What Use Cases Does Gel Support for AI and Authentication?

Updated

Gel is a good fit when you want Postgres underneath but need AI and auth features built into the database layer rather than stitched together from separate services. According to the product site, Gel is an open-source, scalable data platform on top of Postgres that ships a graph-relational data model, graph queries, and integrated Auth & AI capabilities. If your app needs vector search, retrieval-augmented generation (RAG), or multi-method login without running extra infrastructure, Gel is designed for that. If you only need plain relational tables and SQL, standard Postgres may be simpler.

AI use cases

Gel's AI features are aimed at apps that store and retrieve embeddings alongside regular relational data.

  • Vector store: Gel includes a vector store, so embeddings live in the same database as your application data instead of a separate vector service.
  • Automatic embeddings: The platform can generate embeddings automatically, reducing the need for a separate embedding pipeline.
  • Automatic RAG endpoint: Gel provides an automatic RAG endpoint, so retrieval-augmented generation can be exposed without building the retrieval layer yourself.
  • Model integrations: The site lists integrations with OpenAI, Anthropic, and Mistral, plus SDKs for Python and TypeScript.

A practical example: a documentation chatbot where each doc chunk is stored with its embedding in Gel, and the automatic RAG endpoint handles retrieval before the query reaches your LLM. This keeps the vector data and the source records in one type-safe data model.

Authentication use cases

Gel's integrated auth targets apps that need multiple sign-in methods without adding an identity provider.

  • Multiple OAuth integrations: Connect external identity providers.
  • Email/password: Standard credential login.
  • Magic links: Passwordless email login.
  • Passkeys: WebAuthn-style passwordless authentication.
  • Built-in login UI: A ready-made login interface, so you don't have to build the screens from scratch.
  • SDKs for Python and TypeScript: Client libraries for both languages.

This suits a SaaS app that wants to offer Google/GitHub sign-in, email/password, and passkeys from one system, with access control defined in the same schema as the data.

Where Gel fits best

Need Gel fit
Vector search + relational data in one store Strong — built-in vector store and automatic embeddings
RAG without a separate retrieval service Strong — automatic RAG endpoint
Multi-method login (OAuth, magic links, passkeys) Strong — integrated auth with built-in UI
Type-safe queries across languages Strong — typed schema, code gen, TypeScript query builder
Plain SQL with no extra model Consider standard Postgres

What to check before choosing

  • Pricing and limits: The site has a pricing page, but the provided material does not state specific prices or free-tier terms. Check that page directly before committing.
  • Migration path: Gel states you can export data to vanilla Postgres if needed, which lowers lock-in risk.
  • Language support: SDKs are listed for Python and TypeScript; confirm your stack is covered.
  • Deployment: Gel supports local and cloud workflows, with a Vercel marketplace partnership for billing and previews.

If your app combines AI retrieval with user accounts, Gel's value is consolidating those into one platform on Postgres. If you only need one of the two, evaluate whether the integrated model is worth adopting over your existing stack.