Vector Database Comparison

A comprehensive side-by-side comparison of the leading vector databases to help you choose the right one for your project.

Full Comparison Table

Decision Flowchart

Use this flowchart to narrow down your choice:

1. Do you already use PostgreSQL?

   Yes: Start with pgvector. It adds vector search to your existing database with no new infrastructure. Migrate to a dedicated vector DB only if you outgrow it.

2. Are you prototyping or building a small app?

   Yes: Use ChromaDB. Install with pip, run in-memory or persistent, and get started in minutes. Zero configuration needed.

3. Do you need zero infrastructure management?

   Yes: Use Pinecone. Fully managed, serverless, scales automatically. Focus on building, not on ops.

4. Do you need hybrid search (vector + keyword)?

   Yes: Use Weaviate or Qdrant. Both offer native hybrid search that combines vector similarity with BM25 keyword matching.

5. Do you need billion-scale with GPU acceleration?

   Yes: Use Milvus. It supports GPU-accelerated indexing, distributed deployment, and handles billions of vectors.

6. Do you need maximum query performance?

   Yes: Use Qdrant. Written in Rust, it consistently benchmarks among the fastest for query latency and throughput.

Performance Benchmarks

Benchmark results vary based on dataset size, dimensions, hardware, and configuration. These are approximate figures for 1M vectors at 1536 dimensions on similar hardware:

Migration Between Databases

If you need to migrate from one vector database to another, the process is generally straightforward:

1. Export vectors and metadata from the source database.
2. Transform the format to match the target database's API.
3. Batch import into the target database.
4. Re-create indexes with appropriate settings.
5. Validate by running the same queries on both databases and comparing results.

import chromadb
from pinecone import Pinecone

# Export from ChromaDB
chroma = chromadb.PersistentClient(path="./chroma_data")
collection = chroma.get_collection("my_docs")
all_data = collection.get(include=["embeddings", "metadatas", "documents"])

# Import to Pinecone
pc = Pinecone(api_key="your-key")
index = pc.Index("my-index")

# Batch upsert
batch_size = 100
for i in range(0, len(all_data["ids"]), batch_size):
    batch = [
        {
            "id": all_data["ids"][j],
            "values": all_data["embeddings"][j],
            "metadata": {
                **all_data["metadatas"][j],
                "text": all_data["documents"][j]
            }
        }
        for j in range(i, min(i + batch_size, len(all_data["ids"])))
    ]
    index.upsert(vectors=batch)