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Vector Databases: from Embeddings to Applications

Reviewed by AI Agent Rank editors · Last verified 2026-05-24

Our take

If you're going to use a vector DB (and in 2026 most AI engineers will), this is the right 90 minutes to spend. Covers embeddings, ANN algorithms, sparse vs dense, hybrid search, and a head-to-head of Pinecone, Weaviate, Chroma and pgvector. Free, vendor-agnostic enough despite the Weaviate teaching credit. Take before you commit to a vector DB.

About the instructor

Sebastian Witalec
Head of Developer Relations, Weaviate

Weaviate DevRel teaching the vector-DB mental model from first principles — what embeddings are, how ANN search works, when each DB choice matters.

Pros

  • +Vendor-agnostic — covers Pinecone, Weaviate, Chroma, pgvector
  • +First-principles approach — you understand WHY, not just HOW
  • +Free, 90 minutes

Cons

  • −Light on production-ops (sharding, backups, hybrid filtering)

Best for

  • · Engineers about to commit to a vector DB choice
  • · PMs scoping a RAG project who need to understand the storage layer

Not ideal for

  • · People wanting a single-vendor deep-dive — see Pinecone Learn or Weaviate Academy
Ready to enroll?

Free on DeepLearning.AI · ~1.5 hours (5 lessons)

Open DeepLearning.AI course →

Alternatives we considered

Other courses on the same topic. The right pick depends on your level and constraints — see each card for the trade-offs.

Vector Databases: from Embeddings to Applications — review (2026) | AI Agent Rank · AI Agent Rank