Vibeleaderboard

Which database should I use for a new AI-built application?

Postgres, unless you have a specific reason not to. It covers relational, JSON, full-text, and vector search in one system, and one database you understand beats three you operate badly.

Surveyed 17 September 2026

Choose data infrastructure

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No.Tool
  1. 01
    PostgreSQL

    Open-source SQL database with JSON support, full-text search, and extensions such as PostGIS and pgvector.

    Developer Tools
  2. 02
    Supabase

    Open-source Firebase alternative combining a Postgres database with auth, realtime, storage, and edge functions.

    Developer Tools
  3. 03
    Neon

    Postgres run serverlessly, where databases branch, scale themselves, and provision instantly.

    Developer Tools
  4. 04
    PlanetScale

    MySQL-compatible serverless database with branching, non-blocking schema changes, and Vitess-based scaling.

    Developer Tools
  5. 05
    Turso

    SQLite-compatible database built on libSQL that replicates to edge locations for low-latency reads close to users.

    Developer Tools
  6. 06
    MongoDB

    Document database with flexible JSON schemas, offered as a managed cloud service through Atlas.

    Developer Tools
  7. 07
    Redis

    In-memory key-value store used for caching, queues, session storage, and real-time data workloads.

    Developer Tools
  8. 08
    Upstash

    Serverless Redis and Kafka used for caching, rate limiting, and messaging in serverless architectures.

    Developer Tools
  9. 09
    Pinecone

    Serverless managed vector database for storing and querying embeddings at scale, common as a RAG retrieval layer.

    Developer Tools
  10. 10
    SQLite

    Self-contained, zero-configuration SQL database that stores everything in a single file and is public domain.

    Developer Tools

A curated selection in editorial order. Use the fit and evidence to judge it for your task. Something missing?

What to look for

  • 01Can one system cover your access patterns? Every additional store adds operational surface and sync bugs.
  • 02Are migrations versioned in the repository and applied by a pipeline? Dashboard-edited schemas drift silently.
  • 03Does it have a local development story? Testing against production is not a strategy.

Common questions

Do I need a separate vector database?
Usually not. Postgres with pgvector handles millions of embeddings alongside your relational data, which makes filtered semantic queries a single join instead of a cross-system merge.
What is the most common data mistake in AI-built apps?
Schema drift between the repository and production, because a migration was applied in a dashboard and never written down. Version every change as a file and apply it through a pipeline.

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