Vector Database Comparison

Choose the right vector database for your AI applications on Clore.ai GPU servers.

Vector databases store and retrieve high-dimensional embeddings efficiently — the core infrastructure for RAG systems, semantic search, and recommendation engines. This guide compares the four most popular open-source options.

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Quick Decision Matrix

	ChromaDB	Qdrant	Milvus	Weaviate
Best for	Prototyping, local dev	Production RAG	Billion-scale search	Knowledge graphs
Deployment	Embedded/Server	Server/Cloud	Server/Cloud	Server/Cloud
Scalability	Single-node	Multi-node	Distributed	Distributed
GitHub stars	17K+	21K+	31K+	12K+
License	Apache 2.0	Apache 2.0	Apache 2.0	BSD 3-Clause
Managed cloud	No	Yes (Qdrant Cloud)	Yes (Zilliz)	Yes (Weaviate Cloud)
Language	Python	Rust	Go	Go

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Overview

ChromaDB

ChromaDB is the simplest vector database — designed for rapid prototyping and small-to-medium scale applications. It can run entirely in-memory or persist to disk.

Philosophy: Zero configuration, maximum developer experience.

import chromadb

client = chromadb.PersistentClient(path="/data/chroma")
collection = client.create_collection("my_docs")

collection.add(
    documents=["Machine learning is great", "Deep learning uses neural networks"],
    ids=["doc1", "doc2"]
)

results = collection.query(
    query_texts=["What is AI?"],
    n_results=2
)

Qdrant

Qdrant is a production-ready vector search engine written in Rust. It focuses on performance, filtering, and operational simplicity.

Philosophy: Production performance without operational complexity.

from qdrant_client import QdrantClient
from qdrant_client.models import Distance, VectorParams, PointStruct

client = QdrantClient("localhost", port=6333)
client.create_collection(
    collection_name="my_collection",
    vectors_config=VectorParams(size=1536, distance=Distance.COSINE)
)

client.upsert(
    collection_name="my_collection",
    points=[
        PointStruct(id=1, vector=[...], payload={"text": "document 1"}),
    ]
)

results = client.search(
    collection_name="my_collection",
    query_vector=[...],
    limit=10,
    query_filter=Filter(must=[FieldCondition(key="category", match=MatchValue(value="tech"))])
)

Milvus

Milvus is the most scalable open-source vector database, designed for billion-scale deployments. It has a distributed architecture with Kubernetes support.

Philosophy: Massive scale, cloud-native.

from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType

connections.connect("default", host="localhost", port=19530)

fields = [
    FieldSchema(name="id", dtype=DataType.INT64, is_primary=True),
    FieldSchema(name="embedding", dtype=DataType.FLOAT_VECTOR, dim=1536),
    FieldSchema(name="text", dtype=DataType.VARCHAR, max_length=65535),
]
schema = CollectionSchema(fields)
collection = Collection("my_collection", schema)

# Insert data
collection.insert([[1, 2], embeddings, texts])
collection.create_index("embedding", {"metric_type": "COSINE", "index_type": "IVF_FLAT"})
collection.load()

results = collection.search(
    data=[query_embedding],
    anns_field="embedding",
    param={"metric_type": "COSINE", "nprobe": 10},
    limit=10
)

Weaviate

Weaviate combines vector search with knowledge graphs and a GraphQL API. It supports multi-modal search (text, images, audio) out of the box.

Philosophy: Schema-rich, multi-modal, knowledge graph capabilities.

import weaviate

client = weaviate.Client("http://localhost:8080")

# Define schema with classes
client.schema.create_class({
    "class": "Document",
    "vectorizer": "text2vec-transformers",
    "properties": [
        {"name": "content", "dataType": ["text"]},
        {"name": "category", "dataType": ["string"]}
    ]
})

# Insert with auto-vectorization
client.data_object.create(
    {"content": "Machine learning tutorial", "category": "tech"},
    "Document"
)

# Semantic search
result = client.query.get("Document", ["content", "category"])\
    .with_near_text({"concepts": ["artificial intelligence"]})\
    .with_limit(5)\
    .do()

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Performance Benchmarks

ANN Benchmarks (ann-benchmarks.com, 2024)

1M vectors, 768 dimensions, Cosine similarity

Database	QPS (1 thread)	Recall@10	Build Time	Index Size
ChromaDB (HNSW)	~2,000	98.5%	45s	2.1GB
Qdrant (HNSW)	~8,500	99.1%	32s	1.8GB
Milvus (HNSW)	~12,000	98.9%	28s	1.9GB
Weaviate (HNSW)	~6,000	98.7%	38s	2.0GB

10M vectors (scalability test)

Database	QPS	RAM Usage	Notes
ChromaDB	~800	22GB	Struggles at scale
Qdrant	~5,200	18GB	Good with quantization
Milvus	~9,800	15GB (indexed)	Best at scale
Weaviate	~3,500	21GB	Moderate

Benchmarks are guides, not gospel. Performance varies greatly based on index type, hardware, vector dimensions, and query patterns. Always benchmark with your own data.

Filtering Performance (Filtered ANN search)

Filtered search (vector similarity + metadata filter) is crucial for production RAG:

Database	Filtered QPS	Pre-filter	Post-filter
ChromaDB	~500	❌	✅
Qdrant	~6,000	✅ (HNSW + payload index)	✅
Milvus	~8,000	✅	✅
Weaviate	~3,000	✅ (inverted index)	✅

Winner for filtered search: Qdrant and Milvus, which support true pre-filtering without post-filtering performance degradation.

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Feature Comparison

Storage and Indexing

Feature	ChromaDB	Qdrant	Milvus	Weaviate
HNSW index	✅	✅	✅	✅
IVF index	❌	❌	✅	❌
DiskANN	❌	✅	✅	❌
Scalar quantization	❌	✅	✅	✅
Product quantization	❌	✅	✅	❌
Binary quantization	❌	✅	✅	✅
On-disk storage	✅	✅	✅	✅
Mmap	❌	✅	✅	✅

Query Capabilities

Feature	ChromaDB	Qdrant	Milvus	Weaviate
Vector similarity	✅	✅	✅	✅
Hybrid search (BM25+vector)	❌	✅	✅	✅
Metadata filtering	✅ (basic)	✅ (rich)	✅ (rich)	✅ (GraphQL)
Keyword search	❌	✅	✅	✅
Multi-vector search	❌	✅	✅	✅
Sparse vectors (SPLADE)	❌	✅	✅	✅
Named vectors	❌	✅	✅	✅

Operational Features

Feature	ChromaDB	Qdrant	Milvus	Weaviate
REST API	✅	✅	✅	✅
gRPC API	❌	✅	✅	❌
GraphQL API	❌	❌	❌	✅
Authentication	Basic	✅	✅	✅
RBAC	❌	✅	✅	✅
Horizontal scaling	❌	✅	✅	✅
Kubernetes support	❌	✅	✅	✅
Snapshots/Backup	❌	✅	✅	✅
Monitoring (Prometheus)	❌	✅	✅	✅

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ChromaDB: Deep Dive

Strengths

✅ Simplest setup — pip install chromadb and you're done ✅ Embedded mode — no separate server process ✅ Auto-embedding — built-in embedding models ✅ LangChain/LlamaIndex native integration ✅ Zero config — great for prototyping

Weaknesses

❌ Limited scale — struggles beyond 1-2M vectors ❌ No distributed mode — single node only ❌ Limited filtering — no pre-filtering ❌ No quantization — higher memory usage ❌ Slow at scale — Python-based operations

Deployment on Clore.ai

# Client/server mode
docker run -d \
  --name chromadb \
  -p 8000:8000 \
  -v $(pwd)/chroma-data:/chroma/chroma \
  chromadb/chroma:latest

# Test
curl http://localhost:8000/api/v1/heartbeat

Best for: Jupyter notebooks, rapid RAG prototypes, <1M vectors

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Qdrant: Deep Dive

Strengths

✅ Best filtering — true pre-filtered vector search ✅ Rust performance — extremely fast, low latency ✅ Quantization — binary/scalar reduces memory 4-32× ✅ Sparse vectors — hybrid dense+sparse search ✅ Simple ops — single binary, no dependencies ✅ Good documentation — excellent guides and examples

Weaknesses

❌ Single-writer in free tier (no distributed writes) ❌ Smaller ecosystem than Milvus ❌ No GraphQL — REST/gRPC only

Deployment on Clore.ai

# Simple deployment
docker run -d \
  --name qdrant \
  -p 6333:6333 \
  -p 6334:6334 \
  -v $(pwd)/qdrant-storage:/qdrant/storage \
  qdrant/qdrant:latest

# With authentication
docker run -d \
  --name qdrant \
  -p 6333:6333 \
  -e QDRANT__SERVICE__API_KEY=your-secret-key \
  -v $(pwd)/qdrant-storage:/qdrant/storage \
  qdrant/qdrant:latest

# Test
curl http://localhost:6333/health

Best for: Production RAG, filtered search, 1-100M vectors

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Milvus: Deep Dive

Strengths

✅ Massive scale — tested to 10B+ vectors ✅ Distributed — cloud-native Kubernetes architecture ✅ Most index types — IVF, HNSW, DiskANN, ScaNN ✅ GPU acceleration — GPU-powered index building ✅ Enterprise features — RBAC, audit logs, encryption ✅ Zilliz Cloud — fully managed option

Weaknesses

❌ Complex deployment — requires etcd, MinIO, and Pulsar/Kafka ❌ Resource heavy — minimum 3 nodes recommended ❌ Steeper learning curve — more concepts to understand ❌ Overkill for small scale — don't use for <1M vectors

Deployment on Clore.ai (Standalone)

# docker-compose.yml for Milvus standalone
version: "3.8"
services:
  etcd:
    image: quay.io/coreos/etcd:v3.5.5
    environment:
      - ETCD_AUTO_COMPACTION_MODE=revision
      - ETCD_AUTO_COMPACTION_RETENTION=1000
      - ETCD_QUOTA_BACKEND_BYTES=4294967296
    command: etcd -advertise-client-urls=http://etcd:2379 -listen-client-urls=http://0.0.0.0:2379

  minio:
    image: minio/minio:RELEASE.2023-03-13T19-46-17Z
    environment:
      MINIO_ACCESS_KEY: minioadmin
      MINIO_SECRET_KEY: minioadmin
    command: minio server /minio_data --console-address ":9001"

  milvus:
    image: milvusdb/milvus:v2.4.0
    command: ["milvus", "run", "standalone"]
    environment:
      ETCD_ENDPOINTS: etcd:2379
      MINIO_ADDRESS: minio:9000
    ports:
      - "19530:19530"
      - "9091:9091"
    depends_on:
      - etcd
      - minio

docker compose up -d
# Takes ~60 seconds to fully start

Best for: Large-scale production, 100M+ vectors, enterprise deployments

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Weaviate: Deep Dive

Strengths

✅ Multi-modal — text, images, audio, video ✅ Auto-vectorization — built-in model integrations ✅ GraphQL API — rich querying with graph traversal ✅ Module system — pluggable vectorizers and readers ✅ Hybrid search — BM25 + vector out of the box ✅ Generative search — built-in RAG with generate module

Weaknesses

❌ Higher memory — schema-aware storage is larger ❌ No gRPC — GraphQL only (slower for high QPS) ❌ Complex schema — requires upfront class definition ❌ Slower at extreme scale than Milvus

Deployment on Clore.ai

# Simple deployment
docker run -d \
  --name weaviate \
  -p 8080:8080 \
  -p 50051:50051 \
  -e AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED=true \
  -e PERSISTENCE_DATA_PATH=/var/lib/weaviate \
  -e DEFAULT_VECTORIZER_MODULE=none \
  -e CLUSTER_HOSTNAME=node1 \
  -v $(pwd)/weaviate-data:/var/lib/weaviate \
  cr.weaviate.io/semitechnologies/weaviate:1.25.0

# With transformer vectorizer
docker run -d \
  --name weaviate \
  -p 8080:8080 \
  -e DEFAULT_VECTORIZER_MODULE=text2vec-transformers \
  -e TRANSFORMERS_INFERENCE_API=http://t2v-transformers:8080 \
  cr.weaviate.io/semitechnologies/weaviate:1.25.0

Best for: Multi-modal search, knowledge graphs, generative search

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When to Use Which

Scale-Based Decision

< 100K vectors    → ChromaDB (embedded)
100K - 10M        → Qdrant (best balance)
10M - 1B          → Milvus or Qdrant (clustered)
1B+               → Milvus (distributed)

Use-Case-Based Decision

Use Case	Best Choice	Why
RAG prototype	ChromaDB	Zero setup, simple API
Production RAG	Qdrant	Fast filtering, simple ops
Semantic search	Qdrant or Milvus	Best performance
Multi-modal	Weaviate	Built-in image/audio support
Knowledge graph	Weaviate	Graph traversal queries
Billion-scale	Milvus	Distributed architecture
Hybrid search	Qdrant or Weaviate	BM25 + vector
Enterprise	Milvus or Weaviate	RBAC, audit logs

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Memory Requirements on Clore.ai

RAM Estimation Formula

RAM needed ≈ (vectors × dimensions × 4 bytes) × 1.5 (overhead)

Example: 1M vectors × 1536 dims × 4 bytes × 1.5 = 9.2GB RAM

With quantization (Qdrant binary):
1M × 1536 / 8 × 1.5 = 0.29GB RAM (32× compression!)

Recommended Server Specs

Dataset Size	ChromaDB	Qdrant	Milvus	Weaviate
1M vectors	16GB RAM	8GB RAM	32GB RAM	16GB RAM
10M vectors	❌	32GB RAM	64GB RAM	48GB RAM
100M vectors	❌	128GB+	256GB+	256GB+

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Quick Comparison: Docker Setup Time

Database	docker run to ready	Dependencies
ChromaDB	~5 seconds	None
Qdrant	~3 seconds	None
Milvus	~60 seconds	etcd + MinIO
Weaviate	~15 seconds	None (standalone)

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Pricing (Self-Hosted on Clore.ai)

All four databases are free to self-host. Cost is just Clore.ai server rental:

Example: 1M vectors RAG system
- Qdrant: 8GB RAM server ~$0.10/hr
- ChromaDB: 16GB RAM server ~$0.15/hr  
- Weaviate: 16GB RAM server ~$0.15/hr
- Milvus: 32GB RAM server ~$0.30/hr (+ overhead for etcd/minio)

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Useful Links

• ChromaDB Docs

• Qdrant Docs

• Milvus Docs

• Weaviate Docs

• ANN Benchmarks

• Vector DB Benchmark by Qdrant

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Summary

Start with...	If you need...
ChromaDB	Quick prototype, <1M vectors, minimal setup
Qdrant	Production RAG, great filtering, operational simplicity
Milvus	Billion-scale, enterprise, distributed architecture
Weaviate	Multi-modal, knowledge graphs, GraphQL querying

For most production RAG applications on Clore.ai, Qdrant offers the best balance of performance, features, and operational simplicity. For large-scale or enterprise needs, Milvus is the industry standard.

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Clore.ai GPU Recommendations

Use Case	Recommended GPU	Est. Cost on Clore.ai
Development/Testing	RTX 3090 (24GB)	~$0.12/gpu/hr
Production	RTX 4090 (24GB)	~$0.70/gpu/hr
Large Scale	A100 80GB	~$1.20/gpu/hr

💡 All examples in this guide can be deployed on Clore.ai GPU servers. Browse available GPUs and rent by the hour — no commitments, full root access.