Inside the Tech Stack of Million-Dollar AI Startups: 7 Open-Source Libraries

Author @eyishazyer points out that hundred-million-dollar AI startups don't possess a secret recipe; their underlying tech stacks are actually assembled from 7 core open-source libraries. This standardized combination explains how current AI-native applications achieve efficient responses and large-scale deployment.

Confirmed

• Core Components: This tech stack primarily consists of open-source tools including FastAPI, PostgreSQL, LlamaIndex, vLLM, and LangChain.
• FastAPI: The Python framework used by most AI backends today. Thanks to its speed and asynchronous request handling, it is highly suitable for scenarios waiting for model responses.
• PostgreSQL: Despite being decades old, its reliability and proven track record make it the go-to database for most AI-native companies to actually store data.
• LlamaIndex: Acting as the data connection layer, it transforms data like PDFs, Notion documents, and databases into searchable and reason-able formats for models, serving as the underlying mechanism for various "chat with documents" products.
• vLLM: As an inference engine, it enables each GPU to handle more requests through smarter memory management, making large-scale model production deployments cheaper and more efficient, which is a key factor determining AI application response speed.
• LangChain: The foundational framework for almost all AI Agent startups, responsible for handling complex logic like prompt chaining, external tool calling, and cross-conversation memory.

Why It Matters

This series of posts reveals the current technological landscape of the AI startup space: seemingly cutting-edge AI-native products still rely heavily on mature open-source tools and traditional database technologies for their underlying infrastructure. This not only lowers the technical barrier to entry for AI startups but also indicates that the industry's core competition is shifting from building underlying infrastructure to productizing applications and optimizing user experiences based on general-purpose open-source components.

2026-08-07 ~ 2026-08-07 · 6 related posts

• [source] $100M AI Startups Have No Secret Sauce: They Glue Together 7 Open-Source Repos — eyishazyer · 2026-08-07
• LangChain: The Framework Behind Most AI Agent Startups — eyishazyer · 2026-08-07
• vLLM: The Serving Engine Making LLM Deployment Affordable — eyishazyer · 2026-08-07
• LlamaIndex: The Data-Connection Layer for AI Apps — eyishazyer · 2026-08-07
• FastAPI: The Python Framework Powering Most AI Backends — eyishazyer · 2026-08-07
• PostgreSQL: The Battle-Tested Database Behind AI-Native Companies — eyishazyer · 2026-08-07