What is an AI Gateway? Popular AI Gateways in 2026
Artificial intelligence is transforming how businesses build applications, automate workflows, and deliver digital services. From AI-powered chatbots and coding assistants to autonomous agents and enterprise knowledge systems, modern applications increasingly depend on large language models (LLMs).
However, integrating multiple AI models into a production application introduces several challenges. Developers need to manage API keys, monitor usage, control costs, handle provider failures, and maintain consistent security policies.
This is where an AI gateway becomes useful.
An AI gateway provides a centralized layer between an application and AI models or providers. It can simplify model access, manage requests, monitor performance, apply security controls, and help developers build more reliable AI applications.
Popular AI gateway platforms include Portkey, LiteLLM, OpenRouter, Cloudflare AI Gateway, Vercel AI Gateway, Kong AI Gateway, and LangSmith LLM Gateway.
Today, I will explain what an AI gateway is, how it works, the difference between an AI gateway and an AI router, and how to choose the right platform for your project.
Table of Contents
What is an AI Gateway?
An AI gateway is a software layer that sits between an application and one or more AI models or providers, helping developers manage, route, monitor, and secure AI requests through a centralized interface.
Instead of connecting every application component directly to individual AI providers, developers can send requests through a gateway that handles supported operational tasks.
Depending on the platform, an AI gateway may provide:
Unified access to multiple AI model providers
Model routing and load balancing
API authentication and access controls
Request retries and fallback models
Rate limiting and usage quotas
Token and cost monitoring
Request logging and observability
Caching to reduce repeated model calls
Security policies and sensitive-data protection
For example, imagine a company operates an AI-powered customer support assistant. The application uses one model for routine questions and another for more complex requests.
An AI gateway can centralize the model integrations, enforce usage policies, monitor performance, and support configured fallback behavior when a provider encounters an error.
The exact capabilities depend on the gateway and its configuration. An AI gateway does not automatically provide every feature listed above.
Why Are AI Gateways Important?
As AI applications grow, managing individual model integrations becomes more complicated.
Without a gateway, developers may need to implement provider-specific authentication, retries, monitoring, usage tracking, and fallback logic within their applications.
An AI gateway can centralize many of these responsibilities.
1. Simplified AI model integration
Different AI providers expose different APIs, authentication methods, and model capabilities. A gateway can provide a common interface for supported models, reducing the amount of provider-specific integration code.
2. Better cost management
AI models can have different input-token and output-token prices. A gateway can help monitor spending, enforce configured limits, and support routing strategies that balance cost and quality.
3. Improved reliability
AI providers can experience rate limits, outages, timeouts, or temporary failures. Gateways that support retries and fallbacks can attempt alternative requests when configured conditions are met.
4. Centralized security
A gateway can help teams manage credentials, control access, and apply supported policies to AI traffic. This is especially useful when multiple applications or teams share AI infrastructure.
5. Better observability
Developers need to understand how their AI applications behave in production. Gateway monitoring can help track latency, errors, token usage, and costs, making it easier to investigate problems.
6. Greater flexibility
A gateway can make it easier to change model providers or experiment with alternative models without rewriting every application integration.
These benefits make AI gateways particularly valuable for production systems that use multiple models or require centralized operational controls.
How Does an AI Gateway Work?
An AI gateway typically operates between an application and the AI model providers it uses.
The workflow generally follows these steps.
Step 1: An application sends a request
A chatbot, web application, backend service, or AI agent sends a request to the gateway.
For example, a user might ask an AI assistant to summarize a report or generate a software function.
Step 2: The gateway validates the request
Depending on the configuration, the gateway may verify credentials, check access permissions, enforce rate limits, or apply security policies.
Step 3: The gateway selects or routes the request
If routing is configured, the gateway may select a model or provider based on developer-defined rules, task requirements, model availability, or other supported criteria.
Not every gateway automatically classifies tasks or selects the optimal model. Some use explicit model selection or predefined routing policies.
Step 4: The gateway forwards the request
The gateway sends the request to the selected AI provider, using the appropriate credentials and API integration.
Step 5: The model generates a response
The selected model processes the request and returns its output.
Step 6: The gateway processes and returns the response
Depending on the implementation, the gateway may record request metrics, apply supported response policies, or handle errors before returning the result to the application.
This centralized approach can simplify how AI applications interact with multiple providers.
What is an AI Router?
An AI router is a component that determines where an AI request should be sent. It can select an AI model or provider based on configured rules, cost, latency, task requirements, availability, or other criteria.
For example:
Simple summarization requests may go to a cost-effective model.
Complex reasoning tasks may go to a model selected for advanced reasoning.
Coding requests may go to a model with strong programming capabilities.
Failed requests may be retried or sent to an alternative provider when supported fallback rules are configured.
AI routing can help developers balance model quality, performance, reliability, and cost.
Some AI routing systems use fixed rules, while others support more dynamic selection strategies.
AI Gateway vs. AI Router: What is the Difference?
AI gateways and AI routers are related, but they do not always serve exactly the same purpose.
An AI router primarily focuses on selecting the destination for an AI request. An AI gateway typically provides a broader infrastructure layer for managing requests between applications and AI providers.
Feature | AI Gateway | AI Router |
Primary purpose | Centralized AI request management | Model or provider selection |
Model routing | Often supported | Core function |
Authentication and access control | Common capability | Not necessarily included |
Request monitoring | Common capability | Depends on implementation |
Cost management | May include tracking and limits | May consider cost during routing |
Caching | Often available | Not necessarily included |
Retries and fallbacks | Common capability | May be supported |
Security policies | May be supported | Not necessarily included |
These categories overlap. Many AI gateways include routing capabilities, and some products described as routers also provide broader gateway functionality.
For example, Portkey and LiteLLM offer gateway and routing capabilities, while OpenRouter combines multi-model access with routing functionality.
In simple terms: An AI router helps decide where a request should go, while an AI gateway can manage the wider process of sending, monitoring, and controlling that request.
Popular AI Gateways in 2026
Several AI gateway platforms offer different approaches to model access, routing, observability, and governance.
The following seven platforms are worth evaluating based on your application's needs.
Portkey AI Gateway
Best for: Production AI applications that need centralized request management, reliability, and observability.
Portkey is an AI infrastructure platform that helps developers connect applications to AI providers through a centralized gateway.
It offers capabilities for managing AI requests, monitoring application behavior, and configuring routing and reliability policies.
Key features include:
Unified access to supported AI providers
Configurable routing and fallback strategies
Retries for supported failure conditions
Request tracing and observability
Usage and cost monitoring
Caching capabilities
Access controls and supported guardrails
Portkey can be useful for businesses operating AI-powered applications where request visibility, reliability, and centralized management are important.
Example use case: An e-commerce company runs an AI customer support assistant. The engineering team wants to monitor latency, investigate failures, and configure alternative models when a provider becomes unavailable.
Portkey can provide a centralized layer for these operations, subject to the features enabled in the chosen deployment.
Website: https://portkey.ai/ Documentation: https://portkey.ai/docs/product/ai-gateway
LiteLLM
Best for: Developers and engineering teams looking for an open-source gateway with configurable routing and self-hosting options.
LiteLLM provides a unified interface for connecting applications to multiple LLM providers. It offers both an SDK and a gateway for managing model requests.
Key features include:
OpenAI-compatible API interface
Support for multiple AI providers
Load balancing across model deployments
Configurable retries and fallbacks
Usage tracking and budget controls
Rate limiting and gateway management
Self-hosting options
LiteLLM is particularly useful for teams that want greater control over their AI infrastructure and deployment environment.
Example use case: A company operates several internal AI applications and wants to distribute requests across model deployments, monitor team usage, and configure fallbacks when an endpoint fails.
Website: https://www.litellm.ai/ Documentation: https://docs.litellm.ai/docs/proxy/docker_quick_start
OpenRouter
Best for: Developers who want convenient access to a wide range of AI models through a unified API.
OpenRouter is an AI model access and routing platform that allows developers to work with models from multiple providers through a common interface.
Its emphasis on model discovery and access makes it useful for experimenting with different models without implementing every provider integration separately.
Key features include:
Unified API access to multiple AI models
Model discovery and pricing information
Provider selection options
Routing capabilities for supported workflows
Supported fallback and provider-selection features
OpenRouter is particularly useful for developers who want to compare models, prototype applications, or access different model families through one integration.
Example use case: A developer building an AI writing assistant wants to test multiple models for content generation, summarization, and reasoning through a common API.
Website: https://openrouter.ai/ Documentation: https://openrouter.ai/docs/quickstart/
Cloudflare AI Gateway
Best for: Developers who want AI request analytics and gateway functionality within the Cloudflare ecosystem.
Cloudflare AI Gateway provides a layer for managing and observing requests to supported AI providers and services.
It is relevant to applications that already use Cloudflare infrastructure and want visibility into AI traffic.
Key features include:
AI request analytics
Usage and cost visibility
Logging options
Caching capabilities
Supported request reliability features
Integration with the Cloudflare ecosystem
The availability of particular features depends on the gateway configuration and supported provider integrations.
Example use case: A developer wants to monitor AI API usage and analyze request performance in an application already deployed with Cloudflare.
Documentation: https://developers.cloudflare.com/ai-gateway/
Vercel AI Gateway
Best for: Web developers building AI applications with Vercel and modern JavaScript frameworks.
Vercel AI Gateway provides a unified way to access supported models from multiple providers.
It is designed to simplify model integration and help developers build AI-powered applications without managing a separate integration for every provider.
Key features include:
Access to models from multiple providers
A common interface for model requests
Supported provider-selection and fallback capabilities
Usage and cost information
Integration with the Vercel development ecosystem
Example use case: A developer building a Next.js application wants to experiment with different AI models while keeping the application integration consistent.
Website: https://vercel.com/ai-gateway Documentation: https://vercel.com/docs/ai-gateway
Kong AI Gateway
Best for: Enterprises that need AI traffic management alongside broader API management.
Kong provides API gateway infrastructure that can be used to manage AI-related traffic and integrations.
It is worth evaluating for organizations that already use Kong or require centralized API policies across traditional services and AI applications.
Key features include:
AI traffic and API management
Authentication and access policies
Rate limiting and traffic controls
Integration with supported AI services
Enterprise-oriented API governance
The exact capabilities depend on the Kong product, plugins, and deployment configuration.
Example use case: An enterprise wants to apply consistent authentication, traffic management, and access policies to its existing APIs and AI-powered services.
Website: https://konghq.com/products/kong-ai-gateway Documentation: https://developer.konghq.com/ai-gateway/
LangSmith LLM Gateway by LangChain
Best for: Developers building AI agents and LLM applications with LangChain and LangGraph.
LangSmith LLM Gateway is part of the LangChain ecosystem. It provides a centralized layer for managing and governing requests to supported language models and providers.
While LangChain and LangGraph help developers build AI applications and agent workflows, LangSmith provides tooling for development, evaluation, and observability. The LLM Gateway adds capabilities for managing model requests at runtime.
Key features include:
Model access and routing: Manage requests to supported models and providers through a centralized gateway.
Spend controls: Configure spending and usage limits to help manage AI infrastructure costs.
Fallbacks and reliability: Configure alternative models or providers for supported failure scenarios.
Security and governance: Apply supported policies for model access and sensitive-data protection.
Observability: Connect gateway activity with LangSmith tracing and monitoring workflows.
LangChain ecosystem integration: Useful for teams building applications with LangChain and LangGraph.
Example use case: A company develops a customer support agent using LangGraph. It wants to monitor model requests, control AI spending, and configure fallback providers when a model becomes unavailable.
LangSmith LLM Gateway can provide a centralized layer for these controls.
Documentation: https://docs.langchain.com/langsmith/llm-gateway
AI Gateway Comparison: Which Platform Should You Choose?
The right AI gateway depends on your use case, infrastructure, and operational requirements.
AI Gateway | Best Suited For | Main Strength |
Portkey | Production AI applications | Gateway management and observability |
LiteLLM | Engineering and platform teams | Open-source routing and self-hosting |
OpenRouter | Developers exploring different models | Unified model access and discovery |
Cloudflare AI Gateway | Cloudflare users | Analytics, caching, and request management |
Vercel AI Gateway | Web application developers | Convenient access to multiple models |
Kong AI Gateway | Enterprise API management | Traffic controls and API governance |
LangSmith LLM Gateway | LangChain and LangGraph developers | Spend controls, governance, and LangSmith integration |
This is a use-case-based comparison rather than a universal ranking. Features, supported providers, and commercial terms can change, so check official documentation before choosing a platform.
How to Choose the Best AI Gateway for Your Project
Before selecting a platform, evaluate the following factors.
1. Model and provider support
Confirm that the gateway supports the models and providers you plan to use. Do not assume every provider supports every model feature through every gateway.
2. Routing capabilities
Determine whether you need simple model selection, rule-based routing, load balancing, or more dynamic routing strategies.
3. Cost transparency
Review model inference charges, gateway fees, usage tracking, and available spending limits. A gateway may simplify cost management without necessarily reducing every request's cost.
4. Reliability and fallback behavior
Check how the platform handles timeouts, rate limits, provider outages, and unsuccessful requests. Confirm which behaviors are automatic and which require configuration.
5. Security and privacy
Understand how prompts and responses are processed, whether request data is logged, and what retention policies apply.
For sensitive workloads, review access controls, regional requirements, contractual terms, and downstream provider policies.
6. Deployment options
Decide whether you need a hosted service, open-source software, or self-hosted infrastructure.
7. Observability
For production systems, useful capabilities include request tracing, latency measurements, error reporting, and per-model usage analytics.
8. Ecosystem compatibility
Consider how well the gateway fits your existing framework, programming language, and deployment environment. For example, teams using LangChain and LangGraph may want to evaluate LangSmith LLM Gateway.
Are AI Gateways Worth Using?
AI gateways can be valuable when an application depends on multiple AI models, needs centralized monitoring, or requires stronger control over costs and reliability.
They may be unnecessary for a small prototype that uses one model and has minimal operational requirements.
For developers experimenting with multiple models, OpenRouter is a useful option to evaluate.
For teams seeking an open-source gateway, LiteLLM is worth considering.
For production applications requiring centralized gateway management, Portkey is ideal.
Cloudflare AI Gateway and Vercel AI Gateway can fit their respective ecosystems.
Kong AI Gateway may suit organizations with broader API management needs.
Teams building agents with LangChain and LangGraph should use LangSmith LLM Gateway.
The best choice depends on your application's architecture, model requirements, security policies, traffic, and budget.
Conclusion
AI gateways are becoming an important component of modern AI infrastructure because they help developers manage model access, routing, reliability, security, and monitoring through a centralized layer.
Popular platforms such as Portkey, LiteLLM, OpenRouter, Cloudflare AI Gateway, Vercel AI Gateway, Kong AI Gateway, and LangSmith LLM Gateway offer different approaches to AI request management.
Each platform has its own strengths. Some emphasize model discovery, others focus on open-source infrastructure or enterprise API governance, and others integrate closely with existing AI development ecosystems.
Before selecting a gateway, identify your technical requirements, test representative workloads, and review the platform's current pricing, provider support, security controls, and deployment options.
By choosing an appropriate gateway, development teams can build AI applications that are easier to manage, monitor, and evolve as their requirements change.
Frequently Asked Questions (FAQs)
What is an AI gateway in simple terms?
An AI gateway is a layer between an application and AI providers that helps manage requests, model access, monitoring, and other supported operational controls.
What are the best AI gateways in 2026?
Popular options to evaluate include Portkey, LiteLLM, OpenRouter, Cloudflare AI Gateway, Vercel AI Gateway, Kong AI Gateway, and LangSmith LLM Gateway. The best option depends on your use case and requirements.
What is the difference between an AI gateway and an AI router?
An AI router primarily selects where an AI request should go. An AI gateway typically provides a broader layer that may include routing, authentication, monitoring, caching, and security controls.
Does LangChain have an AI gateway?
Yes. LangChain offers LangSmith LLM Gateway for managing and governing requests to supported models and providers. It complements LangChain and LangGraph rather than replacing them.
Is Portkey an AI gateway?
Yes. Portkey provides an AI gateway and related infrastructure capabilities for managing AI requests, including supported routing, retries, fallbacks, and observability features.
Is LiteLLM an AI gateway?
Yes. LiteLLM provides an open-source gateway and SDK for accessing multiple LLM providers, with supported routing, load balancing, retries, and usage controls.
Is OpenRouter an AI gateway?
OpenRouter is primarily known for unified access to multiple AI models and its routing capabilities. It overlaps with AI gateway functionality, although its emphasis differs from platforms focused on broader enterprise gateway management.
Can an AI gateway reduce AI costs?
Potentially. A gateway can help monitor spending, enforce limits, apply caching, and support cost-aware routing. Actual savings depend on model prices, workload patterns, and configuration.
Are AI gateways free?
Some platforms provide open-source software or free tiers, while hosted and enterprise offerings may charge for usage or advanced features. Model inference charges may be separate.
Do I need an AI gateway for my application?
Not necessarily. Direct API integration may be sufficient for a simple application using one model. A gateway becomes more useful when you need multiple providers, centralized monitoring, routing, fallback policies, or organization-wide controls.
