Markets do not price companies. They price inferences about companies. The distinction matters, because an inference can be directionally correct and numerically reckless at the same time. Micron Technology’s crossing of one trillion dollars in market capitalization this week is a case study in exactly that failure mode: a conclusion that follows from the evidence, pushed well past what the evidence actually supports.

Start with what the data says, stated without editorial softening. Micron’s second fiscal quarter 2026 delivered $23.9 billion in revenue, up 196% year-on-year. Non-GAAP gross margin reached roughly 69%, a level the company had never approached in a prior cycle. High-bandwidth memory — the stacked die architecture that feeds the bandwidth requirements of large-scale AI accelerators — is entirely sold out through calendar 2026 under binding, price-fixed contracts with hyperscalers. Micron has begun volume shipments of HBM4 aligned with Nvidia’s Vera Rubin platform. Management forecasts the HBM total addressable market expanding from approximately $35 billion in 2025 to $100 billion by 2028, a timeline pulled forward by two years from prior estimates. None of that is conjecture. It is contracted, reported, and confirmed by the buyers on the other side of those agreements.

Table of Contents

1. What is a Vector Database?
2. Core Concepts
3. How Vector Search Works
4. Choosing a Vector Database
5. Getting Started
6. Embedding Models
7. Indexing & Storage
8. Querying & Filtering
9. RAG: Retrieval-Augmented Generation
10. Performance Tuning
11. Security Considerations
12. Real-World Examples

What is a Vector Database?

A vector database is a database optimized for storing and searching high-dimensional numerical vectors — called embeddings — that represent the semantic meaning of data (text, images, audio, etc.).