A review of the top open-source AI gateways for developers and platform teams in 2026, comparing options like Bifrost, LiteLLM, and Kong for performance, features, and enterprise readiness. Bifrost stands out for performance-critical, self-hosted deployments.
As development shifts from single-model applications to multi-provider, agentic workflows, an AI gateway has become essential infrastructure. An AI gateway is a specialized proxy that sits between your applications and the various LLM providers (like OpenAI, Anthropic, and Google), unifying them behind a single, consistent API. This layer handles critical operational tasks like routing, load balancing, automatic failover, caching, and security, allowing engineering teams to focus on building features instead of managing infrastructure.
Opting for an open-source AI gateway provides several advantages over proprietary managed services. The primary benefits are data sovereignty and cost control. With an open-source solution, all prompt and response data remains within your own infrastructure, a critical requirement for organizations handling sensitive information or operating in regulated industries. Furthermore, you avoid the per-request pricing models of managed gateways, paying only for the compute resources you consume.
This article compares the 10 best open-source AI gateways, evaluating them on performance, feature depth, provider support, and overall maturity.
1. Bifrost
Bifrost is a high-performance, open-source AI gateway written in Go, designed for production-scale, low-latency workloads. Its major differentiator is performance; benchmarks show it adds only 11 microseconds of overhead per request at a sustained load of 5,000 requests per second.
Bifrost unifies access to over 20 providers through a single OpenAI-compatible API, enabling teams to use it as a drop-in replacement for existing SDKs. Key features include automatic provider failover, weighted load balancing, and a dual-layer semantic caching system that supports both exact-match and vector-based similarity searches to reduce costs. It also functions as an MCP (Model Context Protocol) gateway, allowing AI agents to discover and execute external tools securely.
Best for: Enterprise teams that require best-in-class performance, deep governance features, and self-hosted control for mission-critical AI workloads.
2. LiteLLM
LiteLLM is the most widely adopted open-source proxy and SDK for managing LLM API calls. Written in Python, its main strength is its vast ecosystem, offering a unified OpenAI-compatible interface for over 100 LLM providers. This broad support makes it an excellent choice for teams that need to experiment with a wide variety of models without writing provider-specific integrations.
The LiteLLM gateway (proxy server) can be deployed as a centralized service, providing features like virtual API keys for spend tracking, request fallbacks, and budget management. It also includes an admin UI for managing keys and viewing usage logs. While its Python architecture may introduce more latency than Go-based alternatives, its ease of use and extensive provider list make it a default choice for many development teams.
Best for: Development teams and platform engineers who need to support the widest possible range of LLM providers with a simple, self-hostable proxy.
3. Kong AI Gateway
Kong AI Gateway extends Kong's popular open-source API gateway with a suite of plugins specifically for AI and LLM traffic. For organizations already using Kong to manage their microservices and APIs, this provides a natural path to governing AI workloads within their existing infrastructure.
The AI capabilities are delivered through plugins that offer features like multi-provider connectivity, prompt engineering, request transformations, LLM cost control, and observability. It supports routing to major providers like OpenAI, Anthropic, and Azure AI and includes advanced features for security and governance, such as PII sanitization and prompt injection detection. While the core gateway is open-source, some advanced AI features may require an enterprise license.
Best for: Large organizations already invested in the Kong ecosystem that want to apply consistent governance and security policies across both traditional APIs and new AI services.
4. Apache APISIX
Apache APISIX is a high-performance, cloud-native API gateway from the Apache Software Foundation. Like Kong, it uses a plugin-based architecture, and recent additions have introduced AI-specific capabilities. APISIX is built on NGINX and etcd, offering dynamic routing and hot-reloading of plugins without restarts, which is ideal for microservice environments.
Its AI features, such as the ai-proxy and ai-proxy-multi plugins, allow APISIX to route requests to various LLM providers, perform load balancing, and manage access. Because it's a general-purpose API gateway, teams can leverage its rich ecosystem of existing plugins for authentication, observability, and security. However, this also means that achieving a full-featured AI gateway requires configuring and managing multiple plugins.
Best for: Teams looking for a mature, high-performance, and extensible open-source API gateway that they can adapt for AI workloads alongside existing API traffic.
5. Envoy AI Gateway
Envoy AI Gateway is an open-source project that brings AI traffic management to Envoy Proxy, the CNCF-graduated project that underpins service meshes like Istio. This makes it a compelling option for platform teams that have standardized on Kubernetes and Envoy for their cloud-native infrastructure.
The gateway provides a unified API for routing to different model providers, along with features for rate limiting, observability, and automatic failover. It leverages Kubernetes-native configuration (CRDs), allowing platform engineers to manage AI routing rules with the same declarative tools they use for other services. As a newer project in the space, its list of natively supported providers is smaller than some alternatives, but its alignment with the CNCF ecosystem is a significant advantage for Kubernetes-native teams.
Best for: Platform engineering teams deeply integrated with the Kubernetes and Envoy ecosystem who want to manage AI traffic using familiar, cloud-native patterns.
6. Portkey
Portkey offers a fast and lightweight open-source AI gateway designed for reliability and ease of use. It provides a single API to route requests across more than 1,600 models and includes production-ready features like automatic retries with fallbacks, weighted load balancing across multiple API keys or providers, and request timeouts.
A key feature of Portkey is its focus on cost management and observability. It offers intelligent caching (both simple and semantic) to reduce redundant API calls and provides detailed analytics on usage, latency, and costs. The core gateway is open-source, and its compatibility with the OpenAI SDK makes it easy to integrate into existing applications. The project is actively developed and is merging its core enterprise features into the open-source version.
Best for: Teams looking for a simple-to-deploy open-source gateway with strong reliability features and built-in cost-control mechanisms like semantic caching.
7. Helicone
Helicone started as an open-source observability platform for LLMs and has evolved to include a capable AI gateway. Its main value proposition is combining routing and observability in a single, easy-to-integrate package. By changing one line of code (the base URL), developers can route their requests through Helicone and get immediate insights into cost, latency, and usage.
The gateway, written in Rust, provides an OpenAI-compatible API that can route to over 100 models, with features like intelligent routing and automatic fallbacks. Although the project was acquired by Mintlify and is now in maintenance mode, its strong focus on developer experience and integrated observability makes it a useful tool, especially for teams prioritizing monitoring and debugging.
Best for: Developers and teams who prioritize tightly integrated observability and want a simple, one-line integration for logging and routing their LLM requests.
8. Tyk API Gateway
Tyk is another established open-source API gateway, written in Go, that has extended its platform to include AI governance. The core Tyk gateway is known for its performance and flexibility, with a "batteries-included" approach that provides most features without a feature lockout in its open-source version.
With the introduction of Tyk AI Studio, the platform offers a centralized control plane for all AI traffic. This allows organizations to apply the same security policies, rate limits, and audit trails to their LLM, MCP, and agent-based traffic as they do to their traditional APIs. The AI governance layer is also open source, making Tyk a strong candidate for teams who need a self-hostable platform with a transparent, extensible architecture.
Best for: Organizations needing a mature, Go-based open-source API gateway that provides a unified control plane for both conventional API traffic and modern AI workloads.
9. Gravitee
Gravitee's open-source platform has expanded from API management to also cover event streams and AI agent governance. Its core, the Apache 2.0 licensed API Gateway, is protocol-agnostic and can manage REST, GraphQL, and event-based traffic. The platform now includes an LLM proxy and MCP proxy to govern AI agent traffic using the same rate limits and policies.
Gravitee emphasizes the concept of a unified platform for all types of service traffic, AI included. This is particularly relevant for enterprises looking to avoid building separate, siloed infrastructure for their AI applications. While the Community Edition provides the core gateway, advanced AI and agent management features are part of their tiered commercial plans.
Best for: Enterprises seeking a unified platform to manage APIs, event streams, and AI traffic from a single control plane with a strong open-source foundation.
10. Spring Cloud Gateway
For teams heavily invested in the Java and Spring ecosystem, Spring Cloud Gateway is a natural choice. It is an open-source project built on Spring Framework 5, Project Reactor, and Spring Boot 2, designed to provide a simple and effective way to route to APIs and address cross-cutting concerns.
While not an "AI gateway" by default, its highly customizable and extensible nature allows developers to build AI-specific logic into it. Developers can implement custom filters for tasks like authentication, rate limiting, and routing to different LLM providers. Its tight integration with other Spring Cloud components, like Circuit Breaker for resilience, makes it a powerful building block for Java-based teams creating their own AI gateway solution.
Best for: Java development teams already using the Spring ecosystem who prefer to build a custom AI gateway solution using familiar tools and patterns.
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