Question 1

What’s the difference between embeddings and vector databases?

Accepted Answer

Embeddings are the vectors (numbers) produced by a model to represent meaning. A vector database (or vector index) is the system that stores those vectors and lets you quickly search for “nearest” vectors (most similar items). You often use both: generate embeddings with a model, then store/search them in a vector database.

Question 2

When should I use embeddings?

Accepted Answer

Use embeddings when you need meaning-based matching instead of exact keyword matching. Common cases include semantic search, retrieval-augmented generation (RAG) for chatbots over your documents, recommendations (“items like this”), deduplication/near-duplicate detection, clustering topics, and similarity-based classification.

Question 3

How much do embeddings cost?

Accepted Answer

Costs usually come from (1) generating embeddings (priced by input size, often per token for text or per image) and (2) storing/querying vectors (database/index compute, storage, and read/write operations). Total cost depends on how many items you embed, how often you re-embed (e.g., when content changes), vector dimension size, and query volume/latency requirements.

Embeddings

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