The growing number of LLM providers creates significant API management hurdles. AI gateways address this complexity by acting as a central routing point, enabling developers to interact with multiple providers through a single, unified API, thereby simplifying development and maintenance. If you plan to use one of these AI gateways, you can:
- prepare your OpenAI-compatible API request with our tool
- compare the pricing of services with the tool below
Cost comparison of AI gateways
You can enter the token count information and see the cost comparison of the gateways for Llama 4 Scout (17Bx16E) model.
You can read more about LLM pricing.
Prepare your API request with our tool
You can use the tool below to prepare your OpenAI-compatible API request for any of the models provided by AI gateways.
Supported model counts
| AI gateway | Supported model count |
|---|---|
| OpenRouter | 301 |
| AI/ML API | 196 |
| Together AI | 92 |
| Groq | 19 |
| SambaNova | 14 |
Top AI gateways analyzed
OpenRouter
OpenRouter’s unified API simplifies sending requests to large language models (LLMs) by providing a single, OpenAI-compatible endpoint to access over 300 models from providers like Anthropic, Google, and Grok. It intelligently routes requests to optimize cost, latency, or performance, with features like automatic failovers, prompt caching, and standardized request formats, eliminating the need to manage multiple provider APIs. Developers can seamlessly switch between different models without code changes, enhancing flexibility and reliability.
AI/ML API
AI/ML API offers a unified API for sending requests to various LLMs, streamlining integration for tasks like text generation and embeddings. Its standardized interface supports multiple models, allowing developers to send requests without handling provider-specific complexities. The API abstracts infrastructure management, enabling efficient, scalable access to AI models with consistent request formats for rapid development.
Together AI
Together AI’s unified API enables sending requests to over 50 open-source LLMs with a single interface, supporting high-performance inference and sub-100ms latency. It handles token caching, model quantization, and load balancing, allowing developers to send requests without managing infrastructure. The API’s flexibility supports easy model switching and parallel requests, optimized for speed and cost.
Groq
Groq, developed by Groq Inc., is an innovative AI gateway offering a unified API for sending requests to large language models (LLMs) like Llama 3.1. It leverages custom-designed Language Processing Units (LPUs) to deliver high-speed, low-latency responses. With an OpenAI-compatible API, it provides developers with flexibility, though it operates solely over HTTP without WebSocket support.
SambaNova
SambaNova’s unified API, accessible via platforms like Portkey, facilitates sending requests to high-performance LLMs like Llama 3.1 405B, leveraging its custom Reconfigurable Dataflow Units for up to 200 tokens per second. The API standardizes requests for enterprise-grade models, ensuring low-latency, high-throughput processing with seamless integration, ideal for complex AI workloads.
What is the role of an AI gateway in AI application development?
AI Gateways serve as a centralized platform that connects AI models, services, and data to end-user applications. They facilitate seamless integration by providing standardized APIs, often OpenAI-compatible, to interact with multiple AI providers (e.g., OpenAI, Anthropic, or Google). This reduces the need to manage provider-specific APIs, handles tasks like load balancing and caching, and ensures efficient operation, allowing developers to prioritize application logic over infrastructure management.
How does an AI gateway differ from a traditional API gateway?
A traditional API Gateway serves as a single entry point for client requests to backend services, managing and securing API traffic. In contrast, an AI Gateway is tailored for AI models and services, addressing specific challenges like model deployment, large data volumes, and performance monitoring. AI Gateways offer advanced features such as semantic caching, prompt management, and AI-specific traffic management, ensuring compliance with security and regulatory standards, unlike general-purpose API Gateways.
What are the key benefits of using an AI gateway for AI integration?
AI Gateways significantly enhance AI integration by:
- Centralizing and automating AI model deployment and management, reducing complexity.
- Accelerating time-to-market, enabling businesses to adapt quickly to market changes.
- Ensuring reliability and scalability through automated resource management and load balancing.
- Seamlessly integrating with CI/CD pipelines for continuous model updates and improved productivity.
- Providing a secure and scalable platform to minimize downtime and optimize performance.
For example, gateways like OpenRouter and Kong AI Gateway simplify multi-model access while ensuring enterprise-grade reliability.
How does an AI Gateway ensure enhanced security architecture?
AI Gateways provide a robust security architecture through:
- Data encryption, access control, and authentication to protect sensitive data.
- Role-based access control to manage permissions for AI models and services.
- A single point of control for authenticating and authorizing AI traffic.
- Support for virtual keys to securely manage AI models and services.
- Prompt security features to prevent misuse, like prompt injection attacks.
These measures ensure compliance and safeguard AI applications in enterprise settings.
What deployment options are available for AI Gateways?
AI Gateways offer flexible deployment options, including:
- On-premises, cloud, or hybrid environments to suit organizational needs.
- Support for containerization and serverless architectures for scalability.
- Integration with existing security infrastructure for seamless and secure deployment.
- Automated deployment and scaling to ensure high availability and performance.
- A self-service portal for developers to easily deploy and manage AI models.
For instance, Kong AI Gateway supports multi-cloud and on-premises deployments, enhancing flexibility.
More advanced AI Gateways
As depicted in the architecture diagram below, Kong AI Gateway serves as a powerful middleware platform bridging Apps & Agents with AI Providers (e.g., OpenAI, Anthropic, LLaMA) and Vector DBs (e.g., Pinecone, Qdrant). It offers an OpenAI-compatible unified API interface, streamlining access to multiple LLM providers while abstracting complexities. The gateway enhances performance through features like AI Semantic Caching, AI Traffic Control, Load Balancing, and AI Retries, ensuring low latency and optimized operations.
Security is a priority, with AI Prompt Guard to prevent prompt injection attacks, AuthNZ for access control, and data encryption for compliance in enterprise settings. Additionally, Kong AI Gateway provides AI Observability, AI Flow & Transformations, and flexible deployment options across multi-cloud, on-premises, and hybrid environments, making it a scalable and reliable choice for complex AI workloads.
FAQ about AI gateway
What is an AI Gateway?
An AI Gateway is a middleware platform that simplifies the integration, management, and deployment of AI models and services within an organization’s infrastructure. It acts as a bridge between AI systems (such as large language models or LLMs) and end-user applications, providing a centralized environment to streamline access, optimize performance, and ensure scalability. By abstracting the complexities of AI infrastructure, AI Gateways enable developers to focus on building applications rather than managing underlying systems.
What AI Services Can an AI Gateway Unlock for You?
AI Gateways open the door to a wide range of AI services by providing a unified interface to interact with multiple large language models (LLMs) and AI providers. For example, platforms like OpenRouter allow access to over 300 models from providers such as Anthropic and Google, enabling services like text generation, embeddings, and more. Features like prompt caching and standardized APIs simplify the process, letting developers leverage diverse AI capabilities—such as natural language processing or semantic search—without juggling multiple provider-specific integrations.
How Can an AI Gateway Improve Cost Management?
AI Gateways enhance cost management by optimizing resource usage and reducing operational overhead. They intelligently route requests to the most cost-effective models based on performance and pricing, as seen with Together AI’s load balancing and token caching. This minimizes redundant processing and lowers API call expenses. Additionally, gateways like SambaNova streamline infrastructure management, reducing the need for extensive in-house resources, which helps organizations save on maintenance and scaling costs while maintaining high performance.
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