Free Lesson
Scale Your Vector DB to a Million Docs
30 min
Jul 21, 2026 9:30 AM
Virtual (Zoom)
In this video
What you'll learn
Why naive vector search slows down
Watch a query crawl at scale and see exactly which part of the pipeline is the real bottleneck.
Pick the right index for your size
When a flat index is fine and when you need an approximate index like HNSW or IVF to stay fast.
Filter and shard before you search
Use metadata filters and partitioning to cut a million record search down to the slice that matters.
Why this topic matters
A vector search that feels instant on 5,000 documents can crawl once you hit a million. The bottleneck is rarely the model, it is how the index is built and queried. In this free 30 minute session you watch a vector database go from a toy collection to a million record index, and you see the three settings (index type, chunk size, and metadata filters) that decide whether queries stay fast. You leave able to size and tune a vector DB so retrieval stays quick as your data grows.
