One platform for your AI application
Timescale’s enhanced PostgreSQL data platform is the home for your application's vector, relational and time-series data.
Flexible and transparent pricing
No “pay per query” or “pay per index”. Decoupled compute and storage for flexible resource scaling as you grow. Usage-based storage and dynamic compute (coming soon), so you pay only for what you actually use.
Ready to scale from day one
Push to prod with the confidence of automatic backups, failover and High Availability. Use read replicas to scale query load. One-click database forking for testing new embedding and LLM models. Consultative support to guide you as you grow at no extra cost.
Enterprise-grade security and data privacy
SOC2 Type II, HIPAA and GDPR compliance. Data encryption at rest and in motion. VPC peering for your Amazon VPC. Secure backups. Multi-factor authentication.
Access your PostgreSQL database any way you want. Go with a Python client, integrations in your favorite LLM frameworks, or through PostgreSQL libraries, ORMs, connectors, and tools.
Pgvector vs Pinecone: Vector database comparison
Pgvectorscale Github
Pgai Github
PostgreSQL and pgvector: Now Faster than Pinecone, 75% Cheaper, 100% Open Source
Pgai: Giving PostgreSQL Developers AI Engineering Superpowers
Making PostgreSQL a Better AI Database
PostgreSQL Hybrid Search Using Pgvector and Cohere
How to Implement RAG With Amazon Bedrock and LangChain
A Beginner’s Guide to Vector Embeddings
LangChain and pgvector: Up and Running
Create, store and query OpenAI embeddings with PostgreSQL and pgvector
What Are ivfflat Indexes in pgvector and How Do They Work
A Complete Guide to Creating and Storing Embeddings for PostgreSQL Data
How We Designed a Resilient Vector Embedding Creation System for PostgreSQL Data
Text to SQL with PostgreSQL and GPT4o
Combine Vector Search and RAG with Instructor