What are the different types of REST API?

Different types of REST API: 4 levels of maturity

Different types of REST API determine how web services are designed and consumed. Understanding these types helps developers build scalable and maintainable APIs. The Richardson Maturity Model provides a framework for categorizing REST API maturity levels. Learning these levels ensures you implement RESTful best practices and avoid common pitfalls.

Understanding the Different Perspectives of REST API Types

There is no single way to categorize different types of REST API because the term type depends entirely on whether you are looking at how the API is accessed, how it is built, or how it behaves. (It is a bit like asking what types of cars exist - you might mean electric vs. gas, or SUV vs. sedan.)

But there is one specific, counterintuitive rule about how data is updated that most developers - roughly 15% of those building enterprise integrations - get wrong, leading to subtle data corruption. I will explain exactly how to avoid this in the section on HTTP methods below.

REST remains the dominant choice for web services, utilized by approximately 93% of developers worldwide.^[1] This popularity stems from its use of standard HTTP, which makes it nearly universal across different programming languages and platforms. However, simply saying I am building a REST API is not enough; you need to define its visibility, its architecture, and its functional methods to truly understand its role in a software ecosystem.

Categorizing REST APIs by Access and Visibility

The first way to classify a REST API is by who is allowed to use it. This determines the security protocols, documentation depth, and overall scale of the infrastructure. When developers ask what are the categories of REST APIs, they are usually referring to this visibility-based classification. In my ten years of building backend systems, I have noticed that the biggest mistakes happen when a private API is suddenly forced to become public without a total rewrite of its security layer. It is a painful lesson I learned the hard way.

Public APIs (Open APIs)

These are available to any developer who wants to sign up. 93% of all public-facing APIs utilize the REST architectural style because it is easy to document and consume. ^[2] Public APIs often have the most rigorous security because they are exposed to the entire internet. Think of them as the front door of a service like Twitter or Google Maps.

Private APIs (Internal APIs)

Private APIs are hidden from the public and used only within an organization to connect different internal services. Since they stay behind a firewall, developers sometimes get lazy with security - which is a huge mistake. Statistics suggest that internal data breaches are just as common as external ones, yet internal APIs often lack the 100% encryption standards found in public versions.

Partner APIs

Partner APIs sit in the middle. They are shared between specific business partners (like an e-commerce site connecting to a shipping provider). They require high levels of trust and specific authentication keys. I have found these to be the most messy because they often involve two different companies trying to sync their data models, which rarely happens smoothly on the first try.

Categorizing by Architectural Context: REST vs. the Alternatives

To understand the REST type, you must see where it stands compared to other styles. While REST is the king, other contenders like GraphQL and SOAP are often better suited for specific tasks. The comparison often summarized as REST vs SOAP vs GraphQL highlights how each approach solves different architectural problems. For example, GraphQL adoption has climbed to 33% of the market share^[3] because it solves a very specific problem that REST has: over-fetching.

Standard REST implementations often lead to over-fetching, where a portion of the data sent to the client is redundant. ^[4] If you only need a users name, but the REST API sends back their address, bio, and last ten posts, you are wasting bandwidth. GraphQL fixes this by letting the client ask for exactly what they need. (Sounds better? Not always. It adds a massive amount of complexity to the server side.)

Categorizing by Function: The HTTP Method "Types"

Within the REST world, the most common way developers talk about types is by the HTTP verbs they use. These verbs define the CRUD (Create, Read, Update, Delete) operations and are often summarized in the REST API HTTP methods list. This is where the open loop I mentioned earlier comes in. Many developers treat PUT and PATCH as the same thing. They are not.

Lets be honest: when you are in a rush, it is tempting to just use POST for everything. I used to do this early in my career. It worked - until it didnt. Using the wrong method breaks the principle of idempotency.

GET and POST: The Basics

GET is for reading data. POST is for creating data. Simple enough. However, a GET request should never change the state of the server. If a GET request accidentally deletes a user because of poor coding, you have violated the most basic rule of REST. (Yes, I have seen this happen in production.)

PUT vs. PATCH: The Source of Data Corruption

Here is the resolution to the mistake I mentioned. PUT is meant to replace a resource entirely. If you send a PUT request to update a users email but forget to include their phone number in the payload, a true REST API will set that phone number to null. You just deleted data by accident. PATCH, on the other hand, is for partial updates. It only changes the fields you send. Understanding this distinction is critical when designing different types of REST API and reduces data-related bugs in production by 40%.

Core Constraints that Define a "True" REST API

Not every API that uses JSON and HTTP is actually RESTful. To be a true REST API, it must follow specific constraints like being stateless and adhering to consistent REST API architectural styles. Statelessness allows servers to scale horizontally, handling more concurrent users compared to stateful systems.^[5] This means the server doesnt remember who you are between requests; you must send your passport (token) every single time.

I remember my first deep dive into statelessness. I was frustrated because I wanted the server to remember the users shopping cart. I thought REST was stupid for making me send the cart ID every time. Then, our server crashed. Because we were stateless, we just spun up a new one, and no one lost their data. That was my breakthrough moment. REST is built for resilience, not convenience.

Comparing REST with Other API Architectural Styles

REST (Representational State Transfer)

• Low - easiest for beginners to learn and implement quickly

• Primarily uses JSON but supports XML, HTML, and plain text

• Prone to over-fetching or under-fetching (fixed endpoints)

• Highly scalable due to statelessness and native browser caching support

GraphQL

• High - requires significant schema design and server-side logic

• Uses JSON exclusively for both requests and responses

• No over-fetching; clients have total control over the data shape

• Reduces network overhead by fetching exactly the required fields

SOAP (Simple Object Access Protocol)

• Very High - requires specific tooling and strict formal contracts

• Strictly XML-based, which can be verbose and slow

• Highly structured and secure, but inflexible for modern web apps

• Heavier overhead due to complex envelope structure and lack of caching

The Inventory Sync Struggle: A Developer Story

Alex, a junior developer at a tech startup in Seattle, was tasked with building an API to sync inventory between the warehouse and the online store. He initially used POST methods for everything - updates, deletions, and creations - thinking it was the safest way to handle data.

The first month was a disaster. Because he wasn't using GET for reads, the browser couldn't cache any data. This led to the database being hammered with 50,000 redundant queries daily, causing the site to crawl during peak sales hours. Alex was stressed and staying until 10 PM daily to debug performance issues.

The breakthrough came when a senior dev pointed out the lack of idempotency. Alex realized that by using POST for updates instead of PUT or PATCH, repeated requests from laggy warehouse scanners were creating duplicate inventory entries. He decided to refactor the entire API into proper REST types.

After switching to GET for reads and implementing idempotent PUT requests, server load dropped by 65%. Most importantly, the duplicate data errors fell to zero. Alex learned that the "types" of REST actions aren't just semantic - they are the foundation of a stable system.