Qdrant vs. LanceDB
Compare Qdrant vs. LanceDB 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 LanceDB 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. LanceDB at a Glance
Yes. Purpose-built vector database
Yes. Purpose-built vector database
Apache License 2.0
Apache 2.0
21,446
5,313
On-prem, Cloud
On-prem, Cloud
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.
LanceDB overview
LanceDB is an open-source vector database built with a focus on simplicity and performance. Designed to be lightweight, it provides fast, efficient storage and querying of vector embeddings. LanceDB is well-suited for developers seeking a minimalistic, embedded-first database for local or edge-based AI applications.
Benchmarking Qdrant and LanceDB 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.