Qdrant vs. pgvector
Qdrantとpgvectorの以下の能力セットで比較します。私たちでなくても、あなたに最適なデータベースを選んでほしいです。
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 pgvector 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. pgvector at a Glance
はい。専用ベクターデータベース
pgvector は Postgres のベクトル検索アドオンです。
Apacheライセンス2.0
PostgreSQLライセンス (MITに類似)
23,617
15,643
オンプレム、クラウド
オンプレム
Qdrant 概要
Qdrantは高性能な類似検索やリアルタイムAIアプリケーション向けに設計されたオープンソースのベクトルデータベースです。フィルタリング、動的シャーディング、水平スケーラビリティに優れており、複雑な多次元クエリを含む億単位のデータセットを処理するための堅牢なソリューションです。Qdrantはレコメンデーションシステム、パーソナライズド検索、その他AIを活用したアプリケーションに最適です。
pgvector の概要
pgvectorはPostgreSQLの拡張であり、データベース内で直接ベクトルの類似検索のサポートを追加します。pgvectorは、PostgreSQLの成熟したエコシステムを活用し、従来のリレーショナルクエリとベクトルベースの検索を組み合わせたハイブリッドアプリケーションに最適です。
Benchmarking Qdrant and pgvector 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.
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.