
ChromaDB
The AI-native open-source embedding database for building RAG applications with speed and simplicity.

The AI-Native Distributed SQL Engine for RAG and High-Performance Predictive Analytics.

DeepSQL represents the 2026 frontier of database technology, functioning as a high-performance, distributed relational database engine with native AI orchestration capabilities. Unlike traditional SQL databases that require external middleware for machine learning, DeepSQL embeds inference engines directly into the query execution plan. This architecture allows for real-time model serving and vector operations within standard SQL syntax (e.g., SELECT PREDICT...). Built on a distributed consensus protocol, it maintains ACID compliance while scaling to petabyte-level workloads. For the 2026 market, DeepSQL's primary advantage lies in its 'Zero-ETL' approach to AI, where data remains within the transactional layer while being accessible for LLM context windows and vector-based retrieval. It significantly reduces latency in Retrieval-Augmented Generation (RAG) pipelines by co-locating metadata, relational data, and vector embeddings in a single unified storage layer, optimized for both OLTP and OLAP workloads with an AI-first priority queue.
DeepSQL represents the 2026 frontier of database technology, functioning as a high-performance, distributed relational database engine with native AI orchestration capabilities.
Explore all tools that specialize in vector similarity search. This domain focus ensures DeepSQL delivers optimized results for this specific requirement.
Executes ONNX and TorchScript models directly within the database engine during query runtime.
Combines BM25 keyword ranking with HNSW vector similarity in a single execution plan.
Distributes vector index builds across multiple compute nodes to handle billions of embeddings.
Maintains historical versions of vector embeddings for time-series semantic analysis.
Change Data Capture that automatically triggers embedding updates and LLM cache invalidation.
Integrated differential privacy layers that mask PII during AI-driven analytical queries.
Automatically moves infrequently accessed vector data to cold S3-compatible storage.
Provision a DeepSQL cluster via the Cloud Console or Docker Compose.
Establish connection via standard PostgreSQL-compatible drivers.
Define schema using AI-extended DDL (Data Definition Language) for vector columns.
Ingest data using the Bulk Loader or real-time CDC (Change Data Capture) connectors.
Register your ML models (HuggingFace/OpenAI) using the CREATE MODEL command.
Index vector columns using IVFFlat or HNSW algorithms for rapid retrieval.
Configure data access policies and RBAC (Role-Based Access Control).
Execute hybrid queries combining relational filters and vector similarity.
Monitor performance through the DeepSQL Observability Dashboard.
Scale compute nodes horizontally based on query-driven load metrics.
All Set
Ready to go
Verified feedback from other users.
"Highly praised for its seamless integration of ML and SQL, though users note a learning curve for advanced vector tuning."
Post questions, share tips, and help other users.

The AI-native open-source embedding database for building RAG applications with speed and simplicity.

The fastest open-source column-oriented database management system for real-time analytics.

The unified developer data platform for building AI-powered, globally distributed applications.

The World's Leading Graph Database for Knowledge Graphs and GraphRAG-powered AI.
Design, document, and build APIs faster.
Digital developers who are actually easy to work with.