An API (Application Programming Interface) defines how two systems communicate, what data can be shared, and in what format.
But not all APIs are built the same. Over time, as applications evolved, so did the challenges they faced.
This led to the creation of new API styles, each designed to solve specific problems related to performance, flexibility, or real-time updates.
In this article, we’ll break down the 6 most common API styles that power modern software.
1. SOAP
In the beginning, there was SOAP (Simple Object Access Protocol).
As the internet began to rise in the late 1990s, companies needed a standardized way for applications to communicate across different platforms and programming languages.
SOAP emerged as the first major standard to solve this.
SOAP demands that all messages be in XML format, and it operates based on a very strict contract called a WSDL (Web Services Description Language).
Think of WSDL as a detailed instruction manual that precisely defines every operation you can perform.
SOAP is very “verbose,” meaning it uses a lot of text to describe a simple action. All that text for one simple request makes messages large, which slows down network transmission and processing.
Furthermore, the strict WSDL contract creates tight coupling; if the server changes any part of the contract, the client will often break.
While this was acceptable for large, internal enterprise systems, SOAP was just too heavy and inflexible for the fast moving web and new mobile apps. Developers needed something simpler, lighter, and more flexible that used the web’s own language, HTTP.
That’s how REST was born.
2. REST
In response to SOAP’s complexity, REST (Representational State Transfer) emerged and it quickly became the standard for the modern web.
REST represented a complete mindset shift. Instead of complex operations defined in a WSDL, REST treats data as “resources” (like /users/123) that you interact with using the standard HTTP methods (GET, POST, PUT, DELETE) that power the entire web.
It is stateless, meaning every request contains all the information needed to process it. It also embraced JSON over XML, which is far lighter and easier for both humans and machines (especially JavaScript) to read.
REST is amazing and runs the majority of the web. But as applications grew, two common problems emerged:
Over-fetching: Clients often receive more data than they need. You just need a user’s name, but
GET /users/123returns their name, address, entire post history, and a dozen other fields. This is wasted data that slows down apps, especially on mobile networks.Under-fetching: You need to show a user’s profile and their latest posts. This requires two separate requests:
GET /users/123and thenGET /users/123/posts. This “waterfall” of requests creates noticeable lag.
As frontend applications grew richer especially with mobile and single-page apps, developers wanted more control over the data they fetched.
This led to the rise of GraphQL.
3. GraphQL
What if you could ask for exactly what you need, all in one trip?
That’s what Facebook set out to solve when they created GraphQL in 2012 and open-sourced in 2015.
GraphQL is a query language for your API. The most important shift is that the client, not the server, defines the shape of the data it needs.
Instead of dozens of REST endpoints, you typically have just one (like /graphql) that accepts a query. The client sends a query that precisely describes the data it wants, and the server returns a JSON object in that exact same shape.
This single query can pull from multiple sources (like a user database and a post database) and return it all in one response.
There is no over-fetching (you only get the name and title, not the user’s email or post bodies) and no under-fetching (you get the user and their posts in one round trip).
However, GraphQL introduces its own set of challenges:
Complex Server Implementation: Building a GraphQL server (especially with nested data) can be more complex than a simple REST API.
Harder to Cache: The single endpoint and dynamic queries make traditional HTTP caching mechanisms less effective compared to REST.
GraphQL shares a fundamental trait with REST: it’s text based (JSON) and works on a client “pull” model, where the client must make a request.
For high-performance internal communication between dozens of microservices, the overhead of parsing text and the HTTP request-response pattern is too slow.
This need for raw speed led to gRPC.
4. gRPC
Developed by Google and open-sourced in 2015, gRPC is a modern Remote Procedure Call (RPC) framework designed for high-performance, language-agnostic communication between services.
It’s built for performance in two key ways:
Binary Format: It replaces text-based JSON with Protocol Buffers (Protobufs), a highly efficient binary format. This is much faster for computers to serialize (write) and deserialize (read).
HTTP/2: It runs on HTTP/2 by default. This modern protocol is far more efficient than HTTP/1.1, supporting features like multiplexing, where many requests can fly back and forth on a single connection.
You define your services and messages in a simple .proto file. This file acts as a language-agnostic contract, which gRPC uses to generate native code for any language you need (Java, Go, Python, etc.).
gRPC is an excellent choice for internal Service-to-Service Communication, especially over low-bandwidth networks.
However, its binary format is not human-readable, which can make debugging more challenging. It also isn’t directly supported by browsers, requiring a proxy like gRPC-Web for client-side use.
Although highly performant, gRPC is still a request-response pattern: the client asks, the server answers.
What if you need a persistent, two-way connection for a live chat app, a stock ticker, or a multiplayer game?
That’s where WebSocket comes in.
5. WebSocket
Traditional HTTP, which underpins REST, GraphQL, and gRPC, is a request-response protocol. The client must always initiate the conversation.
This becomes inefficient for real-time use cases, because the only way to constantly get updates is to repeatedly ask the server (polling) or keep a request hanging until something happens (long-polling).
WebSocket was built to solve exactly this. It creates a persistent, two-way communication channel over a single connection.
The connection begins as a normal HTTP request, then upgrades to a WebSocket. After that, both client and server can send data to each other whenever they want without new requests.
Client-side code example:
This model is perfect for live dashboards, multiplayer gaming, or chat applications where the server must push updates instantly.
But running WebSockets at scale is not trivial. You have to manage millions of long-lived connections, ensure state consistency across servers, and handle reconnects and failures.
Another limitation: the client must initiate the connection first. That works for browsers and apps, but fails for server-to-server events.
For example, what if Stripe’s server needs to notify your server that a payment just succeeded? Your server isn’t sitting there with an open connection to Stripe.
In such cases, we need a different mechanism: a Webhook.
6. Webhook
Webhooks are essentially “reverse APIs.” Instead of your application polling an API endpoint for new data, the server calls your application (acting as the client) when a specific event occurs. It’s a user-defined HTTP callback.
A Webhook is a server to server push model. You provide a URL (an endpoint on your server) to a third party service like GitHub, Stripe, or Slack. When a specific event happens (like a git push or a payment.succeeded), that service instantly sends an HTTP POST request with the event data (the payload) to your URL.
Webhooks eliminate the need for constant polling, allowing systems to react to events asynchronously and saving resources for both the client and the server.
Despite their power, Webhooks require careful implementation:
Security: Your endpoint is public. You must verify a signature (like
Stripe-Signature) to ensure the request is legitimate and not a forgery.Idempotency: Webhooks can fail and be retried. Your endpoint must be idempotent, meaning processing the same notification multiple times has the same effect as processing it once.
Summary
As you can see, there is no single “best” API. The “best” API is the one that fits your use case. In fact, a single, complex application will often use many of them together:
REST or GraphQL for its public web and mobile apps.
gRPC for its internal, high speed microservice communication.
WebSocket for its real time chat feature.
Webhooks to receive events from its payment provider.
The journey from SOAP to Webhooks shows an evolution towards more specific, efficient, and flexible tools. The real skill is not knowing just one, but knowing which one to pick for the job.
Thank you for reading!
If you found it valuable, hit a like ❤️ and consider subscribing for more such content.
If you have any questions or suggestions, leave a comment.
P.S. If you’re enjoying this newsletter and want to get even more value, consider becoming a paid subscriber.
As a paid subscriber, you’ll unlock all premium articles and gain full access to all premium courses on algomaster.io.
There are group discounts, gift options, and referral bonuses available.
Checkout my Youtube channel for more in-depth content.
Follow me on LinkedIn, X and Medium to stay updated.
Checkout my GitHub repositories for free interview preparation resources.
I hope you have a lovely day!
See you soon,
Ashish
Great article Ashish, thank you for shedding more light on the different API design style/paradigms