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Overview
Product Information
PLATFORMS
Windows, macOS, Linux
DEVELOPER
pgvector open-source project
PRICING MODEL
USER REVIEWS
1 verified review
Expert InsightsAI-Powered Analysis
Free Tier Available
Offers a permanent free plan, making it highly accessible for small teams.
Market Verified
Solid reputation for performance and uptime as reported by verified users.
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pgvector Verified Features
General & Miscellaneous
- HNSW and IVFFlat approximate indexesHelpful
Key Highlights
- pgvector is a PostgreSQL extension for storing vector embeddings with relational data and querying nearest neighbors using several distance metrics. It supports exact search
- and multiple vector formats while retaining PostgreSQL transactions and SQL joins.
Core Features Summary
“pgvector is a PostgreSQL extension for storing vector embeddings with relational data and querying nearest neighbors using several distance metrics. It supports exact search, HNSW and IVFFlat approximate indexes, and multiple vector formats while retaining PostgreSQL transactions and SQL joins.”
Pricing and Plans
Open-source software — free to self-host with available cloud options
100% Free
Free & Open Source
pgvector is completely free to use. There are no pricing tiers, hidden subscriptions, or locked features. You can leverage the full potential of this software for personal or commercial projects at no cost.
Pricing Model
Pricing Type
This software is open source and free to self-host and use under its open-source license.
Pricing Highlights
- Open-source code — free to self-host
- Verified official pricing page
- Managed directly by vendor
Pricing FAQ
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Still have questions? Visit the official pgvector website or contact their support team for detailed pricing information and personalized assistance.
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Developer Support
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Company Information
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User Reviews
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User Reviews
Expert Analysis & Editorial Review
Ratings Breakdown
😀Pros
- Open-source with no separate license fee; keeps embeddings with relational data; supports exact and approximate nearest-neighbor search; works with PostgreSQL clients
🙁Cons
- Requires PostgreSQL administration and tuning; approximate indexes trade recall for speed; large vector workloads need capacity planning
pgvector adds vector similarity search to PostgreSQL for semantic search, recommendations, and retrieval-augmented generation. Developers can use familiar SQL and PostgreSQL operations while keeping embeddings close to application data, reducing the need to run a separate vector database for every use case.
What they liked most
SQL-based vector search inside PostgreSQL
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About pgvector (Company)
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