Experimentation APIs: REST and GraphQL

If you've ever shipped an experiment that crashed because your API couldn't handle the data load, you know the pain of choosing the wrong tool for the job. The debate between REST and GraphQL for experimentation APIs isn't just academic - it directly impacts whether your tests run smoothly or leave you debugging at 2 AM.

Here's the thing: both REST and GraphQL can power successful experiments, but they solve different problems. Let's dig into when each approach makes sense and how to avoid the common pitfalls that trip up even experienced teams.

Introduction to experimentation APIs: REST and GraphQL

Let's start with the basics. APIs are how your experiments talk to your servers - they're the client-server communication layer that makes everything work. REST and GraphQL represent two fundamentally different approaches to API design, and picking the right one can make or break your experimentation platform.

REST APIs treat everything as a resource. You've got endpoints like /experiments/123 that you hit with standard HTTP methods. It's simple, it works with basically any web infrastructure, and your team probably already knows how to use it. The downside? You often get too much or too little data, which tanks performance when you're running experiments at scale.

GraphQL flips the script. Instead of fixed endpoints, clients ask for exactly what they need - nothing more, nothing less. Want just the variant names and conversion rates from your experiment? GraphQL lets you grab precisely that data in a single request. The strongly-typed schema acts like a contract between your frontend and backend, which means fewer surprises in production.

So how do you choose? Start by looking at your actual needs:

• Simple experiments with predictable data? REST is probably fine

• Complex A/B tests with varying metrics? GraphQL's flexibility will save you headaches

• Team of REST experts? Don't switch to GraphQL just because it's trendy

• Evolving requirements? GraphQL handles change better

The Reddit engineering community has some strong opinions on this choice, and after building experimentation platforms at scale, I've seen both approaches succeed and fail spectacularly.

Comparing REST and GraphQL in experimentation

Here's where REST can hurt your experiments: the dreaded over-fetching problem. Let's say you're testing a new checkout flow. Your REST endpoint returns the entire user object - preferences, history, avatar URL, the works - when all you need is their experiment assignment and conversion status. That's wasted bandwidth multiplied by every user in your test.

Under-fetching is equally painful. Picture this: you need experiment results, user segments, and metric definitions. With REST, that's three separate API calls. Your dashboard loads like it's 1999, and users start complaining about performance. AWS's engineering team documented these exact pain points when they compared the two approaches.

GraphQL solves this elegantly. Your experiment dashboard requests exactly what it needs:

One request, perfect data, happy users.

The versioning story is another key difference. REST APIs typically version everything (/v1/experiments, /v2/experiments), which means maintaining multiple versions in production. GraphQL takes a different approach - you evolve the schema gradually, deprecating fields without breaking existing clients. For experimentation platforms that need to iterate quickly, this flexibility is huge.

But let's be honest: GraphQL isn't always the answer. If your experiments have simple, predictable data needs, REST's simplicity might actually be an advantage. The team at Hygraph ran into this when they evaluated both approaches - sometimes the added complexity of GraphQL just isn't worth it.

Implementing experimentation with REST and GraphQL APIs

When you're building an experimentation API from scratch, your first decision isn't actually REST vs GraphQL - it's understanding what your experiments need. Simple feature flags with on/off states? REST handles that beautifully. Complex multivariate tests with real-time results? That's GraphQL territory.

I've seen teams succeed with REST when they:

• Have predictable experiment structures

• Need rock-solid caching (REST + CDNs = magic)

• Want dead-simple debugging with HTTP status codes

• Already have REST expertise on the team

GraphQL shines when you're dealing with the messy reality of modern experiments. Think about platforms like Facebook or Twitter running thousands of concurrent tests - they need to fetch different data combinations for different experiments without creating a maze of endpoints. That's exactly why social media platforms gravitated toward GraphQL.

The schema stitching capability of GraphQL is particularly powerful for experimentation. You can combine your experiment service, user service, and analytics service into one unified API. Instead of your frontend orchestrating multiple calls, it gets everything in one shot:

That said, REST's ecosystem is massive. You get battle-tested tools, libraries for every language, and developers who already know the patterns. Plus, REST's error handling is more straightforward - a 404 means not found, a 500 means something broke. GraphQL errors require more nuanced handling since everything returns a 200 OK.

The real key? Pick based on your actual constraints, not what's trendy. If you're building for a platform like Statsig where experiments can have wildly different data needs, GraphQL's flexibility pays dividends. But if you're running simple split tests, REST's simplicity might be exactly what you need.

Optimizing API performance for experimental applications

Performance kills more experiments than bad hypotheses. You launch a test, traffic spikes, and suddenly your API is timing out. Let's talk about keeping things fast.

GraphQL's N+1 query problem is real, and it'll bite you hard in production. Imagine fetching experiment results for 100 users - without optimization, that could trigger 100 separate database queries. The fix? Batching and caching, as detailed in Statsig's performance guide. DataLoader patterns group those 100 queries into one efficient database call.

Here's your performance toolkit:

• Client-side caching: Apollo Client can store experiment assignments locally

• Server-side caching: Redis for frequently accessed experiment configs

• Query complexity limits: Prevent clients from requesting your entire database

• Pagination: Don't return 10,000 experiment results at once

The monitoring piece is crucial but often overlooked. Tools like Apollo Studio and OpenTelemetry give you visibility into slow queries before they become incidents. Set up alerts for queries taking over 200ms - your future self will thank you.

Smart API design prevents most performance issues:

1. Keep queries shallow (avoid deeply nested data)

2. Implement field-level permissions (not every client needs every field)

3. Use async resolvers for expensive operations

4. Cache aggressively but invalidate intelligently

Remember: the fastest API call is the one you don't make. Design your experiment APIs to minimize round trips, whether you're using REST or GraphQL.

Closing thoughts

Choosing between REST and GraphQL for your experimentation API isn't about picking the "better" technology - it's about matching the tool to your specific needs. REST's simplicity and mature ecosystem work great for straightforward experiments. GraphQL's flexibility and precise data fetching excel when your experimentation platform needs to scale and evolve quickly.

The best experimenters I know focus on the fundamentals: clean API design, solid performance monitoring, and choosing tools their team can actually maintain. Whether you end up with REST, GraphQL, or some hybrid approach, those principles will serve you well.

Want to dive deeper? Check out:

• The GraphQL documentation for schema design patterns

• Apollo's best practices for production GraphQL

• How platforms like Statsig handle experimentation APIs at scale

Hope you find this useful!