Treblle Blog

AI-first API design puts intelligent systems—not humans—at the center of how APIs are built. In this article, we explore what that means, why it matters, and how developers can start designing APIs that speak AI.

Traditional APIs like REST and GraphQL have powered integrations for years, but modern systems demand more flexibility. This article explores how MCP compares to traditional APIs—covering use cases, pros, cons, and how to choose the right fit.

Machine learning APIs make it easy to add AI capabilities like speech recognition, image analysis, and predictive insights to applications—without building models from scratch. In this guide, we’ll explore the best ML APIs, their benefits, and how they simplify AI integration for developers.

Connecting AI models to data sources is tough—custom integrations are time-consuming. Model Context Protocol (MCP) simplifies this by standardizing how models interact with tools. This guide explores how MCP works and why it could be a game-changer for developers.

AI APIs promise powerful capabilities, but their documentation rarely tells the full story. Security gaps, performance issues, and design flaws can surface in production, causing costly setbacks. We put top AI APIs to the test with API Insights to uncover real-world insights—here’s what we found.