LLM Database

The database for AI applications

Structured storage, auto-embeddings, and hybrid search in one API. Define a schema, get a full REST API with search built in. No Pinecone. No Postgres. No sync scripts. Working database in 10 minutes.

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Beyond Vector DBs

Beyond vector databases — structured data for RAG

Vector databases store embeddings. But LLM applications need more than vectors — they need structured data, relationships, permissions, and versioning. That's why teams end up duct-taping Pinecone + Postgres + Elasticsearch + sync scripts.

FoxNose is an LLM database designed from the ground up for AI applications. Structured schemas, auto-generated embeddings, hybrid search, and governance — all in one data platform for AI. No ETL pipelines. No separate embedding service. No sync lag.

Whether you're building a RAG pipeline, an AI agent with persistent memory, or an AI-powered SaaS product — this is the database for retrieval augmented generation that replaces your entire backend stack.

Typical AI data stack

Vector DB

Pinecone, Weaviate

Embedding API

OpenAI, Cohere

Search Engine

Elasticsearch

Database

PostgreSQL, Mongo

+ Sync scripts, ETL pipelines, cron jobs

Keeping 4 services in sync

5 services, 5 bills, infinite glue code

replaces all of it

FoxNose — one LLM database

Structured schemas

+ auto-embeddings

Hybrid search

vector + text + filters

Zero sync

one API, real-time

Governance

RBAC, versioning, audit

1 service, 1 bill, zero glue code

Auto-Embeddings

Database with built‑in vector search

Mark any field as vectorizable in your schema. On every save, the LLM database generates embeddings automatically — no external embedding API, no pipeline to build, no cron jobs to maintain. Your data is searchable by meaning the moment it's written.

1Define schema — mark fields as vectorizable

// Create a "knowledge" folder via Dashboard or Management API
// Then define fields on a schema version:
{
  "title":    { "type": "text" },
  "content":  { "type": "text", "vectorizable": true },
  "category": { "type": "string" },
  "status":   { "type": "string" }
}

2Save content — embedded and searchable instantly

// Management API — create resource in your folder
POST /v1/:env/folders/:folder_key/resources/

{
  "data": {
    "title": "Refund policy",
    "content": "Returns accepted within 30 days...",
    "category": "policies",
    "status": "published"
  }
}
// → Stored + full-text indexed + vector embedded. One call.

3Search by meaning, keywords, or both — plus filters

// Flux API — api_prefix is your API's name, not a version
POST /my-api/knowledge/_search

{
  "vector_search": { "query": "how do returns work?" },
  "where": {
    "$": {
      "all_of": [{ "status__eq": "published" }]
    }
  }
}
// → Finds "Refund policy" via semantic match
// → Even though the query wording is completely different

Visual dashboard

No code required. Create schemas, manage content, and browse data through a visual interface — something vector databases don't offer.

Plain REST API

Don't use Python or JS? The knowledge base API is a standard REST API — works from any language, curl, or Postman.

AI Agents

Database for AI agents — read‑write knowledge store

AI agents don't just read — they learn. A database for AI agents needs a write path, not just a retrieval endpoint. FoxNose gives agents a full read-write knowledge store for LLM applications with structured schemas, versioning, and access control.

Create knowledge, update it, search by meaning or structure, and control who can access what — all through one API. Works with LangChain, CrewAI, and custom agent frameworks.

Agent workflow loop

AI Agent

WriteManagement API

Agent stores new knowledge — auto-embedded & indexed

SearchFlux API

Semantic + keyword + structured filters → relevant context

ReasonLLM ← context

LLM generates grounded, accurate response from retrieved data

Loop — agent learns continuously

Platform

Backend database for LLM apps — schema to API in minutes

Define your data model. FoxNose generates a complete REST API — CRUD endpoints, search, filtering, pagination. A backend database for LLM applications that eliminates boilerplate. Every write auto-generates embeddings. Every query can combine vector, text, and structured filters.

Structured + Vector: Typed schema with auto-generated embeddings. Not just vectors — structured data for RAG that your LLM can actually use.
Hybrid Search: Semantic, full-text, and filtered search in one query. The search layer is built into the LLM database — no Elasticsearch needed.
Auto-Embeddings: Mark fields as vectorizable. Embeddings generate on every save. No embedding pipelines, no sync scripts.
Schema-First API: Define your schema — get REST endpoints instantly. A backend database for LLM apps without writing backend code.
Governance Built In: Schema versioning, audit trails, RBAC. The only LLM data store with production-grade governance.
Serverless: No infrastructure to manage. A database for AI SaaS that scales with your product — from prototype to production.

Multi-tenant

Isolated environments per customer. Same schema, separate data.

Serverless

No servers to manage. Scales from 0 to millions of records.

EU hosting

GDPR-ready. Data stored and processed in the EU.

SDKs

Python and JavaScript SDKs. LangChain and CrewAI integrations.

SaaS Ready

Database for AI SaaS — multi‑tenant, serverless, scalable

Building an AI-powered SaaS product? You need a database for AI startup that handles multi-tenancy, search, embeddings, and governance from day one — not after your Series A. FoxNose is the ai-native database that grows with your product.

Environment isolation means each customer gets their own data boundary. Scoped API keys control access. Knowledge governance tracks every change. A production-ready LLM data store from the first user to enterprise scale.

Comparison

LLM database vs vector database vs Postgres

Vector databases handle embeddings. Postgres handles structured data. FoxNose handles both — plus search, governance, and auto-embeddings. One llm-ready database instead of three services and sync scripts.

Feature	Vector DB	Postgres
Structured schema + validation	~collection-level
Auto-embeddings on save	~Weaviate only	—
Hybrid search (vector + text + filters)	~Weaviate; DIY in others	~custom SQL + fusion
REST API from schema	—	—
Schema versioning (built-in)	—	—
Audit trail (built-in)	—	~pgaudit extension
RBAC & scoped API keys	~varies by vendor
Multi-language / localization	—	—
Vector similarity search		~pgvector extension
No ETL / sync between services	—	—
Serverless / fully managed		~Neon, Aurora
Environment isolation (built-in)	~namespaces	—

Built-in

~Possible with extensions or varies by vendor

—Not available

The LLM database.
Structured. Searchable. Production‑ready.

Get Started See RAG Example

The database for AI applications

Beyond vector databases — structured data for RAG

Database with built‑in vector search

Database for AI agents — read‑write knowledge store

Backend database for LLM apps — schema to API in minutes

Database for AI SaaS — multi‑tenant, serverless, scalable

LLM database vs vector database vs Postgres

The LLM database.
Structured. Searchable. Production‑ready.

Explore more

Knowledge Base API

Hybrid Search API

AI Knowledge Governance

Headless CMS

The database for AI applications

Beyond vector databases — structured data for RAG

Database with built‑in vector search

Database for AI agents — read‑write knowledge store

Backend database for LLM apps — schema to API in minutes

Database for AI SaaS — multi‑tenant, serverless, scalable

LLM database vs vector database vs Postgres

The LLM database.Structured. Searchable. Production‑ready.

Explore more

Knowledge Base API

Hybrid Search API

AI Knowledge Governance

Headless CMS

The LLM database.
Structured. Searchable. Production‑ready.