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Vector Database Architecture Comparison

Choosing a vector store means trading off control, convenience, and features. This diagram lays out three popular solutions - FAISS, Pinecone, and Weaviate - as layered architecture stacks, from the application down to the deployment model, followed by a feature comparison matrix.

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Hover over any layer to read what that part of the stack does.

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Overview

Each column is an architecture stack read from top (your application) to bottom (how it is deployed). FAISS is an embedded library you compile into your own program; Pinecone is a fully managed cloud service behind an API; Weaviate is an open-source engine you can self-host or run in the cloud, with hybrid search built in.

How It Works

  • FAISS (blue) - a high-performance library. You call index APIs directly and manage persistence, scaling, and metadata yourself. Native GPU support.
  • Pinecone (green) - a serverless managed service. You talk to a REST/gRPC API and Pinecone auto-scales storage; no infrastructure to run, cloud-only.
  • Weaviate (purple) - an open-source database with a schema, vectorization modules, and an HNSW + BM25 index enabling hybrid keyword-plus-vector search.

Feature Comparison

Feature FAISS Pinecone Weaviate
Deployment Embedded library Fully managed cloud Self-hosted or cloud
Pricing Free (open-source) Usage-based Free (OSS) or managed
Metadata Manual Built-in Built-in with schema
Hybrid search No Limited Yes (BM25 + vector)
GPU support Yes (native) No (abstracted) No (CPU optimized)
Scalability Manual sharding Automatic Manual or managed
Best for Maximum control Fast deployment Hybrid search needs

Lesson Plan

  • Warm up: Ask what "vector database" responsibilities a team takes on when they embed a library versus when they call a managed API.
  • Explore: Hover each layer in all three columns and identify which layers are shared (gray) versus product-specific.
  • Discuss: When is FAISS's manual control worth the extra engineering? When does Weaviate's hybrid search justify self-hosting?
  • Extend: Have students pick a solution for a startup with no ops team versus a research lab with GPUs, and defend each choice with the matrix.

References