PostgreSQL is a relational database that goes significantly further than standard SQL. It stores structured data in tables like any relational database, but it also handles JSON documents, full-text search, geographic data, array columns, and custom data types - all within the same database, using the same query language. Products that would normally need multiple separate databases to handle different types of data can often be built on PostgreSQL alone. Fewer systems to manage means fewer things that can go wrong.

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In Postgres we trust. When data correctness is non-negotiable, PostgreSQL is the industry's first choice.

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The query capabilities in PostgreSQL are broader than most relational databases. Window functions let you calculate running totals, rankings, and moving averages directly in a query without pulling all the data into the application first. Common Table Expressions let you break complex queries into readable, named steps. Partial indexes index only the rows that match a condition, making them smaller and faster than full-column indexes for filtered queries. These are not features most applications need on day one - but they are there when the data requirements grow complex enough to need them, and they allow problems to be solved in the database where they belong rather than in application code.

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One capability we use extensively in AI products is pgvector - a PostgreSQL extension that adds vector storage and similarity search. When building RAG systems and AI search, embeddings - numerical representations of text - need to be stored and searched by similarity. pgvector handles this directly inside PostgreSQL, meaning the embeddings sit alongside the data they describe in the same database. There is no separate vector database to deploy, maintain, and keep in sync. The query that retrieves a document can also find semantically similar documents in the same operation.

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What this means for your product:

• One database that handles structured data, JSON, full-text search, and AI embeddings together

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• Query capabilities that solve complex data problems without pulling everything into application code

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• A database with a strong reputation for correctness - it does what the documentation says

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• Vector search built in for AI features without a separate database system

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Chips:

PostgreSQL · JSONB · Full-Text Search · pgvector · Window Functions · CTEs · Advanced Indexing

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