A Backend for AI Applications — Postgres, Auth, and Storage for LLM Apps
AI apps still need a normal backend: user accounts, conversation history, uploaded documents, and usage tracking. Kolaybase handles that foundation — PostgreSQL, auth, storage, and a REST API — so you can focus on the model and the product.
Store chats and history
Keep conversations, messages, and prompts in PostgreSQL with relational structure and fast queries.
Document storage for RAG
Upload source files to S3-compatible storage and track their metadata in the database for retrieval pipelines.
User auth and quotas
Authenticate users and model usage limits in your schema, enforced with row-level security.
Standard SQL, pgvector-ready
Because it's real PostgreSQL, you can adopt extensions and patterns the ecosystem already supports.
Save a conversation turn
await kb.from("messages").insert({
conversation_id: conversationId,
role: "user",
content: prompt,
});Frequently asked questions
- Can I build a chatbot or LLM app backend on Kolaybase?
- Yes. Store users, conversations, messages, and documents in PostgreSQL, handle auth and file uploads, and serve everything through the REST API while your app calls the model.
- Does Kolaybase work for retrieval-augmented generation (RAG)?
- You can store documents in object storage and their metadata in PostgreSQL. Since it's standard Postgres, you can use the broader ecosystem of extensions and tooling around it.
More use cases
Start building today
A complete backend for your ai apps — running in minutes.