What a Good API Design Looks Like (And Why Most Are Overcomplicated)

Daniel Gorlovetsky
July 8, 2025

APIs are supposed to simplify things. But most of the ones we see in the wild do the opposite.

At TLVTech, we’ve worked on backend systems across dozens of startups—from quick MVPs to enterprise-scale platforms. And if there’s one thing we consistently fix, it’s overcomplicated, unclear, or inconsistent API design.

A good API doesn’t just “work.” It’s obvious. It’s boring. It’s predictable.

Here’s what a clean API looks like—and why so many teams get it wrong.

1. Clear Naming Is 80% of the Work

You don’t need to read a spec to understand what POST /users/123/reset-password does. But POST /core/actions/trigger? That’s a guessing game.

Good APIs:

  • Use nouns for resources (/users, /projects, /sessions)
  • Use verbs for actions that aren’t CRUD (/users/123/reset-password)
  • Avoid abbreviations unless they’re industry-standard
  • Prefer consistency over cleverness

If you need a wiki to explain your endpoints, your naming is broken.

2. Stick to the HTTP Basics

Too many teams try to outsmart HTTP. Don’t.

Use the standard verbs:

  • GET to fetch
  • POST to create
  • PUT or PATCH to update
  • DELETE to remove

No need for POST /getData or GET /createThing. HTTP already gives you semantics—use them.

3. Avoid Deep Nesting

GET /users/123/projects/456/comments/789/tasks
This kind of nesting looks structured—but quickly becomes unreadable, hard to test, and painful to maintain.

Unless the hierarchy truly matters, flatten it:

  • Use query params or filters
  • Link resources using IDs, not paths

4. Think in Use Cases, Not Just Data Models

A common mistake: designing APIs like direct database wrappers.

Yes, the data model matters—but the API should reflect how the frontend or client uses the data.

Example:
Instead of forcing 3 requests to get user info, preferences, and subscriptions—offer a GET /users/123/dashboard endpoint that returns everything the UI needs in one go.

Backend is for composition. API is for usability.

5. Versioning and Stability Are Not Optional

Don't break clients with unannounced changes.

  • Use versioning (/v1/users)
  • Deprecate gradually
  • Keep old versions stable unless there’s a critical issue

If your API changes weekly, no one will trust it long-term.

Final Word: Simplicity Wins

The best APIs feel invisible. They just make sense. No surprise responses, no guessing game with endpoints, no need to dig through docs just to get started.

At TLVTech, we treat APIs like products—because every time someone calls your API, it is part of the product.

If you're building something and want your backend to scale without chaos, let’s talk.

Daniel Gorlovetsky
July 8, 2025

Related Articles

The Stack We Use for Fast Backend Delivery (and Why It Works)

We use a battle-tested backend stack—Node.js, NestJS, Postgres, Docker, and GitHub Actions—that helps startups ship fast, stay stable, and scale without technical debt.

Read blog post

Do Startups Really Need a CTO?

Startups need smart tech leadership, but a full-time CTO isn’t always the right move. A Fractional CTO gives you the expertise to scale, build, and fundraise—without the full-time cost or commitment. Get the right tech strategy at the right time. Here’s how.

Read blog post
Are Machine Learning Algorithms Transforming Tech?

Are Machine Learning Algorithms Transforming Tech?

- Machine learning includes three types of algorithm: supervised, semi-supervised, and unsupervised learning. Supervised is guided learning using labeled data, unsupervised finds patterns in unlabeled data without guidance, and semi-supervised uses both to learn and train. - Four groups of machine learning algorithms are: classification and regression (predictive sorters), and clustering and association (find patterns and associations). - Benefits of machine learning algorithms include decoding patterns, solving problems with minimal human intervention, uncovering unknown insights, predicting trends, automating tasks, and improving security. - To implement machine learning models, we need to gather and clean data, understand the data, select a model, train and test the model, tweak the model, and integrate it into existing systems. - Machine learning models include neural networks, regression techniques, decision trees, and support vector machines. - Future trends in machine learning involves advanced algorithms, improved cybersecurity, scaling of algorithms, and continuous research and development.

Read blog post

Contact us

Contact us today to learn more about how our Project based service might assist you in achieving your technology goals.

Thank you for leaving your details

Skip the line and schedule a meeting directly with our CEO
Free consultation call with our CEO
Oops! Something went wrong while submitting the form.