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PostgreSQL

PostgreSQL

The world's most advanced open source relational database.

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35+ years of active development, battle-tested at massive scale — the gold standard of open-source relational databases trusted by every major tech company.

Value Proposition

The most feature-complete, standards-compliant open-source relational database — free, portable, and capable of powering workloads from small apps to global enterprises.

Problem Solved

Provides a rock-solid, extensible relational foundation without licensing costs, avoiding proprietary vendor lock-in.

Target Audience

Developers, DBAs, enterprises, and data engineers who need a reliable, feature-rich, standards-compliant relational database for any scale of workload.

Life Without PostgreSQL

×No licensing fees — completely free to use at any scale
×No vendor lock-in — runs on any cloud or on-premises
×No proprietary query language — standard SQL
×No artificial usage or connection limits imposed by the database itself

Life With PostgreSQL

Full ACID compliance and MVCC for data integrity
Advanced SQL with CTEs, window functions, and lateral joins
JSON and JSONB support for semi-structured data
Powerful extension ecosystem — pgvector, PostGIS, TimescaleDB and more
Full-text search built in
Row-level security for fine-grained access control
Logical replication and streaming replication
Active 35+ year community with regular major releases

Questions & Answers

Absolutely. PostgreSQL powers production databases at companies like Apple, Instagram, Spotify, Reddit, and Twitch, handling billions of rows and thousands of concurrent connections. With proper tuning, connection pooling (e.g. PgBouncer), and replication, it scales to very large workloads.

As of February 2026, the latest stable releases are PostgreSQL 18.3, 17.9, 16.13, 15.17, and 14.22 — all released on February 26, 2026 as part of an out-of-cycle update addressing several regressions.

Yes. The pgvector extension adds native vector storage and similarity search to PostgreSQL, making it possible to build RAG pipelines, semantic search, and embedding-based recommendation systems without a separate vector database. This is the same technology powering Supabase's AI features.

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