Best Embedded Database Systems for Python

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Compare the Top Embedded Database Systems that integrate with Python as of June 2025

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This a list of Embedded Database systems that integrate with Python. Use the filters on the left to add additional filters for products that have integrations with Python. View the products that work with Python in the table below.

What are Embedded Database Systems for Python?

Embedded database systems are lightweight, self-contained databases that are integrated directly into applications, allowing data management without requiring a separate database server. They are optimized for performance and simplicity, often running within the same process as the host application, making them ideal for use in mobile apps, IoT devices, and small-scale applications. These databases support SQL or other query languages and offer full database functionality, including transaction management and data integrity. Embedded database systems are designed to operate with minimal configuration, providing fast, reliable data storage and retrieval within constrained environments. Their ease of integration and low resource usage make them essential for applications that need efficient local data management without the overhead of external databases. Compare and read user reviews of the best Embedded Database systems for Python currently available using the table below. This list is updated regularly.

  • 1
    LanceDB

    LanceDB

    LanceDB

    LanceDB is a developer-friendly, open source database for AI. From hyperscalable vector search and advanced retrieval for RAG to streaming training data and interactive exploration of large-scale AI datasets, LanceDB is the best foundation for your AI application. Installs in seconds and fits seamlessly into your existing data and AI toolchain. An embedded database (think SQLite or DuckDB) with native object storage integration, LanceDB can be deployed anywhere and easily scales to zero when not in use. From rapid prototyping to hyper-scale production, LanceDB delivers blazing-fast performance for search, analytics, and training for multimodal AI data. Leading AI companies have indexed billions of vectors and petabytes of text, images, and videos, at a fraction of the cost of other vector databases. More than just embedding. Filter, select, and stream training data directly from object storage to keep GPU utilization high.
    Starting Price: $16.03 per month
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  • 2
    Apache Phoenix

    Apache Phoenix

    Apache Software Foundation

    Apache Phoenix enables OLTP and operational analytics in Hadoop for low-latency applications by combining the best of both worlds. The power of standard SQL and JDBC APIs with full ACID transaction capabilities and the flexibility of late-bound, schema-on-read capabilities from the NoSQL world by leveraging HBase as its backing store. Apache Phoenix is fully integrated with other Hadoop products such as Spark, Hive, Pig, Flume, and Map Reduce. Become the trusted data platform for OLTP and operational analytics for Hadoop through well-defined, industry-standard APIs. Apache Phoenix takes your SQL query, compiles it into a series of HBase scans, and orchestrates the running of those scans to produce regular JDBC result sets. Direct use of the HBase API, along with coprocessors and custom filters, results in performance on the order of milliseconds for small queries, or seconds for tens of millions of rows.
    Starting Price: Free
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