When is GraphQL a better choice than REST?

GraphQL fits best when you control the clients, those clients need nested or varied data across many screens, and you want to evolve the API without versioning endpoints. REST is usually better for simple public APIs, file-heavy workloads or cases where HTTP caching matters most. We’ll recommend one based on your actual apps.

Can GraphQL sit on top of our existing REST APIs?

Yes. A GraphQL layer can call your existing REST services, databases and third-party APIs behind the scenes, giving client apps a single, consistent graph. It’s a practical way to modernize the front-end experience without rewriting every back-end service at once, and you can migrate pieces behind it over time.

It can be, with the right controls. We enforce authentication on every request, authorization at the field and type level, depth and complexity limits, and persisted or allow-listed queries for production apps. We also disable introspection in public environments where it would expose more of the schema than you want.

What drives the cost of a GraphQL project?

The size of the schema, the number of data sources behind it, authorization complexity, real-time needs such as subscriptions, and whether federation across services is involved. We work through those in discovery and give you a fixed written quote, so you know the full cost before development starts.

How do you handle caching with GraphQL?

Differently from REST, since most requests go to one URL. On the client, normalized caches in Apollo or Relay store objects by ID so screens share data. On the server, we cache at the resolver or data-loader level and can add response caching for persisted queries. For public, read-heavy data, persisted queries over GET also let a CDN cache responses.

Should we write the schema first or generate it from code?

Both work. Schema-first suits teams that want product and front-end developers reviewing the contract in plain SDL before implementation. Code-first, with tools like Pothos or Nest, suits TypeScript-heavy teams that want types and resolvers kept together. We pick with you based on who reviews changes and how your team already works.

Cloud, API & Platform Engineering · GraphQL

GraphQL schemas built for how your apps read data

We design GraphQL schemas and servers that let web and mobile clients request exactly the data each screen needs in one round trip — with batching, query limits and caching that keep the server fast and safe.

Client-driven schema design
Batched data loading
Query cost limits

Start your project How we work

Overview

Running GraphQL well, not just running it

GraphQL moves a decision from the server to the client: instead of the back end deciding what each endpoint returns, every screen asks for exactly the fields it needs. That is powerful for products with many views over related data, and it changes where the hard problems sit. Authorization must be enforced per field and per object, caching cannot lean on simple URLs, and query cost has to be measured rather than assumed.

The decisions a buyer faces are mostly about scale and ownership. One schema owned by one team is simple; several teams contributing to one graph needs federation and schema governance. Code-first schemas suit fast-moving TypeScript teams; schema-first suits teams that want the contract reviewed before code. Public GraphQL APIs need stricter limits than private ones, often with persisted queries only. We help you choose based on your team, not on fashion.

Healthy GraphQL looks like a schema that reads like your product’s vocabulary, resolvers that stay fast as screens multiply, and schema changes checked against real client usage before they ship. Front-end developers get typed data without waiting on back-end changes, and back-end developers can see exactly which fields are used, by which clients, and how much each query costs to serve.

Who it’s for

Built for teams like yours

01
Multi-screen product teams
Companies with web and mobile apps showing many different views of related data, where REST endpoints keep multiplying or screens need several calls to render.
02
Teams unifying several back ends
Organizations with data spread across services or legacy APIs that want one coherent graph for front-end developers to query, without rewriting what sits underneath. Each source keeps its own owner.
03
Existing GraphQL users
Teams already running GraphQL that are fighting slow resolvers, unclear authorization or schema sprawl and want an experienced review and a cleanup plan. We work within your current schema wherever possible.

Why it matters

Flexible for clients, disciplined on the server

GraphQL shines when many screens need different slices of related data, and a REST API would mean dozens of endpoints or chatty requests. That flexibility has a cost: careless resolvers trigger N+1 database queries, and open-ended queries can overload a server. We design the schema around client use cases and put guardrails in place from the start.

We work with Apollo Server and Client, GraphQL Yoga, Hasura and Relay-style pagination conventions. We’ll also tell you honestly when plain REST would serve you better, because GraphQL is a tool for specific problems, not a default.

Every engagement includes

Schema workshopwe map screens and use cases to types, queries and mutations with your product team.
Schema & server buildresolvers, validation and authorization rules implemented and peer-reviewed.
Performance safeguardsbatching, caching, depth limits and query cost analysis configured before launch.
Client integrationtyped queries and generated hooks for your React, iOS or Android apps.
Schema checksautomated tests that flag breaking schema changes before they reach production.
Docs & handoverschema documentation, an explorer for developers and source code in your repository.

Features

GraphQL without the pitfalls

01
Client-driven schema design
Types, fields and mutations modeled on real screens and use cases, not mirrored straight from database tables.
02
Batched data loading
DataLoader-style batching and caching that remove N+1 queries and keep resolvers efficient under real traffic.
03
Query cost limits
Depth limits, complexity scoring and persisted queries that stop expensive or abusive requests before they run.
04
Federation for growing teams
Apollo Federation or schema stitching so separate services can each own part of one unified graph.
05
Real-time subscriptions
Live updates over WebSockets for chat, dashboards, notifications and other screens that must refresh instantly.
06
Typed client code
Code generation that gives front-end teams type-safe queries and catches schema mismatches at build time.

In practice

Problems GraphQL is good at

Rich dashboards and feeds
Screens combining users, activity, metrics and permissions in one request, so the client fetches everything a view needs without a waterfall of dependent calls. Adding a widget means adding fields to a query, not a new endpoint.
Mobile apps on slow networks
Mobile screens request only the fields they display, cutting payload size and round trips for users on weak connections or metered data plans. Older app versions keep working because fields are deprecated rather than removed.
A graph over existing services
A GraphQL layer that stitches together existing REST services and databases, giving front-end teams one schema while back-end systems keep running as they are. Underlying services can be modernized later without changing client queries.
Live collaboration features
Subscriptions powering comments, presence, notifications or status changes that appear instantly for every user viewing the same record or workspace. Authorization is checked on every event, so users only receive updates for records they are allowed to see.

Process

How we work

1
Screen inventory
We gather the screens and workflows the graph must serve, noting the data each needs, how often it changes and which users may see which fields. This list becomes the test for every schema decision.
2
Schema design
Types, connections and mutations are drafted in your product’s language with naming and nullability conventions, then reviewed by front-end and back-end developers together. Agreed conventions are written into a short guide for future contributors.
3
Resolver build
Resolvers are implemented with batched data loading, field-level authorization and error conventions, and profiled against realistic queries to catch N+1 patterns early. Every resolver is covered by tests for permissions and expected data.
4
Guardrails
We add depth and cost limits, persisted or allow-listed queries where appropriate, response caching and tracing per field, so expensive queries are visible and controllable. Limits are tuned from real traffic after launch.
5
Usage-aware evolution
Field usage is tracked so deprecations are based on real client traffic, and schema checks in CI flag breaking changes before merge. Removing a field becomes a measured decision rather than a guess about who might break.

Deliverables

What you receive

Schema design guide and naming conventions
Annotated schema with field-level authorization rules
Resolver performance profile on representative queries
Query cost and depth limit configuration
Persisted query or allow-list setup
Field usage tracking and deprecation reports
Generated typed hooks for client apps

Tools & methods

Servers & gateways

Apollo Server
GraphQL Yoga
Apollo Router
Hasura
Pothos
Strawberry

Clients & codegen

Apollo Client
Relay
urql
GraphQL Code Generator
Apollo iOS
Apollo Kotlin

Methods

DataLoader batching
Persisted queries
Schema linting
Field-level tracing

FAQ

Frequently asked questions

Anything else about GraphQL? Ask us directly.

GraphQL fits best when you control the clients, those clients need nested or varied data across many screens, and you want to evolve the API without versioning endpoints. REST is usually better for simple public APIs, file-heavy workloads or cases where HTTP caching matters most. We’ll recommend one based on your actual apps.

Let’s work together

Have a project in mind?

Book a strategy call and we’ll show you exactly how to turn your goals into a system that generates consistent results.

Book a strategy call All services

GraphQL schemas built for how your apps read data

Client-driven schema design

Batched data loading

Query cost limits

Federation for growing teams

Real-time subscriptions

Typed client code

Rich dashboards and feeds

Mobile apps on slow networks

A graph over existing services

Live collaboration features

Screen inventory

Schema design

Resolver build

Guardrails

Usage-aware evolution

01When is GraphQL a better choice than REST?

02Can GraphQL sit on top of our existing REST APIs?

03Is GraphQL secure?

04What drives the cost of a GraphQL project?

05How do you handle caching with GraphQL?

06Should we write the schema first or generate it from code?

Have a project in mind?