Your RAG infrastructure.
Your data. Your rules.
Most RAG tools assume your data can leave your building. They default to cloud embeddings, hosted vector stores, and paid inference APIs — and every query is a data transfer event you'll need to explain to your legal team.
RAG Studio runs entirely on your own infrastructure. Local models only. No document, query, or response touches an external server. Configure multi-project pipelines through a dashboard, deploy with Docker, and call a REST API from your application. GDPR-compliant by architecture, not by checkbox.
The data exposure problem with standard RAG setups.
Most RAG pipelines route your documents through at least three external APIs: one for embeddings, one for vector storage, one for generation. Under GDPR, every one of those is a data transfer event that needs a DPA, a data flow record, and an answer when your regulator asks where your clients' documents went.
RAG Studio eliminates that chain entirely.
Every component runs locally on your infrastructure — embedding models, vector store, inference. Your documents are indexed locally. Your queries never leave your network boundary. You get the retrieval quality of a production RAG system without the compliance overhead of a cloud-dependent one.
Deploy a self-hosted 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.
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.
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.
Push documents via API
Send documents to the ingestion API. The worker queue processes them asynchronously — chunking, embedding, and indexing — without overwhelming the system.
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.
Full parametric control — per project, per pipeline.
Per-project pipeline templates
Define a template once — embedding model, vector dimensions, metadata schema, search type, chunk strategy — and reuse it across documents and projects. Each project has its own independent templates. Change any parameter without affecting other projects.
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.
Local model library — no API keys required
A vetted set of open-source embedding and sparse models — including BGE-M3 and SPLADE-v3 — bundled and runnable locally. No external API keys. No cloud inference. Switch models per project from the dashboard without touching config files.
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.
Built-in retrieval evaluation — precision, recall, MRR
Run retrieval quality metrics — precision@k, recall@k, Mean Reciprocal Rank — directly in the platform before going to production. Tune your template configuration with real numbers against your own data, not synthetic benchmarks.
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.
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.
Built for teams running RAG across multiple projects.
RAG Studio fits you if:
- → Sensitive documents: You handle sensitive documents — legal, financial, medical, operational — that cannot leave your infrastructure under GDPR or contractual obligations.
- → Clean REST API: You're building a product feature that uses RAG and need a clean REST API to call, not a managed service to depend on.
- → Multi-client management: You're a consultancy or development team managing RAG pipelines for multiple clients or use cases that need independent configuration and isolation.
- → Opinionated defaults: You've assembled RAG from open-source parts (LangChain, RAGFlow, LlamaIndex) and want the pipeline decisions already made — with the control to override them per project.
- → Built-in metrics: You want retrieval quality metrics without building an evaluation harness from scratch.
Not the right fit? It's probably not the right fit if you need a full LLM application platform with agents and workflow automation — tools like n8n or Dify cover that better.
Why not just use RAGFlow or LlamaIndex for free?
They're good tools. We've used both. Here's where they fall short for multi-project production deployments:
Locked embeddings & single workspace
RAGFlow locks your embedding model at dataset creation time. Change your mind later — you rebuild the dataset. Its default configuration also routes through paid API models; getting it fully local requires undocumented configuration. And it has no concept of multiple independently-configured projects — everything runs in one workspace.
Too much flexibility, zero opinions
LangChain and LlamaIndex give you maximum flexibility and zero opinions. That means you make every decision yourself: chunking strategy, embedding dimensions, hybrid search logic, evaluation harness. Each decision is a potential source of retrieval quality problems you'll debug in production.
The RAG Studio Advantage
RAG Studio makes those decisions for you, lets you override any of them per project, and gives you metrics to know when your configuration is actually working.
Flat subscription. No usage metering. No API billing as you scale.
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
- 1 project
- Up to 3 templates
- Full dashboard access
- Bundled model library
- Metrics subsystem
- Community + email support
Professional
- Unlimited projects
- Unlimited templates
- Full dashboard access
- Bundled model library
- Metrics subsystem
- Priority support + onboarding session
- Early access to new features
Enterprise
- Everything in Professional
- Custom SLA
- Dedicated onboarding & integration support
- Architecture review with Databik CTO
- Private Slack channel
Ready to request access?
Tell us about your use case and we will reach out for early access.