Comparison of 10 million-level vector databases: Milvus, Qdrant, Chroma, Weaviate
Master the performance comparison of vector databases and efficiently choose the database that suits your project.
Core content:
1. Milvus's enterprise-level application advantages and community support
2. Qdrant's lightweight features and performance
3. Chroma and Weaviate's performance on small and medium-sized data sets and community activity
We often use vector databases in large model development. The following is a comparative analysis of several common types to help us choose the appropriate database for development in our projects.
1. Milvus
Core advantages: Suitable for large-scale enterprise applications , millisecond latency (single query <100ms, depending on index and hardware, excellent performance in high concurrency and large-scale data sets (billions of vectors) scenarios, leading QPS and recall rate. 20 million 768-dimensional vectors (HNSW index), 100-500 QPS on a single machine, support for multiple indexes (leading performance in HNSW, IVF, ANN-Bench tests), adjustable parameters (such as efSearch) to balance speed and accuracy, relatively high resource consumption.
Community: Developed by Zilliz, the community is extremely active, with multi-language SDKs (Python, Java, Go, etc.) and rich APIs. Address: https://github.com/milvus-io/milvus, 34.2k stars (20250422).
Limitations : Distributed clusters are required for high concurrency, and the QPS of a single machine is limited by memory and CPU.
2. Qdrant
Core advantages : efficient engine developed in Rust language, query speed: millisecond level (<100ms), about 100-400 QPS for 20 million vectors, throughput: slightly weaker for single machine, limited improvement for distributed system, cost considerations: the two are similar in cost, Milvus is cost-effective, but lightweight, easy to integrate, good AI framework support, optimized HNSW implementation in memory and on disk, and stable performance.
Community: The community is growing rapidly, with good documentation and technical support, REST and gRPC interfaces, and easy-to-use Python/JavaScript SDK. Address: https://github.com/qdrant/qdrant, 23.1k stars (20250422).
Limitations : The distributed performance is not as stable as Milvus, and the performance on large-scale data sets is slightly inferior to Milvus, especially the throughput is limited in high-concurrency scenarios.
3. Chroma
Core advantages: Works mainly in Python environment , especially using LangChain or LlamaIndex. Good performance for small-scale data sets (less than one million), moderate query latency, suitable for rapid prototyping.
Community: The community is small but active, lightweight, with a simple API, suitable for novices and rapid development projects, and the documentation is simple and clear. Address: https://github.com/chroma-core/chroma, 19.4k stars (20250422).
Limitations: No native persistent storage, reliance on external databases; performance drops by 40% when the number of vectors exceeds 1 billion.
4. Weaviate
Core advantages: Good performance on small and medium-sized data sets, low query latency, support for hybrid search (vector + keyword), strong horizontal expansion capabilities, and can process billions of vectors.
Community: GraphQL query interface is intuitive, Python/Go SDK is easy to use, documentation is detailed, and community support is good. Address: https://github.com/weaviate/weaviate, 13.1k stars (20250422).
Limitations: GraphQL has a learning curve. Hybrid search and modularity are powerful but also increase configuration complexity . Large-scale expansion requires more infrastructure resources and the cost may be higher than Qdrant.
5. Others
Elasticsearch is too heavy, the RedisVL ecosystem is relatively weak, and other cloud platforms are a bit expensive. Those who have money can use them.
In general:
Here Weaviate uses the BSD license, which is relatively more relaxed.