7 Alternatives to REST APIs

REST emphasizes resource-based design using URIs, enabling flexible modifications without breaking clients.

However, alternatives exist for specific needs:

1. Evolving complexity. REST APIs are designed to overcome the complexity of earlier API protocols like SOAP, but they can become challenging to maintain as the number of endpoints and resources increases. This can make it difficult for developers to understand and modify the API over time.
2. Performance. REST APIs are scalable and can handle many requests. However, there may be better choices for real-time or low-latency applications, as they rely on multiple round-trip requests to retrieve data and might require optimizations like HTTP/2, server-sent events (SSE), or caching strategies that mitigate these issues
3. Evolving data requirements. REST APIs may require significant changes to support new use cases or data structures, leading to versioning and compatibility issues and increasing complexity and development time.
4. Specific use case requirements. There are specific use cases, such as real-time data streaming or low-power Internet of Things (IoT) devices (as mentioned above), where other protocols may be better suited than REST.
5. Developer preference. Developers may prefer using alternative protocols because they are more familiar with them or offer specific features or benefits that REST does not provide.

Alternatives to REST APIs

Here are the seven REST alternatives you need to know:

GraphQL

GraphQL enables efficient, flexible data fetching by allowing clients to request exactly what they need in a single query, reducing over-fetching and multiple API calls. Unlike REST’s fixed endpoints, GraphQL uses a single schema-driven entry point, making it ideal for complex, evolving data models and mobile apps with limited bandwidth.

In an e-commerce platform, GraphQL retrieves products, reviews, and recommendations in one request. A social media app, like those Meta originally developed it for, efficiently loads posts, comments, and reactions tailored to a user’s feed. However, its flexibility requires safeguards—query depth limits, rate limiting, and caching (e.g., persisted queries, Apollo Federation) to prevent performance issues.

Security is more complex than REST, as deeply nested queries can overload servers. While GraphQL excels at dynamic, real-time UI updates, REST remains preferable for cache-heavy public APIs or strict access control. Many production systems use both—GraphQL for flexible client-driven queries and REST for authentication and standardized external endpoints.

gRPC

gRPC is a high-performance RPC framework using Protocol Buffers (Protobuf) and HTTP/2 for compact, low-latency communication. Unlike REST’s text-based JSON over HTTP/1.1, gRPC supports multiplexed streams, bidirectional messaging, and automatic client/server code generation, making it ideal for microservices, real-time systems, and IoT.

A financial system benefits from gRPC’s millisecond response times for transaction processing, while high-frequency trading platforms use its bidirectional streaming for live market updates. However, gRPC lacks native browser support and is less suitable for large-scale data processing. Debugging is harder due to binary serialization, and proxies like Envoy or gRPC-Web are needed for frontend compatibility.

In contrast, PubNub is optimized for broadcasting real-time messages to massive audiences, making it a better choice for large-scale notifications or live chat. Production deployments of gRPC require service discovery (Consul, etcd) and observability tools (OpenTelemetry). Security is enforced via TLS, mTLS, or OAuth. Many architectures combine gRPC for backend microservices with REST for public APIs and broader client support.

WebSockets

WebSocket protocol establishes a persistent TCP connection, enabling real-time data exchange without repeated HTTP requests. This reduces latency and bandwidth usage, making them ideal for high-frequency updates. Unlike REST, which requires polling or long polling to simulate real-time behavior, WebSockets allow instant bidirectional communication.

In a chat application, WebSockets handle message delivery, typing indicators, and presence updates, while REST manages user authentication, profile data, and message history retrieval. For financial trading platforms, WebSockets stream live market data, ensuring traders receive updates in milliseconds, while REST serves historical trade data and account management.

However, WebSockets may not be the best choice for applications that require complex data manipulation or for applications where scalability is a concern. Requirng load balancers that support sticky sessions or a distributed event system like Redis Pub/Sub or Kafka. Without these, connections can become bottlenecks as user volume grows. A multiplayer game uses WebSockets to synchronize player positions in real-time, but REST still facilitates game state storage, matchmaking, and user account management.

Security is critical, as WebSockets lack built-in request validation per message. Authentication via JWT or OAuth happens at the initial handshake, followed by token verification on every event. Implementing rate limiting and origin validation helps mitigate abuse, ensuring stability in production environments.

WebSockets do not replace REST but work alongside it. A collaborative document editor like Google Docs uses WebSockets for real-time cursor updates and edits, while REST handles document loading, saving, and access control. This hybrid approach ensures real-time responsiveness without sacrificing reliability and data integrity.

MQTT

MQTT (Message Queuing Telemetry Transport) is a lightweight, open-source messaging protocol designed for IoT devices, operating on a publish/subscribe model. 

MQTT is a strong alternative to REST for IoT and real-time messaging applications, especially when low bandwidth, low power consumption, and reliability are essential. However, for complex data manipulation or interactions, REST remains more appropriate.

Key Technical Considerations:

• Publish/Subscribe vs. Request/Response: MQTT uses a pub/sub model, decoupling message producers and consumers through a broker. This contrasts with REST’s request/response model, where clients must wait for a server's response. MQTT is more efficient for environments with many devices or when real-time communication is needed.
• Low Bandwidth and Small Packet Size: MQTT’s lightweight messages and low bandwidth usage make it ideal for constrained networks or devices with limited resources, unlike REST, which often incurs higher overhead per HTTP request.
• Quality of Service (QoS): MQTT supports QoS levels (0, 1, and 2) to ensure reliable message delivery, even with network disruptions. REST doesn’t provide inherent message delivery guarantees, requiring external mechanisms for reliability.
• Battery Life and Connectivity: MQTT is optimized for battery-powered devices by allowing them to “sleep” between messages. It handles intermittent connectivity well, with devices sending data when possible. REST APIs, in contrast, require a persistent connection for each request, draining battery life.
• Not for Complex Data Operations: MQTT is best for simple data transmission and real-time messaging but isn’t suited for complex interactions or data manipulation like REST APIs, which are designed for CRUD operations and intricate business logic.

MQTT vs. WebSockets

WebSockets allow peer-to-peer communication and are ideal for interactive applications, requiring persistent connections. MQTT, however, uses a broker for scalable, fault-tolerant communication, making it more efficient for large IoT deployments with many devices.

Event-Driven Architecture (EDA)

EDA (Event-Driven Architecture) shifts away from the traditional request/response model of REST by organizing communication around events that trigger actions across the system. In an EDA system, events are messages or state changes (e.g., "order placed," "payment received") that notify various components or microservices to react accordingly. This model allows components to operate in a loosely coupled manner, ensuring that changes in one service don't directly impact others, enhancing both scalability and resilience.

Key Advantages of EDA in Production-Ready Systems:

• Real-Time and Reactive Data Processing: In EDA, services respond in near real-time to events, enabling immediate processing as opposed to waiting for periodic polling or multiple HTTP requests in REST. For example, when an event like "user registration" is triggered, the system immediately reacts by sending a welcome email or logging the action, without waiting for scheduled queries or background tasks.
• Reduced Polling and Latency: REST APIs often require frequent polling, especially in real-time applications (e.g., chat, notifications, or live tracking). Each poll to a REST endpoint incurs network overhead and adds latency. EDA avoids this by ensuring that services subscribe to relevant events, eliminating the need for constant checks. Instead of querying a server to check for new data (which REST requires), services are simply notified when something changes, significantly reducing resource usage and response time.
• Microservices Scalability: EDA is inherently suited for microservices architectures where each service is isolated, handles specific functionality, and communicates through events. Each microservice can independently produce and consume events without direct dependencies on other services, ensuring that scaling one service (e.g., an authentication service) doesn't impact others (e.g., payment processing). This enables elastic scalability, where each microservice can be scaled based on event traffic rather than the fixed request/response overhead that REST often requires.
• Decoupling Services: Unlike REST, where services typically need to know the structure of each endpoint and maintain tight coupling, EDA fosters loose coupling. Services can subscribe to events without knowing who will trigger them or what the full processing flow will be, making the system more flexible and resilient to changes. For instance, the event "order shipped" can trigger multiple downstream processes (inventory updates, email notifications) without each service knowing the exact implementation details of others.

EDA vs. REST & GraphQL

REST’s Rigid Endpoint Structure: REST APIs require predefined endpoints (e.g., /api/orders/{id}) that clients interact with, and each endpoint often corresponds to a specific action (GET, POST, DELETE). This rigid structure can be cumbersome when trying to handle complex interactions across a distributed system, as every new requirement might necessitate adding new endpoints. In contrast, GraphQL provides a flexible query interface, where clients can request exactly the data they need from a single endpoint, without the constraints of fixed URLs for each resource. However, GraphQL still requires direct communication between the client and server.

EDA's Decoupling: Unlike REST and GraphQL, EDA ensures that there is no direct client-to-service communication. Events decouple the producer and consumer of the data, which allows systems to handle complex workflows asynchronously. For example, an "order processed" event might trigger several reactions in various services (inventory, shipping, and payment) without each component needing to know the details about other services.

FALCOR

Companies can also drive innovation in development. FALCOR is a JavaScript library developed by Netflix for building efficient and flexible APIs. It uses a "path-based" approach to data retrieval, representing data as a graph of interconnected paths rather than individual resources accessed through HTTP requests. It offers benefits like:

• WebSocket support, which allows real-time data updates to be pushed to clients without requiring repeated polling
• Declarative data fetching, where the client specifies the data it needs and the API responds with the requested data. This simplifies the client-side code and reduces the amount of data sent over the network.

The name "FALCOR" does not stand for anything. It was chosen by the developers at Netflix to represent the library's approach to data retrieval. It's inspired by the character of the same name from the 1980s movie "The NeverEnding Story," a dragon-like creature that can travel through different dimensions and worlds, much like FALCOR's ability to navigate complex data graphs.

Functions

PubNub’s Functions are JavaScript event handlers that can be executed on in-transit PubNub messages or in the request/response style of a RESTful API over HTTPS. For those who are familiar with Node.js and Express, the On Request event handler development is very similar. Functions can deploy new or additional real-time functionality like chat moderation and language translation.

Run your code within our network or leverage our existing integrations to transform, re-route, augment, filter and aggregate messages to detect and block spammers, measure averages, and more. All code is run at the edge for low latency and is robust enough that you don’t need to build your own infrastructure.

Facilitating innovation one REST alternative at a time

REST alternatives are gaining popularity due to their ability to solve challenges REST APIs face. Each alternative has its own unique strengths and use cases. When choosing a REST alternative, it's essential to consider the specific needs and requirements of the application — whether its scale for live events or real-time capabilities for Web3 — as well as the strengths and limitations of each REST alternative. 

Ultimately, by exploring these alternatives, developers can build more efficient and effective APIs that meet the needs of modern applications.

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