Beyond Heavy API Keys: How I Built a Zero-Cost Asynchronous AI Summarizer in Spring Boot 3.4

When building cloud services around Generative AI, developers often face two major challenges:

High Latency & Thread Exhaustion: Downstream AI model calls take seconds to stream back tokens, pinning standard OS server threads in I/O wait states.

Third-Party API Overhead: Relying on official data APIs (like the YouTube Data API v3) introduces unnecessary API key rotation, cost, and rate-limiting friction.

To address this, I built EchoEngine—a lightweight, high-concurrency Spring Boot 3.4 application designed to ingest YouTube URLs, scrape publicly available metadata cleanly without YouTube API keys, and generate 3-bullet-point technical summaries using Google’s Gemini 2.5 Flash model.

In this article, I will break down the system architecture, how Java 21 Virtual Threads (Project Loom) kept the application non-blocking, and the real-world GCP API quota traps I ran into while building it.