Why I Reverted from GraphQL to REST for Croatian API Endpoints

I have been building web backends for Croatian companies since the early REST days. When GraphQL burst onto the scene, I jumped on it like everyone else. For a while, it felt like the perfect solution for our data-heavy dashboards and mobile apps. But after two years of maintaining a GraphQL layer in production for a Croatian e-commerce platform, I made the call to rip it out and go back to REST. Here is exactly why.

The Real Problem Was Not GraphQL Itself

Let me be clear: GraphQL is not bad technology. The problem is that it solves problems most Croatian projects simply do not have. When you are building for a local market of four million people, your biggest bottleneck is rarely “fetching too much data from the frontend.”

Over-Engineering for an Imaginary Scale

I see this pattern constantly in Zagreb and Split tech meetups. A startup builds a GraphQL API because “Facebook uses it.” But Facebook has thousands of microservices and hundreds of frontend teams that need to fetch data independently. Your Croatian travel booking site with three frontend developers does not have that problem. In our case, we ended up with a single GraphQL endpoint that required more server-side complexity than the entire business logic combined.

The real cost was not just development time. It was cognitive load. Every new developer had to learn resolvers, data loaders, and query complexity analysis before they could ship a single endpoint. For a small team working on a Croatian marketplace, that overhead was killing our velocity.

The N+1 Problem That Would Not Die

Everyone promises that GraphQL solves the N+1 query problem. In theory, yes. In practice, with a PostgreSQL backend and a typical Croatian e-commerce schema, we were fighting it constantly. We had categories, products, variants, suppliers, and translations. Every time a frontend developer added a nested field to a query, we had to update the dataloader or risk a performance regression.

I remember one specific sprint where we spent three days optimizing a single GraphQL query for a product listing page. The query looked innocent: fetch 20 products, their primary images, prices, and stock status. Under REST, that would have been two endpoints and a join. Under GraphQL, we had to implement batch loading, cache the results, and still hit the database multiple times because the resolver chain was unpredictable.

REST Gave Us Back Predictability and Debugging Sanity

The moment we switched back to REST, the biggest win was not performance—it was predictability. When something went wrong, I knew exactly which endpoint was called, what parameters it received, and what it returned. GraphQL, with its single endpoint and dynamic queries, turned debugging into a nightmare.

HTTP Caching That Actually Works

Croatian users often browse on slower mobile networks, especially in rural areas. REST endpoints can be cached aggressively with standard HTTP headers. ETags, Cache-Control, and CDN caching all work out of the box. GraphQL breaks this completely. Because every query is different, you cannot cache responses at the HTTP level without complex persisted query schemes.

For our product catalog, which changes prices daily, we set up Varnish caching in front of our REST endpoints. Response times dropped from 300ms to under 10ms for cached content. That same optimization was impossible with GraphQL without building a custom caching layer that no one on our team wanted to maintain.

Simpler Monitoring and Alerting

When you have ten REST endpoints, you can set up simple health checks and latency alerts per endpoint. You know immediately if the /products endpoint is slow. With GraphQL, a single slow resolver can tank the entire API, and you have to dig into tracing data to figure out which field caused it. For a small ops team in Croatia, that extra complexity was not worth it.

We use Prometheus and Grafana for monitoring. Under REST, we had clear dashboards per endpoint. Under GraphQL, we had to instrument every resolver and still could not easily tell if the frontend was requesting too much data or if the backend was just slow. The cognitive overhead of debugging was draining our team.

The Data Fetching Pattern That Actually Fits Croatian Projects

Most Croatian web applications follow a predictable pattern: list pages, detail pages, and form submissions. REST maps to this naturally. A product list endpoint returns products. A product detail endpoint returns one product with all its relations. You do not need a query language for that.

The Mobile App Case

Our mobile app needed to fetch a user’s profile, their recent orders, and a list of favorite products on the home screen. Under GraphQL, that was one query. Under REST, it was three requests. But here is the thing: the mobile app already made three separate network calls for other reasons. Batching them into one GraphQL query saved maybe 100ms of network latency, but it introduced a single point of failure.

When the GraphQL endpoint went down (and it did, because a resolver crashed), the entire home screen was blank. Under REST, if the favorite products endpoint fails, the profile and orders still render. That resilience matters more than eliminating a single round trip.

Authentication and Authorization Simplicity

REST endpoints have clear scopes. You know exactly what data each endpoint returns. Implementing role-based access control is straightforward: check the user’s role, return the data. With GraphQL, authorization becomes a nightmare because you have to check permissions for every resolved field, especially when nested data comes from different domains.

For a Croatian SaaS platform that handles user data and business data separately, this was a dealbreaker. We had to implement field-level authorization in GraphQL, which multiplied our code complexity. REST allowed us to keep authorization logic in the controller layer where it belonged.

The Migraine of Schema Versioning and Breaking Changes

GraphQL promises no versioning, but that is a lie. In practice, deprecating a field in GraphQL is a social process that requires coordination with every frontend team. REST allows you to version your API cleanly with URL paths or headers. When you need to change a response format, you create a new endpoint and keep the old one running until consumers migrate.

In Croatia, many of our clients have legacy mobile apps that update slowly. With REST, we could maintain v1 and v2 endpoints side by side for months. With GraphQL, deprecating a field meant waiting for all clients to stop querying it, which never happened. We ended up with a GraphQL schema that had deprecated fields from two years ago that no one dared to remove.

The Tooling Gap for Small Teams

Yes, GraphQL has amazing tools like Apollo Studio and GraphiQL. But those tools require setup, maintenance, and understanding of GraphQL internals. REST has Postman, cURL, and browser developer tools that every developer already knows. When a junior developer joins your team, they can start hitting REST endpoints in five minutes. With GraphQL, they need to understand query syntax, fragments, and variables first.

For Croatian agencies that hire junior developers fresh out of university, this onboarding friction is real. We lost two weeks of productivity every time a new developer joined because they had to learn the GraphQL layer before they could ship anything.

When I Would Still Use GraphQL (and You Should Too)

I am not saying GraphQL has no place. If you are building a public API that will be consumed by thousands of different clients with wildly different data needs, GraphQL is the right choice. Think of an API like GitHub’s or Shopify’s. But for a typical Croatian web project—an e-commerce site, a booking platform, a SaaS dashboard—REST is almost always the better fit.

The Practical Takeaway

If you are starting a new project in Croatia today, start with REST. Keep your endpoints simple and focused. Use OpenAPI (Swagger) for documentation. Add caching early. Only consider GraphQL if you have concrete evidence that your frontend teams are struggling with over-fetching or under-fetching data across multiple screens. And even then, try batching REST endpoints first.

I learned this the hard way: technology choices should be driven by your actual constraints, not by what is popular on Hacker News. For most Croatian projects, the constraint is team size, not data complexity. REST respects that constraint. GraphQL does not.

The next time someone tells you “you should use GraphQL,” ask them one question: what specific problem in your current API does GraphQL solve that REST cannot? If they cannot answer clearly, you know what to do.