Building an on-premise RAG architecture: components, stack and approach
Running RAG without data leaving your house takes the right building blocks and an architecture where everything fits together. This guide walks through the components, the pitfalls and the path to production.
Why on-premise
Data sovereignty is not a nice-to-have
Cloud RAG is quick to set up, but for sensitive company data it is often not an option. On-premise keeps the data in house, but it calls for deliberate architecture decisions.
Data sovereignty
Confidential documents never leave your infrastructure. Nothing flows out to external models or providers.
GDPR and compliance
Industries with strict requirements need provable control over how data is stored and processed.
No vendor lock-in
An open architecture can keep evolving instead of being tied to a single provider.
Cost control
At high volume, your own infrastructure can be cheaper and more predictable than usage-based cloud APIs.
The building blocks
The components of an on-premise RAG architecture
A production-ready RAG pipeline is made up of several building blocks that have to work together.
Ingestion and chunking
Documents are read in, normalized and split into meaningful sections.
Embedding model
A locally run model turns text into vectors, without any external API.
Vector database
pgvector, Qdrant or Vespa store the embeddings and enable fast semantic search.
Retrieval and reranking
Hybrid search and reranking make sure the most relevant passages are found.
Local LLM
A self-hosted language model generates the answer based on the retrieved content.
Orchestration
A layer connects the building blocks, controls prompts and enforces source grounding and context limits.
Hardware and models
What it takes to run
On-premise means you run the models yourself. How much hardware you need depends on volume, latency requirements and model size. We help you make a realistic choice.
- GPU requirements depending on model size and throughput
- Open Source LLMs as an alternative to proprietary APIs
- Container orchestration for stable operation
- Scaling to match actual load
Build it yourself or with QUIKK
Two paths to an on-premise architecture
Build it yourself
With the right know-how and time, you can build the architecture in house. This guide gives you the structure.
With QUIKK
We bring the experience from building our own RAG and shorten the path from architecture to a system running in production.
RAG workshop
In the workshop your team builds the pipeline itself and learns to justify and measure every decision.
Review and audit
Already have a system? We assess the architecture and retrieval quality and point out improvements.
Pitfalls
Where on-premise RAG often fails
Setting up the individual building blocks is doable. The real effort is in how they work together and in quality assurance.
- Chunking that breaks apart related content
- Retrieval quality that nobody measures
- Hallucinations with no source grounding
- Missing evaluation as the system evolves
Approach
From architecture to operation
Architecture workshop
We make the key decisions: chunking, retrieval, reranking, models and operating model, documented and ready to implement.
Proof of concept
The architecture runs on your real data and is evaluated quantitatively.
Production and operation
Integration, monitoring and scaling, on-premise or in your own cloud.
FAQ
Common questions about on-premise RAG
What hosting do I need for on-premise RAG?
Your own servers or your private cloud with enough GPU capacity. The exact requirement depends on model size, volume and latency needs.
Which models are an option?
Open Source LLMs and embedding models that can run locally. We select them based on quality, latency and cost.
What about data protection?
With on-premise, your data never leaves your infrastructure. Nothing flows out to external providers, which makes GDPR and strict industry requirements easier to meet.
Is on-premise worth it compared to the cloud?
For sensitive data or high volume, often yes, both from a compliance and a cost perspective. We assess both neutrally.
Can we start small?
Yes. We start with a clearly scoped PoC and only scale once the architecture holds up.
More on RAG
Architecture workshop: a decision in a single day
Book an architecture workshop. By the end you have a documented decision on chunking, retrieval, models and operating model, tailored to your infrastructure.
Let’s talk about how AI can move your business forward.
Get in touch, we look forward to your project.