Polygres
Polygres combines PostgreSQL hosting with native graph traversal, HNSW vector search, and hybrid retrieval APIs for AI agent workflows.
Polygres
What is Polygres?
Polygres vs Similar AI Tools
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How to use Polygres?
- 1Create an account at polygres.com.
- 2Deploy a managed PostgreSQL instance with pgGraph and pgVector.
- 3Connect using any Postgres-compatible client, Python SDK, or REST API.
- 4Write queries combining SQL, graph traversals, and vector similarity search.
Polygres Key Features
- Full PostgreSQL 17 instance with ACID compliance
- Native graph traversal via pgGraph
- HNSW vector search with scalar filtering via pgVector
- Hybrid search combining relational, graph, and vector queries
- Managed cloud with Kubernetes and Docker support
- Python SDK and REST API for agent workflows
Polygres Use Cases
- AI agent data storage and retrieval
- Building knowledge graphs with semantic meaning
- Hybrid search for RAG (Retrieval-Augmented Generation)
- Storing and querying embeddings at scale
Polygres Pricing & Free Credits
Polygres currently operates on a Free, Freemium model.
Free Tier
Free
Get started with free credits to deploy a small database.
Managed Cloud
Contact for Pricing
Scalable cloud hosting with personalized support.
Polygres Pros & Cons
Pros
- Combines relational, graph, and vector capabilities in one database
- Open source core components (PostgreSQL, pgGraph, pgVector)
- ACID compliant and battle-tested
- Easy integration with existing Postgres tools and ORMs
Cons
- Relatively new platform with limited community support
- Requires learning pgGraph syntax for graph traversal
- No built-in GUI for schema design
What is Polygres best for?
- AI agents needing structured, relational, and semantic data
- Developers building knowledge graphs with vector search
- Teams looking for a unified database for hybrid retrieval
Polygres FAQ
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