pgvector vs. KDB.AI
Compare pgvector vs. KDB.AI 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.
pgvector and KDB.AI 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.
pgvector vs. KDB.AI at a Glance
No. pgvector is just a vector search add-on to Postgres
No. It is a time series database service with vector search is an add-on.
PostgreSQL License (similar to MIT)
Proprietary License
13,608
N/A
On-prem
On-prem, Cloud
pgvector overview
pgvector is an extension for PostgreSQL that adds support for vector similarity search directly within the database. It allows developers to store, index, and query vector embeddings alongside relational data. pgvector is ideal for hybrid applications that combine traditional relational queries with vector-based retrieval, leveraging PostgreSQL’s mature ecosystem.
KDB.AI overview
KDB.AI is a high-performance time-series and vector database tailored for AI and machine learning applications. It combines its strength in time-series data processing with vector search capabilities, enabling real-time analytics and retrieval for complex datasets. KDB.ai is particularly well-suited for use cases involving temporal and high-dimensional data.
Benchmarking pgvector and KDB.AI 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 pgvector 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.