Vector Databases Explained: Design Choices and Trade-Offs
In this video, we break down how vector databases are used in real production systems, and the key design decisions you need to make when building semantic search and recommendation engines.
Using a real system built at Ready Tensor as a case study, we walk through common vector database use cases, compare popular database options, and explain the practical trade-offs behind each architectural choice.
You'll learn how to:
* Understand the core use cases for vector databases: semantic search and recommendations
* Compare popular vector DB options like PGVector, Chroma, FAISS, Milvus, and Pin…
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Chapters (8)
Why vector databases matter in agentic AI systems
0:40
Core use cases: semantic search and recommendations
1:17
Choosing a vector database: key questions and trade-offs
3:55
Embedding model decisions: open source vs APIs
6:03
Chunking strategies and when they matter
7:34
Similarity metrics and why cosine similarity is common
8:19
System architecture overview
8:45
Live demo: semantic search and recommendations in production
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