Ship RAG into your product
without the infrastructure complexity.

Setting up retrieval-augmented generation today means hiring a specialised AI engineer, stitching together vector databases, embedding models, rerankers, and queues — then maintaining all of it. RAG Studio replaces that with a self-hosted platform you configure through a dashboard, deploy with Docker, and plug into your app via API.

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Docker deploy

Runs on your server

Your data, your infra

REST API integration

RAG Studio · project: carrier-docs

Project overview

documents

1,284

↑ 48 this week

avg precision@5

0.91

↑ from 0.84

Active template

hybrid-v2 active

dense_model bge-m3

sparse_model splade-v3

search_type hybrid + rerank

top_k 8

// how it works

From zero to a working RAG pipeline in an afternoon.

RAG Studio handles the infrastructure layer. You focus on configuring what matters for your use case and calling the API from your application.

01 // deploy

Pull and run with Docker

One docker-compose command spins up the full stack — dashboard, backend, vector database, Redis, and ingestion workers — on your own server.

02 // configure

Create a project and template

In the dashboard, create a project for your use case. Define a template: choose your dense and sparse embedding models, metadata fields, search type, and reranking.

03 // ingest

Push documents via API

Send documents to the ingestion API. The worker queue processes them asynchronously — chunking, embedding, and indexing — without overwhelming the system.

04 // query

Query from your application

Call the query endpoint from your app. RAG Studio returns ranked, relevant chunks ready to pass to your LLM. Your app controls access — RAG Studio handles retrieval.

// capabilities

templates

Reusable project templates

Define a template once — embedding models, metadata schema, search configuration — and reuse it across documents and projects. Ship examples included so you're not starting from scratch.

retrieval

Hybrid search out of the box

Combines dense vector search with sparse (BM25-style) retrieval, plus cross-encoder reranking. Better recall and precision than single-method search — configured in one toggle.

models

Curated open-source model library

A vetted set of open-source embedding and sparse models — including BGE-M3 and SPLADE-v3 — bundled and manageable directly from the dashboard. No manual binary wrangling.

ingestion

Queue-based ingestion

Documents are processed through a worker queue backed by Redis. High-volume ingestion doesn't saturate the system — jobs are distributed and tracked with status visibility in the dashboard.

metrics

Built-in RAG evaluation

Run retrieval quality metrics — precision, recall, MRR — directly in the platform before going to production. Know whether your configuration is actually working, not just hope it is.

access control

Your app owns document access

RAG Studio handles retrieval. Document-level access control stays in your application layer — no lock-in, no opinionated auth model imposed on your architecture.

// what's in the license

Everything you need to run RAG in production.

One flat subscription covers the full platform. No usage metering, no per-query charges, no surprise bills as you scale.

Dashboard & control panel

Full UI to manage projects, templates, models, ingestion jobs, and metrics. No CLI required for configuration.

RAG orchestration backend

The core engine — query handling, chunking, embedding pipeline, reranking, and response assembly. Fully self-hosted.

Vector database & Redis

Pre-configured vector store and Redis instance bundled in the Docker compose. No separate procurement or setup.

Curated embedding model library

A set of open-source dense and sparse models — upload, activate, and switch between them from the dashboard.

Metrics & evaluation subsystem

Run retrieval quality benchmarks against your own data. Iterate on template config with real numbers, not guesswork.

Onboarding & technical support

Hands-on setup support from the Databik team. We've built this in production — we'll help you get there faster.

// pricing

Flat subscription. No usage metering.

Pay once per period and run as many queries and documents as your server can handle. You own the infrastructure — we provide the platform.

Starter

€299 / month

For developers evaluating or building their first RAG feature.

1 project
Up to 3 templates
Full dashboard access
Bundled model library
Metrics subsystem
Community + email support

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Professional

€699 / month

For teams running RAG in production across multiple use cases.

Unlimited projects
Unlimited templates
Full dashboard access
Bundled model library
Metrics subsystem
Priority support + onboarding session
Early access to new features