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Here is a comprehensive comparison of Qdrant vs Milvus vs ChromaDB, three of the most popular open-source vector databases used in AI and LLM applications:
|
Feature / Criteria
|
Qdrant
|
Milvus
|
ChromaDB
|
|---|---|---|---|
| Core Language | Rust | C++ + Go | Python |
| Performance | High (optimized for speed and memory) | Very high (FAISS/IVF-based acceleration) | Medium (best for prototyping & light use) |
| GPU Acceleration | Not yet native (planned) | Yes (via Faiss GPU support) | No (CPU only) |
| Vector Index Types | HNSW, IVF-PQ, Flat | IVF, HNSW, ANNOY, NSG, DiskANN | Only supports HNSW |
| Filtering | Strong payload filtering + metadata | Rich filtering with scalar fields | Basic filtering support |
| Multi-tenancy | Yes | Yes (via collection partitioning) | No |
| Scalability | Horizontally scalable with sharding | Highly scalable, Kubernetes-native | Limited (not recommended for scale) |
| Deployment Options | Docker, Kubernetes, Binary | Docker, Helm, K8s, Cloud | Python-only, local development |
| Ease of Use | Simple REST/gRPC API, good docs | Powerful but more complex setup | Very easy for devs familiar with Python |
| Best For | Production RAG, semantic search | Large-scale vector search & AI pipelines | Quick prototyping & experiments |
| Active Development | 🔥 Active | 🔥 Active | 🟡 Slower compared to others |
| Use Cases | RAG, Search, Recommendations, Filters | Massive-scale RAG, image/video retrieval | Small RAG apps, toy projects |
Milvus is widely adopted by companies, researchers, and developers building AI-native applications, especially those requiring vector similarity search. Below are some of the main groups and organizations using Milvus!
Store and query dense vector embeddings from models like BERT or CLIP to power intelligent search over documents, products, or images.
Combine Qdrant with large language models (e.g., LLaMA, Mistral, GPT) to create custom assistants that retrieve relevant context from your knowledge base before generating answers.
Use vector similarity to recommend similar products, songs, or movies based on user behavior or content features.
Store and index embeddings from image or video encoders (e.g., CLIP) to find visually similar items or scenes.
By mapping behavior or system logs into vector space, Qdrant can help identify outliers through vector distance metrics.
Store embeddings from multilingual transformers like LaBSE or XLM-R to enable cross-language semantic search.
Index audio clip embeddings (e.g., from Whisper or Wav2Vec) to search by voice similarity.
Deploy user-specific vector spaces for real-time search or feed ranking tailored to each user’s interests.
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