricing across models, charging a unified rate per million tokens.
Its primary appeal is the sheer breadth of models available. Developers can experiment with and route requests to hundreds of different models without managing individual provider accounts or keys. It includes a ranking of models by price and capability, helping teams find the best fit for their use case. While it simplifies access, it is a fully managed, third-party service, which may not be suitable for organizations with strict data residency or security requirements.
- Best for: Startups and development teams focused on rapid experimentation and access to the widest possible array of models without the overhead of managing multiple provider relationships.
6. NVIDIA NIM
NVIDIA NIM (NVIDIA Inference Microservices) are packaged, optimized inference servers for deploying AI models anywhere. While not a gateway in the same sense as the others, a collection of NIMs fronted by a load balancer can serve a similar purpose for self-hosted models.
Each NIM is a container that includes a highly optimized inference engine like TensorRT-LLM and a standard API. Platform teams can use them to deploy NVIDIA, community, or custom models on their own infrastructure, from on-premise data centers to any cloud. This approach provides maximum control over the model stack but requires more operational effort to manage routing, failover, and governance. An external gateway like Bifrost is often deployed in front of NIMs to provide these capabilities.
- Best for: Organizations with deep MLOps expertise that need to self-host and serve a fleet of optimized open-source or custom models with maximum performance.
7. Amazon Bedrock
Amazon Bedrock is a fully managed service from AWS that provides access to a range of foundation models from providers like Anthropic, Cohere, Meta, and Amazon itself through a single API.
Bedrock simplifies the process of building and scaling generative AI applications by handling the underlying infrastructure. It integrates with other AWS services for security, monitoring, and governance. Teams can use features like Guardrails for Amazon Bedrock to implement safety policies. It's a powerful option for teams building on AWS, but it primarily supports models available within the Bedrock ecosystem.
- Best for: Teams deeply integrated with the AWS ecosystem who want a managed service for accessing a curated set of high-performing models with built-in security and MLOps tooling.
8. Azure AI Gateway
Microsoft Azure offers AI services that can be composed to function as a gateway. Using Azure API Management, platform teams can create a unified facade for various AI models, including those from Azure OpenAI Service, and other providers.
This approach allows teams to apply Azure's native policies for security, throttling, and caching. It provides a robust, enterprise-grade solution for governance and monitoring through Azure Monitor and Application Insights. It offers significant flexibility but requires expertise in configuring multiple Azure services to build a complete gateway solution.
- Best for: Enterprises committed to the Microsoft Azure stack that need to integrate AI workloads with existing Azure governance, security, and operational policies.
9. Google Vertex AI
Similar to Azure and AWS, Google Cloud's Vertex AI platform offers a suite of tools that can be used to build a gateway for AI models. Vertex AI provides access to Google's Gemini models, as well as models from third parties, through a unified API.
Platform teams can use Vertex AI Endpoints and integrate them with services like Apigee API Management to handle routing, authentication, and rate limiting. The platform excels at MLOps, offering tools for model evaluation, monitoring, and management. This approach provides a powerful, scalable solution for teams building within the Google Cloud ecosystem.
- Best for: Organizations building on Google Cloud who need a comprehensive MLOps platform for managing the entire lifecycle of both proprietary and open-source models.
10. Ollama
Ollama is a tool for running open-source LLMs locally. While it is primarily a local inference server, it exposes an OpenAI-compatible API. By deploying Ollama on a centralized server, a platform team can create a private, self-hosted gateway for a suite of open-source models.
This setup is ideal for development, testing, or production use cases that require data privacy and full control over the model environment. It is lightweight and easy to manage. However, to achieve enterprise-grade reliability and governance, it should be placed behind a more capable AI gateway that can provide features like failover, load balancing, and virtual keys.
- Best for: Teams that need a simple, efficient way to self-host and serve a variety of open-source models with full data privacy and control.
How to Choose the Right AI Gateway
The best AI gateway for a platform engineering team depends on the organization's specific needs. For teams that require top-tier performance, robust governance, and the flexibility to deploy anywhere, an open-source solution like Bifrost is a leading contender. For those deeply embedded in a specific cloud ecosystem, the native offerings from AWS, Azure, or Google provide seamless integration. Simpler, developer-focused tools like LiteLLM are excellent for getting started quickly.
Ultimately, the goal is to select a gateway that abstracts away the complexity of a multi-provider AI world, enabling developers to build applications quickly while the platform team ensures reliability, security, and cost control. Teams can evaluate the options by starting with the open-source versions or exploring managed trials, and a good next step is to request a Bifrost demo or review its open-source repository.
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