AI Gateway vs API Gateway: Differences and Leading Products (2026)

When we put this question to five AI assistants in October 2026, all five named Kong first: an API gateway company. That tells you how blurry the line has become. An API gateway controls access and traffic for general APIs. An AI gateway adds model-aware controls, including provider routing, token accounting and model policies. Some products do both. We make Requesty, one of the products compared here.

What is an AI gateway?

An AI gateway sits between a client and model providers. It receives a model request, applies configured policies and forwards it to a model endpoint. Depending on the product, it tracks token usage, manages provider credentials, routes to a backup model or inspects prompts for sensitive data.

An LLM gateway focuses on language-model calls. An AI gateway is a broader term: some products also handle image or audio APIs, connections to tools through the Model Context Protocol (MCP), or agent-to-agent traffic. Names alone do not tell you which features a product has. Kong describes model, MCP and agent-to-agent traffic as separate types.

MCP is a separate path. For client-side tools, the model returns a tool request and the application or agent executes it. Provider-hosted tools can run on the provider's infrastructure. An MCP gateway controls configured connections to MCP servers; its credentials and permissions are separate from model inference. See what an MCP server does for the protocol details.

A classic API gateway still earns its place for application traffic. AWS describes API Gateway as a way to create and manage REST, HTTP and WebSocket APIs, with authorization, traffic management and monitoring. It is not limited to REST.

AI gateway vs API gateway: side by side

The columns describe a general-purpose API gateway baseline and AI-aware capabilities. A single platform can have both. Kong, Apigee and Azure API Management all add AI controls to their API platforms. Individual capabilities depend on the product and configuration.

Why one request is not one unit of cost

Assume a model charges $2 per million input tokens and $10 per million output tokens. Ignore caching, gateway fees and other charges. Both calls below count as one request, but their token bills differ:

A request-per-minute limit controls call frequency. A tokens-per-minute limit controls token consumption over time. A monthly dollar budget controls spend, and an in-flight limit controls unfinished calls. Even a token quota is not a dollar budget: input, output and cached tokens can have different rates. Azure API Management documents token-rate limits and period quotas; Requesty's in-flight limit is a concurrency control, not a requests-per-minute limit.

When testing a budget, send concurrent requests near the threshold and check when the system rejects new work. Usage accounting and requests already in flight can affect the final amount.

Three kinds of caching

Caching is another place where one word hides several different mechanisms:

Requesty Auto Caching uses provider-level prompt caching for supported providers. The model still receives the request. Cloudflare's exact response cache matches a key derived from the request, while Azure's semantic cache uses vector similarity to retrieve prior completions.

Two similar questions can need two different answers. Test semantic cache hits for correctness and freshness. For any shared response cache, include tenant and authorization context in the cache design. Cloudflare warns that requests sharing a custom cache key share the cached answer.

How the gateways fit together

In practice, teams start from one of three architectures. The choice is about which component controls each call. You do not have to chain two proxies on every request.

With a direct integration, the application calls the provider API. For a single-provider prototype with simple access and accounting needs, that is all you need.

With an AI-capable API platform, the platform can handle application ingress and model access. Those are separate calls, even if the same product handles both. Azure's AI gateway is part of API Management.

With separate gateways, a client calls your application through an API gateway. The application then calls a model provider through an AI gateway. An agent or coding tool can also call the AI gateway directly. A client can be configured to send model traffic through both gateways in a chain, but that is a design choice.

Which products lead in 2026?

The best shortlist depends on your existing platform, deployment preference and the controls your application needs. The table groups products by their starting point, not by market share. Product details are as of October 2026.

Start with your existing API or cloud platform

If you already run Kong, look there first. Its 2.x concepts use reusable AI Model Provider and AI Model entities with AI Policies. Earlier Kong Gateway deployments use proxy plugins.

Start with an AI-focused gateway

Pick by the job you need the gateway to do:

• Pick Kong if you already run Kong and want to extend that platform.
• Pick Azure API Management or Apigee if that is your existing API platform and its AI policies cover your workload.
• Pick LiteLLM if an open-source, self-hosted proxy is a requirement and your team can operate it.
• Pick Portkey if you want a hosted gateway with configurable Prisma AIRS guardrails in Palo Alto's stack. Portkey's guardrails run asynchronously by default; configure synchronous checks with deny=true to block failed requests. Output guardrail results for streamed responses are informational after the stream completes.
• Pick OpenRouter for hosted model discovery, including free models.
• Pick Requesty for managed model routing, per-key and team spend caps, and an EU gateway alongside your existing API gateway.

OpenRouter charges 5.5% on Standard credit purchases and 8% on Business credit purchases. Compare total invoices, including provider charges, BYOK terms and any feature fees.

When should you use both?

Keep your existing API gateway and add an AI gateway when the API team already controls application ingress but model traffic needs its own provider credentials, routing policies or spend attribution. This also lets a coding agent use the model gateway without routing through an application API first.

Use the AI capabilities in your existing platform when they cover the model endpoints, policies and deployment requirements you have. Adding another gateway gives you another component to operate and another failure point to investigate. For a single provider and a small application, direct access may be enough.

Before deciding, trace one production request end to end. Identify who authenticates the user, which component holds provider credentials, where usage is attributed, and what happens if either gateway is unavailable. If an MCP tool is involved, trace that call separately.

Connect Requesty to an existing stack

Keep your API gateway in front of your application and use Requesty to centralize its outbound model calls. Point the OpenAI SDK at Requesty's OpenAI-compatible endpoint, then choose your model and fallback order.

1. 1
   Create a key

   Sign up for Requesty, create an API key, and install the OpenAI Python SDK. For the paid model in this example, add a prepaid balance as described in the billing documentation. Set the key in your environment:

   Shell

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   pip install openai
   export REQUESTY_API_KEY="your-requesty-key"

2. 2
   Send a model request

   The model ID below selects an OpenAI endpoint from the Requesty model catalog. For tool calling with GPT-6.1 Terra, use the Responses API.

   Python

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   import os
   from openai import OpenAI
    
   client = OpenAI(
       api_key=os.environ["REQUESTY_API_KEY"],
       base_url="https://router.requesty.ai/v1",
   )
   response = client.chat.completions.create(
       model="openai/gpt-6.1-terra",
       messages=[{"role": "user", "content": "Summarize: The team reduced request failures by adding a backup provider."}],
   )
   print(response.choices[0].message.content)

3. 3
   Add a fallback policy

   In the Requesty console, create a fallback policy named production and select compatible, approved models in order. Replace the code's model ID with policy/production. Test the requests your application sends against every candidate.

Set monthly spend limits for API keys, users and the organization. Shared team budgets require Enterprise and support enablement of Group Budget mode; the default mode assigns individual member limits. Review usage in analytics. Approved Models restricts model access.

Optional guardrails scan incoming requests for PII and secrets. Mask replaces detected values before forwarding and restores matching values in the response. The scanners do not scan responses for new sensitive data. Standard-plan scanner charges are usage-based.

For EU requirements, select the EU gateway and check the inference region of every selected model and fallback. Gateway location and inference location are separate decisions. For agent tools, configure the MCP Gateway as a separate tool-access path.

Test before production

Run the same workload against each shortlisted product. Start with your real request shapes and a recorded quality target. Then test failures and policy boundaries alongside successful text responses.

An application-supplied metadata label is useful for attribution, but it is not proof of an authenticated end user.

Streaming is where most gateway trials go wrong. A failure before the first output gives the application a chance to retry without showing a partial answer. Once text or a tool call has reached the client, replaying the request can duplicate output or side effects. The OpenAI Python SDK does not automatically retry stream consumption for that reason. API gateways can stream too: AWS API Gateway documents streaming for supported REST integrations, with different behavior from its default buffered mode.

Last, test the backup itself. A second provider does not help if it lacks a required tool schema, shares the same exhausted quota, violates your region policy or fails at the same time. Use the OpenAI-compatible API guide to plan feature checks.

If you already have an API gateway, start by testing its AI policies on one production-shaped model workflow. This week, trace that workflow from user authentication to provider response, then run the checklist against it. If the missing controls sit on outbound model traffic, add an AI gateway for that path and test the fallback before expanding the rollout.