The essential course that AI Agent engineers cannot avoid: API Gateway vs API Management

Author: Wangchen

The terms “API Management” and “API Gateway” are often used interchangeably, especially in the context of large model applications (large models are considered catalysts for the API economy/monetization). However, they represent different concepts that serve different stages of the API lifecycle. This article will explore the origins and development of both, compare their key differences, how they work together, and future trends. It is hoped that this article will assist technical teams in making more informed architectural decisions.

I. Origins and Development

Evolution of API Gateway

The API Gateway has evolved in different forms alongside the evolution of software architecture.

The evolution of software architecture is a process of continuous adaptation to changes in technology and business needs, experiencing monolithic architecture, vertical architecture, SOA architecture, microservices architecture, and cloud-native architecture. With the popularity of large models, it is beginning to evolve towards AI-native architecture.

Traffic Gateway

Under monolithic architecture, the gateway is responsible for managing and optimizing data traffic to enhance business scalability and high availability. Nginx, as a representative software of traffic gateways, is well-regarded for its efficient performance and flexible configuration. The core purpose of a traffic gateway is to solve the traffic load balancing problem of multiple business nodes. By distributing requests across different servers, it evenly shares the load, avoids single points of failure, and ensures service stability and continuity.

Microservices Gateway

Since 2014, as many internet companies have split monolithic architecture into hundreds of microservices, the complexity of inter-service communication has increased exponentially. At the same time, with the rapid development of the internet economy, access traffic has surged. Nginx has struggled to manage traffic under a microservices architecture, prompting engineers to urgently need a feature-rich gateway to address the following problems:

• Traffic Routing: Forwarding traffic to backend services (such as microservices, third-party APIs, etc.) based on request paths or parameters.

• Protocol Conversion: Converting the protocol of client requests (such as HTTP/REST) to the protocol required by backend services (such as Dubbo, gRPC, etc.).

• Basic Security Capabilities: Providing authentication (such as API keys, JWT), rate limiting, firewalls, and other functions to prevent malicious attacks.

• Performance Optimization: Supporting caching, load balancing, and request circuit breaking to enhance system stability and response speed.

Early open-source implementations such as Zuul, Spring Cloud Gateway, etc., aimed to achieve load balancing, rate limiting, circuit breaking, and identity verification, managing and optimizing interactions between microservices through a unified entrance. This not only simplified the complexity of client and microservices communication but also provided additional protection for system security.

Cloud-Native Gateway

Cloud-native gateways are an innovative type of gateway that emerged with the widespread adoption of K8s. The natural isolation of networks inside and outside K8s clusters requires gateways to forward external requests to internal services within the cluster. K8s uses Ingress/Gateway API to unify the gateway configuration method while providing elastic scaling to help users solve application capacity scheduling issues.

Based on this, users have new demands for gateways: they hope that gateways can handle massive requests with traffic gateway characteristics while also possessing microservices gateway characteristics for service discovery and governance, and requiring gateways to have elastic scaling capabilities to address capacity scheduling issues. As a result, a unified multi-layer gateway architecture has become a trend. For example, Envoy and Higress are typical open-source cloud-native gateways that unify north-south and east-west traffic management.

AI Gateway

New capabilities were developed to address the new demands of AI scenarios, including traffic management for large models and MCP Servers, featuring long connections, high bandwidth, and high latency, providing:

• For large models: flexible switching between multiple models and fallback retries, content safety and compliance for large models, semantic caching, load balancing for multiple API keys, token quota management and rate limiting, large model traffic shading, call cost auditing, etc.

• For MCP Servers: supporting quick conversion from API to MCP and providing MCP Server proxy, security authentication, as well as unified observability, rate limiting, and other governance capabilities.

For instance, Higress has evolved capabilities specifically designed for AI scenarios based on cloud-native gateways.

Evolution of API Management

The evolution of API management is also a history of modern software engineering's continuous pursuit of control, observability, and operability. From the initial stage of interface document sharing, API management has gradually developed into a complete API lifecycle governance system, becoming one of the core pillars of modern digital infrastructure.

1. Documentation Stage: Coming from interface documentation

Typical Period: 2005–2010 (with the rise of REST as a watershed)
Representative Tools: Word documents, Wikis, interface manuals, early Swagger

The earliest “API Management” essentially involved the writing and maintenance of interface documentation. Interfaces often existed in the forms of “function documentation” or “HTTP call instructions”:

• Documents were usually manually maintained and lacked standards;

• Updates lagged, leading to inconsistencies with actual interfaces;

• There was no unified collaboration process, completely reliant on developer agreements.

This stage accumulated early user habits and demand models for subsequent standardization.

2. Standardization Stage: Interface design enters a normative track

Typical Period: 2010–2016
Representative Norms/Tools: Swagger (OpenAPI), RAML, API Blueprint, Stoplight

With the popularity of REST APIs, interface management gradually shifted from “post-documentation” to “pre-design”:

• Swagger/OpenAPI gradually became a de facto standard;

• Developers began using structured specifications to define APIs (such as JSON Schema);

• Tools that support interface mocking (Mock) and automatic document generation emerged;

• API testing, acceptance, and integration became more efficient and standardized.

This initiated the emergence of a specification-centric API lifecycle.

3. Platformization Stage: Establishing API collaboration and governance systems

Typical Period: 2016–2020
Representative Tools: APIFox, Postman, SwaggerHub, Stoplight Studio, YApi

Against the backdrop of the explosion of microservices and front-end/back-end separation, the number of APIs surged, and manual management became unsustainable. Platformization became the trend:

• Integrating design, documentation, mocking, testing, collaboration into one;

• Supporting version management, change review, and permission control for interfaces;

• Teams could manage interfaces like managing code;

• Interfaces became contracts between teams;

• Simultaneously balancing developer experience (DX) and interface asset management.

This type of platform often focuses on the R&D stage, not necessarily covering production environments or traffic governance, but significantly improving development efficiency and quality.

4. Lifecycle Governance Stage: Interface assets enter the DevOps process

Typical Period: 2020–2023
Representative Platforms: Backstage, Gravitee, Tyk Dashboard, Apigee, Kong Konnect (in part)
Key Characteristics:

• APIs are incorporated into SDLC (Software Development Lifecycle) management;

• Unified governance standards: naming, categorization, dependencies, approval, and release;

• Automation and CI/CD process integration (e.g. API Linter verification, change compliance checks);

• View from the entire lifecycle perspective: design → development → testing → release → monitoring → iteration;

• Introducing the “API Catalog” concept, similar to a code repository for interface repositories;

• Managers can visually grasp the structure, dependencies, and quality metrics of API assets.

This stage marks the emergence of APIs as governable digital assets, rather than engineering byproducts.

5. Commercialization and Open Platform Stage: APIs as a Service

Typical Period: 2022 to present
Representative Products: Apigee, AWS API Gateway Portal, Azure API Management, Alibaba Cloud Open Platform

Companies have begun to operate and commercialize APIs as products and services:

• Building open platforms for partners/developers (Developer Portal);

• Supporting registration, invocation, subscription, billing, quotas, monitoring;

• APIs have product features like “configurable SLA,” “service level,” “version control,” and “lifecycle notifications”;

• Managers manage APIs like operating SaaS products;

• This aids in the reuse, service packaging, and monetization of APIs.

This stage marks the leap of APIs from “internal tools” to “vehicles for enterprise open ecosystems.”

II. Key Differences Comparison

When discussing the key differences between API Gateways and API Management, a common metaphor is: “API gateways are like doormen, while API management is like property management.” This is certainly vivid, but to truly understand the fundamental differences, one must return to the problem domains they focus on.

1. Different Starting Points: Runtime vs Lifecycle

The starting point of the API Gateway is runtime request control. It addresses issues such as “after a request comes in, how to forward it, how to rate limit, whether it is secure, and whether the return is compliant.” These are all real-time processing traffic issues, thus the gateway components must be high-performance, low-latency, and close to the service call chain, with responsibilities similar to infrastructure.

In contrast, the starting point of API Management is the full lifecycle governance of APIs. It focuses on questions like “how to define interfaces, how to write documentation, how to control versions, how to enable third parties to use securely, how to measure and bill, and how to deprecate obsolete APIs,” which are more service-oriented issues. It is aimed at managing APIs as an “asset,” not just a single call at runtime.

This difference in starting points is the root of the distinctions between the two.

2. Different User Roles: Architect vs Operator

API Gateways are deployed and configured primarily by platform teams or operations and architects. For example, in cloud-native scenarios, the gateway is responsible for taking over all incoming and outgoing traffic, integrating security authentication, service discovery, load balancing, and more.

On the other hand, API Management serves more API designers, product managers, and even developer relations (DevRel) teams. It provides tools for documentation, mocking, change notifications, publishing processes, usage metrics, etc., and serves as the core platform for building developer ecosystems and interface asset directories.

It can be said that gateways are more akin to “infrastructure,” while API management resembles an “application middle platform” or “service operation tool.” Typical open API platforms include Gaode, WeChat Public Accounts, Alibaba Cloud Open Platform, and various large model API open platforms.

3. Different Technical Core: Traffic Proxy vs Metadata Management

From a technical implementation perspective, the core of an API Gateway is a high-performance proxy service (such as Envoy, Higress) that directly participates in the network path to intercept and process each request.

The core of an API Management platform is a metadata-driven API orchestration system that manages interface definitions (such as OpenAPI), permissions, versions, subscriptions, documentation, etc., and can integrate with CI/CD and SDK generation, API Portals, and other peripheral capabilities.

Therefore, there are significant differences in implementation approaches, deployment modes, performance requirements, and observation dimensions between the two.

4. Practical Integration Scenarios: API as Interface, but also as Asset

During the digital transformation of traditional enterprises, we often say “APIs are services,” which looks at APIs from the perspective of business output. However, to treat an API as an externally provided service, it is necessary not only to control who can access it (the gateway’s responsibility) but also to manage its lifecycle, stability, version iterations, and developer experience (the management responsibility).

Thus, large enterprises or platforms usually deploy both capabilities simultaneously: using gateways to control underlying request traffic and using API Management platforms to assist in “producing, operating, and commercializing” APIs.

Summary: Key Differences Comparison Between API Gateway and API Management

III. Collaborative Work

In real-world systems, API Gateways and API Management have never been a matter of “either-or,” but rather a combination of “two swords merged into one.” One is responsible for runtime scheduling and protection of traffic, while the other is responsible for the production, publishing, and operation of APIs. Only by coordinating both can a high-efficiency and sustainable API infrastructure be built.

Collaborative Roles in Layered Architecture

In a platformized architecture, the API lifecycle can be abstracted into three layers of responsibilities:

• Production Layer (API Design and Implementation): Developers use standards like OpenAPI/GraphQL to define APIs.

• Publishing Layer (API Management Platform): Manages API versions, permissions, documentation, subscriptions, audits, etc.

• Runtime Layer (API Gateway): Responsible for request access control, protocol conversion, routing, forwarding, and security interception.

In these three layers, the API Management platform leads production and publishing, while the API Gateway controls runtime access. The two work together through mechanisms such as interface registration, service discovery, and policy delivery.

For example:

• A developer publishes a new interface /v2/user/info in the API Management platform and sets API Key binding as a requirement for users.

• The platform will send interface definitions and authentication rules to the API Gateway.

• The Gateway will intercept the requests, verify identities, and forward them to backend services.

• Call logs, failure rates, and other data will be uploaded back to the management platform as a basis for monitoring and operational analysis.

This creates a closed loop from design → publishing → invocation → feedback return.

Collaboration Methods: Policy Interaction and Interface Synchronization

Specifically, the collaboration between the two is mainly reflected in the following aspects:

Tool Combinations: How Open Source and Commercial Ecosystems Complement Each Other

This collaboration has already matured in modern API toolchains, whether in open-source or commercial solutions:

• Open Source Combination: Higress + Apifox

• Commercial Platforms: API Gateway + API Management Tool Enterprise Version, in which Alibaba Cloud API Gateway and Google Apigee provide unified control and data planes, integrating API management and gateways.

These solutions collectively reflect a trend: the more mature the platform, the more it emphasizes the integration and automation level between API management and gateways.

IV. Future Development Trends: Evolving Towards AI Gateways and MCP Server Management

As applications move towards the paradigm of large models, the role of APIs is undergoing fundamental changes. From “accessing a backend service interface” to “calling MCP Server through a large model.” This shift brings new challenges, pushing API gateways and API management into a new phase, specifically AI gateways and MCP Server management.

AI Gateways: Transitioning from Container and Microservices Entry to Model and MCP Entry

In the context of container and microservices architecture, the API gateway is responsible for access control, service discovery, protocol conversion, and security policies. However, in the era of large models, it redefines the meanings of “traffic” and “service,” and the API gateway has also completed the transition from microservices entry to model entry.

Why is an AI Gateway Needed?

In large model applications, traffic is no longer short-lived HTTP requests but long connections, semantic, high-cost, and complex state inference requests. These types of requests possess the following new characteristics:

• Dynamic Changes in Call Paths: Different scenarios require routing to different large models or model versions.

• Uneven Resource Consumption: The same request may consume thousands to tens of thousands of tokens, necessitating dynamic quota management.

• Strong Dependency on Request Context: Prompts, historical messages, and system settings can greatly affect model outputs.

• Sensitivity to Gray Control: New model launches need to support gray releases by user groups, fallback strategies, and metric monitoring.

• Significant Security and Compliance Pressures: Call contents and return contents may involve data security, copyright, and ethical issues.

These characteristics exceed the traditional responsibilities of API gateways, prompting the birth of AI gateway forms.

New Capability Structure of AI Gateways

AI gateways can be seen as “the infrastructure for large model interfaces,” retaining the core responsibilities of traditional gateways while extending the following two layers of capabilities:

• For large models: New capabilities are added regarding model availability, security protection, reducing model hallucinations, and observability; for details, please visit: here. In practical engineering, these capabilities are often built upon cloud-native gateways like Higress and extended through plugins for AI scene gateway capabilities.

• For MCP:

  • API-to-MCP: Offers direct conversion from REST API to MCP Server, avoiding redundant work in rebuilding and maintaining MCP Servers.

  • Protocol Offload: Seamlessly supports the latest official MCP protocol versions, lowering upgrade costs, such as supporting the conversion of SSE to Streamable HTTP, preventing even stateless applications from needing to use SSE.

  • MCP Marketplace: Provides an officially maintained MCP marketplace, ensuring that MCP services are available, usable, and safe.

It can be said that the emergence of AI gateways marks a shift in the semantics of “traffic”: it is no longer just a carrier of request bytes but also encompasses complex capabilities of semantic understanding, token distribution, cost scheduling, and intelligent decision-making, becoming the essential entry point for enterprises to build intelligent applications.

MCP Server: In the Era of Large Models, Management Tools are Also Needed

In traditional applications, APIs are deterministic input-output interfaces for consumer calls; however, in large model applications, the caller becomes the large model, and the callee becomes the MCP Server. Therefore, traditional API management platforms (designed based on Swagger specifications for design → development → testing → publishing → monitoring → iteration) can no longer fit the MCP specifications.

Just as the early explosion of REST APIs gave rise to API management tools such as Postman and Apifox, the prosperity of MCP will also give rise to management tools for MCP Servers (AI-native APIs), reflecting a new demand.

Referring to API management, it may need to possess the following capabilities:

• Production Layer (MCP Design and Implementation): Developers use MCP and other specifications to develop, define, and debug MCP, making it available for external Agent calls.

• Publishing Layer (MCP Management Platform): Manages MCP versions, permissions, documentation, subscriptions, audits, etc.

• Product Layer (MCP Marketplace): Achieves monetization of MCP Servers through a unified authentication system and builds an open market ecosystem centered on MCP products.

Looking back at the entire evolution of interface technology, we can observe a clear “dual-track evolution” trajectory: API Gateways are responsible for full lifecycle management of traffic, while API Management focuses on the full lifecycle management of APIs. The two naturally collaborate.

In the microservices era, one is responsible for guarding the entrance, while the other orchestrates the exit; in the large model era, they are gradually supporting the new paradigm of service-oriented models, platform-oriented calls, and automated governance together. In the future, APIs will not only connect but also become carriers of intelligent applications; API Gateways and API Management together build the foundation for modern enterprises' capabilities to open up internally and externally.