Qdrant vs. FAISS
Compare Qdrant vs. FAISS for vector search workloads. We want you to choose the most suitable vector database for your use case, even if it’s not us.
As AI technologies evolve, vector similarity search has become essential for powering modern AI applications like retrieval-augmented generation (RAG), semantic search, and recommendation engines. There are various vector search solutions available, including purpose-built vector databases, vector search libraries, and traditional databases with vector search as an add-on. Selecting the right solution is crucial for the success of your AI applications.
Qdrant and FAISS both bring unique strengths to vector search workloads, each with its own capabilities and limitations. The best choice depends on your specific use case and requirements. In the following sections, we’ll compare both databases regarding functionality, scalability, and availability, helping you determine the most suitable option for your needs—even if it’s not us.
Qdrant vs. FAISS at a Glance
Yes. Purpose-built vector database
Yes. It is a vector search library.
Apache License 2.0
MIT License
21,353
32,379
On-prem, Cloud
On-prem
Qdrant overview
Qdrant is an open-source vector database designed for high-performance similarity searches and real-time AI applications. It excels at filtering, dynamic sharding, and horizontal scalability, making it a robust solution for handling billion-scale datasets with complex, multidimensional queries. Qdrant is ideal for applications like recommendation systems, personalized search, and other AI-driven use cases.
Faiss overview
Faiss (Facebook AI Similarity Search) is an open-source library designed specifically for fast and efficient similarity search in large-scale datasets. Optimized for GPU and CPU computations, Faiss supports billions of vectors with high-dimensional indexing techniques like IVF and HNSW. It is a powerful tool for embedding-based searches in AI applications, though it focuses purely on vector search and lacks traditional database features.
Benchmarking Qdrant and FAISS on your own
VectorDBBench is an open-source benchmarking tool designed for users who require high-performance data storage and retrieval systems, particularly vector databases. This tool allows users to test and compare the performance of different vector database systems using their own datasets and determine the most suitable one for their use cases. Using VectorDBBench, users can make informed decisions based on the actual vector database performance rather than relying on marketing claims or anecdotal evidence.
VectorDBBench is written in Python and licensed under the MIT open-source license, meaning anyone can freely use, modify, and distribute it. The tool is actively maintained by a community of developers committed to improving its features and performance.
Check out the VectorDBBench Leaderboard for a quick look at the performance of mainstream vector databases.
Compare Qdrant with others
The Definitive Guide to Choosing a Vector Database
Overwhelmed by all the options? Learn key features to look for & how to evaluate with your own data. Choose with confidence.