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 13 August 2026
Choose data infrastructure
Open in Tools →- 01PostgreSQL
Open-source relational database. The most popular open-source SQL database. JSON, full-text search, extensions (pg_vector, PostGIS), and rock-solid reliability.
Developer Tools - 02Supabase
Open-source Firebase alternative — Postgres database, auth, realtime, storage, and edge functions in one
Developer Tools - 03Neon
Serverless Postgres with database branching, autoscaling, and instant provisioning
Developer Tools - 04PlanetScale
Serverless MySQL platform. MySQL-compatible serverless database with branching, non-blocking schema changes, and horizontal scaling powered by Vitess.
Developer Tools - 05Turso
Edge-hosted SQLite databases. SQLite-compatible database built on libSQL that replicates to edge locations. Ideal for low-latency reads close to users.
Developer Tools - 06MongoDB
Document database. JSON-document database with flexible schemas. Atlas is the managed cloud offering. Strong fit for fast-iterating apps.
Developer Tools - 07Redis
In-memory data store. In-memory key-value store used for caching, queues, sessions, and real-time data. Now under the RSALv2/SSPL license.
Developer Tools - 08Upstash
Serverless Redis and Kafka. Serverless Redis and Kafka with per-request pricing. Ideal for caching, rate limiting, and messaging in serverless architectures.
Developer Tools - 09Pinecone
Pinecone is a fully-managed, serverless vector database that lets developers store and query embeddings at scale without running infrastructure — a common production retrieval layer for RAG.
Developer Tools - 10SQLite
Embedded SQL database. Self-contained, serverless, zero-config SQL database in a single file. Public domain and ubiquitous — great for local-first apps, edge, and small services.
Developer Tools
Ordered by the VibeLeaderboard index, re-surveyed each edition. 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.
More in Ship and operate
- Interface with your agentsTerminals, multiplexers, and runtimes for running coding agents all day.
- Deploy an applicationPublish previews and production builds without managing servers.
- Add a backendCombine databases, storage, APIs, and server-side functions.
- Add authenticationImplement accounts, sessions, identity providers, and authorization.
- Accept paymentsAdd subscriptions, checkout, billing, and payment infrastructure.
- Choose compute and GPUsFind cloud compute, serverless runtimes, and GPU providers for training and inference.
- Monitor product and usageCompare error monitoring, observability, product analytics, and web analytics.
- Automate deliveryBuild, test, preview, and release changes through CI/CD services.
- Add email and messagingSend transactional email, notifications, chat, and product messages.
- Secure and connect a domainFind domains, DNS, certificates, secrets, and application security services.