9 Best Kong AI Gateway Alternatives

This article compares the top Kong AI Gateway alternatives for engineering teams routing production LLM traffic. The comparison covers open-source and managed options, with Bifrost emerging as the leading choice for enterprises needing high performance, advanced governance, and deployment flexibility.

As more companies integrate large language models (LLMs) into their applications, managing the flow of API requests has become a critical infrastructure challenge. An AI gateway acts as a central control plane for this traffic, providing essential services like routing, caching, and observability. Kong AI Gateway is a well-known option, but many teams find themselves looking for alternatives that offer better performance, open-source flexibility, or more specialized features.

This guide provides a detailed comparison of the nine best alternatives to the Kong AI Gateway. It evaluates each tool based on criteria crucial for production AI workloads, including performance, provider support, governance features, and ease of deployment. The goal is to help engineering leaders select the right infrastructure to build reliable and scalable AI products.

Key Criteria for Evaluating AI Gateways

When comparing AI gateway solutions, several technical capabilities are critical. These criteria form the basis for the rankings in this article:

• Performance and Latency: How much overhead does the gateway add to each request? High-throughput, low-latency performance is essential for user-facing applications.
• Provider and Model Support: How many LLM providers does the gateway support out of the box? A broad range of integrations prevents vendor lock-in.
• Reliability Features: Does the gateway offer automatic provider failover and intelligent load balancing to handle outages and traffic spikes?
• Governance and Security: What controls are available for managing access, setting budgets, and enforcing security policies? This includes features like virtual keys, rate limiting, and audit logs.
• Observability: What tools are provided for monitoring requests, tracking costs, and debugging issues? Native support for standards like Prometheus and OpenTelemetry is a key advantage.
• Deployment Flexibility: Can the gateway be deployed on-premise, in a virtual private cloud (VPC), or as a managed service? This is especially important for organizations in regulated industries.

The Top 9 Kong AI Gateway Alternatives

Based on the criteria above, here is a ranked list of the best alternatives to Kong's AI Gateway for 2026.

1. Bifrost

Bifrost is a high-performance, open-source AI gateway from Maxim AI, written in Go. It is designed for enterprise teams that require minimal latency, broad provider support, and robust governance controls for mission-critical AI applications.

Its standout feature is performance. Published benchmarks show that Bifrost adds only 11 microseconds of overhead per request at a sustained load of 5,000 requests per second. This makes it a suitable choice for real-time applications where every millisecond counts.

Key Features:

• Unified API: Provides a single, OpenAI-compatible API for over 1,000 models from providers like OpenAI, Anthropic, AWS Bedrock, Google Vertex AI, and Groq.
• Reliability: Includes automatic failover and intelligent load balancing to ensure zero-downtime operations during provider incidents.
• Advanced Governance: Uses virtual keys to manage access, budgets, and rate limits on a per-user or per-project basis.
• Semantic Caching: Reduces costs and improves latency by caching responses to semantically similar queries.
• MCP Gateway: Functions as a native Model Context Protocol (MCP) gateway, enabling models to connect with and orchestrate external tools securely.

Beyond the gateway, Bifrost's governance and security controls can be extended to developer machines. Bifrost Edge is an endpoint agent that routes AI traffic from desktop apps and CLIs through the central gateway, ensuring that all company AI usage is covered by the same policies and audit logs.

Best for: Enterprise teams building mission-critical AI applications that demand the highest performance, comprehensive governance, and flexible on-premise or VPC deployment options.

2. LiteLLM

LiteLLM is a popular open-source library that provides a unified interface for calling over 100 LLM APIs. It is valued for its simplicity and extensive provider support, making it a common starting point for developers building multi-provider applications. While often used as a library, it can also be deployed as a standalone proxy server.

Key Features:

• Broad Provider Support: Offers a consistent input/output format across a wide range of LLM providers.
• Simple Setup: Can be integrated into existing Python applications with minimal code changes.
• Callback Functions: Allows for custom logic to be executed on request and response data, enabling logging and data streaming to various platforms.
• Key and Budget Management: Provides basic tools for managing API keys and tracking costs.

Compared to more robust gateways like Bifrost, LiteLLM's proxy lacks advanced enterprise features like clustering, adaptive load balancing, and role-based access control. Teams can review a more detailed feature breakdown on the Bifrost LiteLLM alternatives page.

Best for: Startups and development teams looking for a simple, open-source way to abstract away differences between LLM providers during early-stage development.

3. Cloudflare AI Gateway

Cloudflare AI Gateway is a managed service that provides caching, rate limiting, and analytics for AI applications. As part of the broader Cloudflare ecosystem, it benefits from the company's global network, offering low-latency connections for users worldwide.

Key Features:

• Global Distribution: Leverages Cloudflare's edge network to reduce latency.
• Caching: Caches responses to identical requests at the edge, reducing calls to origin model providers.
• Analytics and Logging: Provides a dashboard for viewing request metrics, errors, and costs.
• Rate Limiting: Protects applications from denial-of-service attacks and traffic spikes.

Cloudflare's offering is a managed service, which means it cannot be self-hosted. This may be a limitation for organizations that require data to remain within their own infrastructure for compliance reasons, such as those in healthcare and life sciences.

Best for: Teams already using the Cloudflare ecosystem who need a simple, managed solution for caching and monitoring AI traffic for globally distributed applications.

4. OpenRouter

OpenRouter is a managed service that aggregates access to a wide variety of paid and open-source LLMs through a single API. It simplifies model access by allowing developers to use a single API key and billing account for dozens of models, including experimental and fine-tuned variants.

Key Features:

• Model Aggregation: Offers access to a diverse and constantly updated list of models from various providers.
• Pay-per-use Billing: Simplifies cost management with a unified credit system.
• Model Rankings: Provides leaderboards and performance data to help developers choose the best model for their task.
• Fallback Logic: Allows users to specify fallback models in case the primary choice is unavailable.

OpenRouter's focus is on providing access to the widest possible range of models, rather than on enterprise governance or deployment.

Best for: Developers and researchers who need easy access to a broad selection of different language models for experimentation and prototyping.

5. Apigee (Google Cloud)

Google Cloud's Apigee API Management can function as an AI gateway, although it is a general-purpose API management platform. Teams using Google Cloud can use Apigee to apply security policies, manage traffic, and gain visibility into their LLM API usage alongside their other microservices.

Key Features:

• Deep Google Cloud Integration: Connects seamlessly with other Google Cloud services, including Vertex AI.
• Advanced Security: Offers robust security features, including threat protection and identity management.
• API Analytics: Provides powerful tools for analyzing API traffic and performance.
• Monetization: Enables developers to create and manage API products with different pricing tiers.

Because it is a general-purpose tool, configuring Apigee specifically for AI workloads may require more effort than using a dedicated AI gateway. It lacks specialized features like semantic caching and native MCP support.

Best for: Large enterprises heavily invested in the Google Cloud ecosystem that want to manage LLM APIs within the same platform they use for all other microservices.

6. Amazon API Gateway

Similar to Apigee, Amazon API Gateway is a general-purpose API management service that can be configured to route and manage traffic to LLMs, particularly those hosted on AWS Bedrock. It is a fully managed service that handles traffic management, access control, and API versioning.

Key Features:

• AWS Ecosystem Integration: Works closely with AWS Lambda, Bedrock, and other AWS services.
• Scalability: Automatically scales to handle the amount of traffic an application receives.
• Security Controls: Integrates with AWS Identity and Access Management (IAM) for authentication and authorization.
• Flexible Pricing: Offers a pay-as-you-go pricing model based on API calls and data transfer.

Like Apigee, it is not a purpose-built AI gateway and lacks features such as automatic provider failover to non-AWS models and semantic caching.

Best for: Organizations that run their entire infrastructure on AWS and need to manage LLM API calls within their existing AWS-native tooling.

7. Azure API Management

For teams operating within the Microsoft ecosystem, Azure API Management provides a way to manage traffic to Azure OpenAI Service and other APIs. It acts as a facade for backend services, offering policies for security, transformation, and caching.

Key Features:

• Azure Integration: Native integration with Azure Functions, Logic Apps, and Azure OpenAI.
• Developer Portal: Includes a customizable portal for API documentation and user onboarding.
• Hybrid and Multi-cloud: Can manage APIs hosted on-premise or in other clouds.
• Security Policies: Enforces policies like IP filtering, JWT validation, and client certificate authentication.

This platform is a strong choice for managing Azure-based AI services but requires custom configuration to handle multi-provider routing and lacks specialized AI features.

Best for: Enterprises standardized on Microsoft Azure who want to govern their Azure OpenAI usage with the same tools they use for other enterprise APIs.

8. NGINX

NGINX is a high-performance open-source web server, reverse proxy, and load balancer. While not an AI gateway by default, it can be configured to perform many of the same functions, such as load balancing requests across multiple API endpoints and implementing rate limiting.

Key Features:

• High Performance: Known for its speed and ability to handle a massive number of concurrent connections.
• Flexibility: Can be customized extensively through its configuration files and third-party modules.
• Wide Adoption: A well-understood and battle-tested tool used by millions of websites.
• Community Support: Has a large and active community providing support and documentation.

Using NGINX as an AI gateway requires significant manual configuration and scripting to implement features like provider-aware failover, request logging with token counts, or semantic caching.

Best for: Platform engineering teams with deep NGINX expertise who prefer to build a custom solution using a highly flexible and performant foundation.

9. Tyk

Tyk is an open-source API gateway that is popular for its performance and flexibility. Written in Go, it can be deployed on-premise, in the cloud, or as a managed service. Like NGINX, it can be adapted to serve as an AI gateway with custom middleware.

Key Features:

• Open Source: The core gateway is open source and available on GitHub.
• Extensibility: Supports custom middleware written in several languages, including JavaScript, Python, and Go.
• GraphQL Support: Provides a robust GraphQL engine out of the box.
• Dashboard and Analytics: Includes a UI for managing APIs and viewing traffic analytics.

Tyk offers a powerful foundation, but building AI-specific features like dynamic provider routing and semantic caching would require custom development.

Best for: Teams looking for an open-source, Go-based API gateway that can be extended with custom middleware to handle AI-specific routing and logging logic.

How to Choose the Right Alternative

The best alternative to Kong AI Gateway depends on an organization's specific needs. For enterprises that require high performance, extensive governance, and the ability to deploy in a self-hosted environment, a purpose-built solution like Bifrost is the superior choice. For smaller teams or those with simpler requirements, tools like LiteLLM or OpenRouter can provide a quick way to get started. Teams deeply embedded in a specific cloud ecosystem may prefer to use the native API management services from AWS, Google Cloud, or Azure.

As AI applications become more complex and mission-critical, the need for specialized infrastructure will only grow. Teams evaluating these options can get a more detailed breakdown from the LLM Gateway Buyer's Guide. For those ready to test a high-performance gateway, teams can request a Bifrost demo or explore the open-source repository.