REST vs GraphQL vs OData — Choosing the Right API for the Right Job

REST vs GraphQL vs OData — Choosing the Right API for the Right Job

“Not every app needs GraphQL. Not every enterprise loves OData. And REST isn’t dying anytime soon.”

When building APIs, we often face a critical decision early on: REST , GraphQL , or OData?

Each shines in different scenarios — and each can become a bottleneck if misused.

In this post, we’ll break down:

• ✅ What REST, GraphQL, and OData really are
• 🧠 When to use each with real-world project examples
• ⚖️ Pros and cons of each style
• 🛠️ How to migrate between them (if you must)

🔹 1. REST — The Veteran That Still Works

Best when:

You need a simple, resource-oriented API with predictable endpoints and strong HTTP semantics.

📦 Real Project: A Marketplace API (like Etsy or Flipkart)

Imagine you’re building an API for a marketplace:

GET /products  
GET /products/:id  
POST /orders  
PUT /users/:id

This works great for:

• Mobile and web clients consuming standard resource collections
• Public APIs exposed to third-party developers
• Systems with caching requirements

✅ Pros:

• Simple to understand and implement
• Easy to cache with HTTP headers
• Works with any client/tool (browsers, Postman, curl, etc.)
• Massive ecosystem support

❌ Cons:

• Overfetching/underfetching data
• Multiple calls to build complex UI views
• Versioning can become messy (/v1/, /v2/)

🔧 Example: Fetching user orders

GET /users/123/orders

But now you also need product thumbnails and delivery status — either you call two more APIs or create a new endpoint. This is where REST becomes rigid.

🔹 2. GraphQL — The Frontend Developer’s Dream

Best when:

You need flexible data fetching, rapid UI iterations, or multiple nested resources in one go.

📱 Real Project: Social Networking App (like Twitter or LinkedIn)

Let’s say you’re building a feed screen that shows:

• Post content
• User who posted it
• Comments (with commenter details)
• Like counts

With REST, you’d make 3–4 calls.

With GraphQL, one query does it all:

{
  feed {
    post
    user { name avatar }
    comments {
      text
      user { name }
    }
  }
}

✅ Pros:

• Clients fetch exactly what they need
• Multiple resources in one request
• Great introspection, schema validation, and type safety
• Subscriptions for real-time updates

❌ Cons:

• Caching is more complex (no native HTTP caching)
• Rate limiting and query complexity need to be enforced manually
• Harder for teams unfamiliar with schema-first development

⚠️ Real Pain Point:

If your API is public or used by many third-party clients, GraphQL may be overly flexible , increasing attack surface or server load.

🔹 3. OData — The Enterprise Powerhouse

Best when:

You’re building internal or enterprise-grade apps with rich relational data that needs to plug into tools like Excel , Power BI , or SAP.

🏢 Real Project: Employee Analytics Portal (like HR dashboards in Microsoft Dynamics)

Use cases:

• Fetch top 10 highest-paid employees over age 40
• Export filtered data to Excel or Power BI
• Drill down on KPIs without writing backend logic

OData lets you do this directly:

GET /Employees?$filter=Age gt 40&$orderby=Salary desc&$top=10

✅ Pros:

• Built-in filtering, sorting, pagination, and projection
• Excellent integration with Microsoft/BI tools
• Schema metadata is auto-generated
• Great for low-code tools and reporting systems

❌ Cons:

• Steeper learning curve
• Verbose URLs
• Less flexible for custom business logic
• Not widely adopted outside Microsoft ecosystem

⚔️ Side-by-Side Comparison

🧠 TL;DR — Choose Based on the Job

✍️ Final Thoughts

You don’t have to choose one forever. Many modern platforms use a hybrid approach:

• REST for external integrations
• GraphQL for frontend customization
• OData for reporting dashboards

The key is to pick what’s right for your clients and team  — not what’s trending.

💬 Over to You

What API architecture is your team using — and why?

Have you hit performance bottlenecks or scalability walls?

Let’s chat in the comments 👇