vector-databases

# Vector Databases

## When to Use This Skill

Use this skill when:

- Choosing between vector database options
- Designing semantic/similarity search systems
- Optimizing vector search performance
- Understanding ANN algorithm trade-offs
- Scaling vector search infrastructure
- Implementing hybrid search (vectors + filters)

**Keywords:** vector database, embeddings, vector search, similarity search, ANN, approximate nearest neighbor, HNSW, IVF, FAISS, Pinecone, Weaviate, Milvus, Qdrant, Chroma, pgvector, cosine similarity, semantic search

## Vector Database Comparison

### Managed Services

| Database | Strengths | Limitations | Best For |
| -------- | --------- | ----------- | -------- |
| **Pinecone** | Fully managed, easy scaling, enterprise | Vendor lock-in, cost at scale | Enterprise production |
| **Weaviate Cloud** | GraphQL, hybrid search, modules | Complexity | Knowledge graphs |
| **Zilliz Cloud** | Milvus-based, high performance | Learning curve | High-scale production |
| **MongoDB Atlas Vector** | Existing MongoDB users | Newer feature | MongoDB shops |
| **Elastic Vector** | Existing Elastic stack | Resource heavy | Search platforms |

### Self-Hosted Options

| Database | Strengths | Limitations | Best For |
| -------- | --------- | ----------- | -------- |
| **Milvus** | Feature-rich, scalable, GPU support | Operational complexity | Large-scale production |
| **Qdrant** | Rust performance, filtering, easy | Smaller ecosystem | Performance-focused |
| **Weaviate** | Modules, semantic, hybrid | Memory usage | Knowledge applications |
| **Chroma** | Simple, Python-native | Limited scale | Development, prototyping |
| **pgvector** | PostgreSQL extension | Performance limits | Postgres shops |
| **FAISS** | Library, not DB, fastest | No persistence, no filtering | Research, embedded |

### Selection Decision Tree

```text
Need managed, don't want operations?
├── Yes → Pinecone (simplest) or Weaviate Cloud
└── No (self-hosted)
    └── Already using Post