A review of the top open-source AI gateways with permissive Apache 2.0 licenses for enterprise and commercial applications. This comparison covers features, performance, and ideal use cases, with Bifrost selected as the top choice for its comprehensive enterprise capabilities.
Choosing an AI gateway is a critical infrastructure decision for any team building with large language models. The right gateway centralizes access, improves reliability, and provides essential governance controls. For commercial projects, the choice of software license is equally critical. The Apache 2.0 license is favored by many enterprises because it grants the freedom to use, modify, and distribute the software for commercial purposes without imposing copyleft obligations, while also providing an express grant of patent rights from contributors.
This guide examines the best AI gateways available under the Apache 2.0 license, making them suitable for commercial and enterprise use. We will evaluate each based on features, performance, and overall suitability for production workloads.
Key Evaluation Criteria for Commercial AI Gateways
When selecting an AI gateway for commercial use, beyond the license, several technical capabilities are paramount:
- Performance: The gateway should add minimal latency to requests, even under heavy load.
- Reliability: Features like automatic provider failover and load balancing are essential for production stability.
- Provider Support: A wide range of supported LLM providers (both commercial and open-source) is crucial for flexibility.
- Governance and Security: Robust controls for managing access, budgets, rate limits, and data are non-negotiable for enterprises.
- Extensibility: The ability to add custom logic through plugins or middleware allows the gateway to adapt to specific business needs. ## The Top 10 Apache 2.0 AI Gateways
Based on our evaluation criteria, here are the leading AI gateways with permissive Apache 2.0 licenses suitable for building commercial AI applications.
1. Bifrost
Bifrost is a high-performance, open-source AI gateway from Maxim AI, written in Go. It is designed for enterprise-grade performance, reliability, and governance, adding only 11 microseconds of overhead per request at 5,000 requests per second.
Bifrost unifies access to over 20 LLM providers through a single OpenAI-compatible API, enabling teams to use it as a drop-in replacement for existing integrations. Key features include automatic failover, intelligent load balancing, and semantic caching. For governance, it provides virtual keys to manage budgets and rate limits per user or project. It also functions as a full MCP gateway for building agentic applications. Beyond gateway features, Bifrost's governance and security controls can be extended to employee machines with Bifrost Edge, providing endpoint enforcement for all AI traffic.
- Best for: Enterprises and teams building mission-critical AI applications that require best-in-class performance, comprehensive governance, and a unified platform for LLM, MCP, and agent traffic.
2. Apache APISIX
Apache APISIX is a high-performance, cloud-native API gateway. While not an AI gateway by default, its extensible architecture, based on Nginx and etcd, allows it to function as one through plugins. The project maintains a rich ecosystem of over 50 plugins, and developers can write custom plugins to handle AI-specific tasks like authentication with LLM providers, request transformation, and observability for AI traffic. Its Apache 2.0 license and active community make it a solid foundation for building a custom AI gateway solution.
- Best for: Teams with existing API gateway expertise who need a highly performant and extensible platform to build custom AI routing and governance logic.
3. Spring Cloud Gateway
For teams heavily invested in the Java ecosystem, Spring Cloud Gateway offers a flexible, developer-friendly solution. Built on top of the Spring Framework 5, Project Reactor, and Spring Boot 2, it provides a robust way to route API requests. It can be configured as an AI gateway by defining routes to various LLM providers and applying per-route filters for authentication, rate limiting, and request modification. Its deep integration with the Spring ecosystem, including service discovery and circuit breakers, is a significant advantage for Java-based applications.
- Best for: Java development teams looking to integrate AI capabilities into their existing microservices architecture using familiar Spring patterns.
4. Tyk (Open Source)
Tyk offers an open-source API gateway written in Go, available under the Mozilla Public License 2.0, which has similar characteristics to Apache 2.0 regarding commercial use but with some differences in patent grant and other clauses. It is often considered in the same category of business-friendly open source licenses. Tyk can be configured to manage access to LLM APIs with features like authentication, quotas, and rate limiting. It also has a powerful middleware capability that allows developers to inject custom logic, making it possible to build AI-specific features. The open-source version is a capable starting point for teams needing solid API management fundamentals for their AI services.
- Best for: Go developers and organizations looking for a feature-rich, open-source API gateway that can be extended to manage AI services.
5. Gloo Gateway
Gloo Gateway, from Solo.io, is an Envoy-based API gateway. The open-source version is licensed under Apache 2.0. Gloo excels at managing traffic for microservices and can be configured to route requests to various LLM providers. Its strength lies in its use of Envoy Proxy, known for its high performance and rich feature set. Teams can use Gloo to implement sophisticated routing rules, apply security policies, and gain observability into their AI API traffic.
- Best for: Organizations using Kubernetes and Envoy Proxy that want to extend their existing cloud-native infrastructure to manage AI API traffic.
6. KrakenD
KrakenD is another high-performance, open-source API gateway with an Apache 2.0 license. Its main design principle is to be a "Backend for Frontend" (BFF), aggregating multiple microservices into a single endpoint. This same principle can be applied to LLM providers. Teams can use KrakenD to create endpoints that abstract away the complexity of calling multiple models or providers, applying transformations and filtering on the gateway level. Its stateless architecture and focus on performance make it a strong contender.
- Best for: Teams needing a stateless, high-performance gateway to aggregate and orchestrate calls to multiple backend AI services or LLM providers.
7. OCELOT
For developers in the .NET ecosystem, Ocelot is a popular API gateway licensed under Apache 2.0. It is designed to work with .NET Core and provides a simple way to implement API gateway features within a .NET application. It handles routing, request aggregation, and authentication, which can all be applied to managing access to LLM APIs. Its simplicity and integration with the .NET environment make it a natural choice for C# developers.
- Best for: .NET developers who need to add API gateway capabilities for AI services directly into their ASP.NET Core applications.
8. Kong Gateway (Open Source)
Kong Gateway is one of the most popular open-source API gateways. Built on top of Nginx, it is known for its performance and extensibility through a robust plugin architecture. The open-source version, licensed under Apache 2.0, can be configured with community or custom plugins to act as an AI gateway. These plugins can handle tasks like provider authentication, semantic caching, and routing. Kong's large community and extensive documentation are significant assets for teams building on its platform.
- Best for: DevOps teams and organizations that need a mature, plugin-driven API gateway that can be adapted for both traditional and AI-driven services.
9. Gravitee.io (Open Source)
Gravitee.io provides an open-source API management platform licensed under Apache 2.0. It offers a comprehensive set of features, including an API gateway, access management, and an API designer. Teams can use Gravitee to define policies for accessing LLM APIs, including security, rate limiting, and transformations. Its user interface for designing and managing APIs can help accelerate the process of integrating and governing AI services.
- Best for: Organizations looking for a full-lifecycle API management platform with a graphical interface to govern access to their portfolio of AI and traditional APIs.
10. Choreo Connect
Choreo Connect is a cloud-native, open-source API gateway for microservices, licensed under Apache 2.0. As part of the WSO2 ecosystem, it is designed for decentralized, developer-centric API management. It can be deployed on Kubernetes or Docker, providing a scalable and resilient foundation for routing AI traffic. Teams can define and enforce policies on API requests to LLM providers, integrating with CI/CD pipelines for automated deployment.
- Best for: Enterprises that require a decentralized API gateway that aligns with modern DevOps and GitOps practices for managing AI services. ## Choosing the Right Gateway for Your Project
While all the gateways listed provide the legal clarity of the Apache 2.0 license for commercial use, the best choice depends on your specific needs. General-purpose API gateways like Apache APISIX or Kong offer immense flexibility but require more custom development to implement AI-specific features.
For teams that need a solution built specifically for AI workloads, a dedicated AI gateway like Bifrost provides critical features like provider failover, semantic caching, and unified governance out of the box. This focus can significantly reduce development time and improve the reliability of production AI applications. Teams evaluating these options can request a Bifrost demo or review the open-source repository to compare its capabilities directly.
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