Clean Specs Beat New Standards: Iddo Gino on APIs, MCPs, and Smarter AI

In this Commit & Push episode, host Damien Filiatrault sits down with Iddo Gino, founder of RapidAPI (acquired by Nokia) and now CEO of Datawizz, to talk about the real blockers to AI integrations, why he’s skeptical of MCPs, and how smaller, specialized models can slash AI costs while improving results.

From “Awesome APIs” to RapidAPI, and Beyond

As a teen in Israel, Iddo learned by building: Flash games, early websites, and, eventually, a GitHub repo called Awesome APIs. That list evolved into RapidAPI—an interactive marketplace where developers could explore, test, and adopt APIs. The company reached unicorn status, scaled into the enterprise, and was later acquired by Nokia. Iddo’s reflection: the developer platform—a simple, open marketplace for publishing and consuming APIs—risked getting overshadowed by larger enterprise ambitions.

The MCP Debate: Don’t Reinvent What Documentation Can Fix

Iddo’s hot take: MCPs look tidy today because they’re new—not because they’re inherently better.

• Early OpenAI plugins hinted at the right idea (models calling APIs via specs) but stumbled due to weaker models at the time and, more importantly, messy, drifting API docs.
• MCPs offer a “clean slate,” but that means duplicating the entire ecosystem—gateways, auth, tooling, governance—and risking the same decay over time.
• The pragmatic path: keep REST (or GraphQL), fix your specs, and maintain documentation so both humans and LLMs can integrate reliably.

Datawizz: Smaller Models, Bigger Wins

Datawizz helps teams replace generic LLM calls with tiny, purpose-built models—and route traffic intelligently:

• Router as an OpenAI-compatible endpoint: drop-in URL swap.
• Starts with your existing LLM flow, clusters real usage, then trains narrow models that excel at specific, high-volume tasks.
• Routes requests to the best fit: a specialized model when confident, or your preferred LLM for novel prompts.
• Typical impact: ~85–95% cost reduction versus sending everything to a large, general model.