Building a semantic search system for a few thousand documents is relatively straightforward. You generate embeddings, store them in a vector database, and run similarity search to retrieve the most relevant results.

But things start to change when your dataset grows.

What works well for 10,000 vectors may struggle with 10 million. Queries become slower, memory usage increases, and the cost of storing and searching high-dimensional vectors rises quickly. At large scale, even small inefficiencies in indexing or retrieval can have a big impact on performance and cost.

This is where scaling techniques come into play.