Compare the Top Relational Database that integrates with LangChain as of December 2025

This a list of Relational Database that integrates with LangChain. Use the filters on the left to add additional filters for products that have integrations with LangChain. View the products that work with LangChain in the table below.

What is Relational Database for LangChain?

Relational database software provides users with the tools to capture, store, search, retrieve and manage information in data points related to one another. Compare and read user reviews of the best Relational Database for LangChain currently available using the table below. This list is updated regularly.

  • 1
    Tiger Data

    Tiger Data

    Tiger Data

    Tiger Data is the creator of TimescaleDB, the world’s leading PostgreSQL-based time-series and analytics database. It provides a modern data platform purpose-built for developers, devices, and AI agents. Designed to extend PostgreSQL beyond traditional limits, Tiger Data offers built-in primitives for time-series data, search, materialization, and scale. With features like auto-partitioning, hybrid storage, and compression, it helps teams query billions of rows in milliseconds while cutting infrastructure costs. Tiger Cloud delivers these capabilities as a fully managed, elastic environment with enterprise-grade security and compliance. Trusted by innovators like Cloudflare, Toyota, Polymarket, and Hugging Face, Tiger Data powers real-time analytics, observability, and intelligent automation across industries.
    Starting Price: $30 per month
  • 2
    CrateDB

    CrateDB

    CrateDB

    The enterprise database for time series, documents, and vectors. Store any type of data and combine the simplicity of SQL with the scalability of NoSQL. CrateDB is an open source distributed database running queries in milliseconds, whatever the complexity, volume and velocity of data.
  • 3
    ZeusDB

    ZeusDB

    ZeusDB

    ZeusDB is a next-generation, high-performance data platform designed to handle the demands of modern analytics, machine learning, real-time insights, and hybrid data workloads. It supports vector, structured, and time-series data in one unified engine, allowing recommendation systems, semantic search, retrieval-augmented generation pipelines, live dashboards, and ML model serving to operate from a single store. The platform delivers ultra-low latency querying and real-time analytics, eliminating the need for separate databases or caching layers. Developers and data engineers can extend functionality with Rust or Python logic, deploy on-premises, hybrid, or cloud, and operate under GitOps/CI-CD patterns with observability built in. With built-in vector indexing (e.g., HNSW), metadata filtering, and powerful query semantics, ZeusDB enables similarity search, hybrid retrieval, filtering, and rapid application iteration.
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