API Gateway and AI Gateway: Do You Need Both?

API Gateway and AI Gateway: Do You Need Both? Posted in Strategy Janet Wagner October 21, 2026 Traffic makes the digital world go round, but not all of it is the same. Much of it goes to backend services — APIs or microservices — that give applications the capabilities they need to serve users. However, a rapidly increasing volume of traffic goes from applications to large language models (LLMs), as well as from AI agents to APIs. API and application developers need to govern this traffic — controlling costs, enforcing security, and stabilizing performance — whether it’s a client consuming services or an application calling an LLM. The best way to do that is with an API gateway or an AI gateway. In many cases, you may need both. This article explains what an API gateway and an AI gateway are, the key differences between them, and why you would need them both. What Is an API Gateway? An API gateway manages traffic coming in from clients to your APIs or microservices. It serves as a reverse proxy, allowing clients to talk only with the gateway and not the services sitting behind it. Unlike generic reverse proxies, API gateways are designed specifically for APIs, so they include specialized capabilities, such as: Traffic routing API rate limiting Load balancing Authentication and authorization Monitoring and observability Protocol conversion Examples of open source API gateways include Apache APISIX, Gravitee, Kong, KrakenD, NGINX, Traefik, Tyk, and WSO2. Companies that offer commercial API gateways include Amazon (AWS), Axway, F5 NGINX, Google Cloud (Apigee), MuleSoft, and Tyk. Developers can use the capabilities of API gateways to improve API design and security. What Is an AI Gateway? An AI gateway is middleware designed specifically for managing interactions with LLMs. It provides a single control plane to govern AI traffic. AI traffic includes prompts sent to LLMs and responses to those prompts. For some AI gateways, AI traffic also includes tool-call requests facilitated by Model Context Protocol and traffic stemming from Agent2Agent protocol communication. Common capabilities of AI gateways include: Model routing Semantic caching Token rate limiting Prompt enrichment Input and output guardrails LLM observability Companies that develop AI gateways include API7.ai, Gravitee, Kong, KrakenD, Solo.io, TrueFoundry, Tyk, and Zuplo. API Gateway vs. AI Gateway: Key Differences API gateways and AI gateways are designed to handle traffic, but in different capacities. An API gateway is mainly about inbound traffic, providing a protective layer between a client or web application and your application’s backend and services. When a client sends a request to that backend, the request follows a path controlled by the API gateway. An AI gateway is primarily about outbound traffic, specifically the calls your application makes to an external LLM like Claude, Gemini, or ChatGPT, or a self-hosted model like DeepSeek, Mistral, or Llama. It governs how applications call and interact with AI models. Currently, these two gateways are not interchangeable — they cater to different types of traffic that go in different directions. That may eventually change (more on this later), but until then, API gateways and AI gateways differ in several ways. API gateway AI gateway Main purpose Manage and secure traffic to backend services (APIs and microservices) Manage and secure interactions between applications and LLMs Position Primarily inbound: client to services Primarily outbound: application to LLMs Familiar with API requests, paths, headers, and payloads LLM prompts, tokens, and responses Caching Exact match (same URL and headers return the same response) Semantic (similar prompts return the same response) Rate limiting Based on number of requests (requests per second or per minute) Based on LLM token count (input and output tokens) Security Authentication, authorization, web application firewall rules, and schema validation User input filtering, model response validation, and data loss prevention policies Monitoring API-focused: usage, latency, traffic, error rates, and logs Model-specific: token usage, model costs, model latency, inference time, and tool usage Why You Would Need Both If your application exposes more than a few services, you should have an API gateway. It is essential for handling traditional HTTP traffic and includes features that strengthen API governance and security. Does your application rely on multiple LLMs? Then you’ll likely need an AI gateway. It helps you manage LLM token costs, use different LLMs to power the same application, and add security measures for risks like prompt injections. A growing number of organizations are developing LLM-driven applications — and you really can’t have AI without APIs. So, for most companies today, having both an API gateway and an AI gateway makes sense. Sahajmeet Kaur, part of the Founder’s Office at TrueFoundry, published a detailed story on DEV about the team’s experience running Kong for microservices and then routing LLM traffic through that same API gateway. However, according to Kaur, that setup wasn’t effective for managing AI workloads. “We had no idea what each team was spending on tokens. We had no way to set a budget cap that would fire before the bill arrived,” says Kaur in the article. “Our rate limits were based on requests per minute, which meant a single request with a 50k token prompt counted the same as one with a 200 token prompt.” The article makes clear that Kaur’s criticisms are not aimed at Kong. The API gateway functioned exactly the way it was supposed to. The real problem was that the team was building an AI gateway inside an API gateway. So, they moved to the TrueFoundry AI Gateway for LLM traffic and kept the Kong API gateway for everything else. Gateways: A Steady Convergence Right now, API gateways and AI gateways manage distinct types of traffic and sit in different places relative to clients. However, the boundaries between the two are steadily blurring. More vendors are developing platforms that house the capabilities of both, creating a single layer for all traffic types — traditional HTTP and AI alike. The distinction between API and AI gateways may soon fade. AI Summary This article explains the differences between an API gateway and an AI gateway, and why most organizations building LLM-driven applications benefit from running both. An API gateway manages primarily inbound traffic from clients to backend services, acting as a reverse proxy with capabilities like traffic routing, rate limiting, load balancing, authentication, authorization, and protocol conversion. An AI gateway manages primarily outbound traffic from applications to LLMs, offering model routing, semantic caching, token rate limiting, prompt enrichment, input and output guardrails, and LLM observability. The two gateways differ in rate limiting (requests versus tokens), caching (exact match versus semantic), security focus (schema validation and firewall rules versus prompt filtering and response validation), and monitoring (API usage versus token costs and inference time). Routing LLM traffic through a traditional API gateway can leave teams unable to track token spending or set budget caps, since request-based rate limits treat large and small prompts the same. Vendors are increasingly combining both capabilities into a single platform, so the distinction between API gateways and AI gateways may fade over time. Intended for API architects, platform engineers, and developers managing API and LLM traffic in AI-enabled applications. The latest API insights straight to your inbox