LLM inference, RAG and model APIs close to company data

Architecture for private LLM inference, RAG, embeddings, cache and APIs on DataHouse infrastructure.

LLM/RAG platform

LLM inference, RAG and model APIs close to company data

The biggest AI cost is often not the model itself, but a poorly designed data flow: indexes, embeddings, cache, queues, authorization, monitoring and no fallback plan.

Short answer

The biggest AI cost is often not the model itself, but a poorly designed data flow: indexes, embeddings, cache, queues, authorization, monitoring and no fallback plan.

RAG as a data layer

RAG needs sources, indexes, document versioning, access control and answers with source references. GPU alone does not solve knowledge quality.

Inference and latency

An LLM endpoint should be measured: first-token time, tokens per second, queue, timeouts, request cost and failure behavior.

GPU, CPU or API

Not every request needs GPU. Some tasks are better through a model API, some on CPU, with GPU reserved for heavy inference or batch work.

Security and logs

Private model APIs need logs, limits, data filtering, access roles, retention and a clear policy for which data may reach the model.

Practical checklist

  1. Define questions, knowledge sources, documents, access roles and required source citations.
  2. Choose the model path: API, local model, SPARC mini-model, CPU, GPU or hybrid architecture.
  3. Design indexes, embeddings, cache, queues, request limits and cost metrics.
  4. Separate the AI layer from the production database and prepare a model-free fallback.
  5. Test answer quality, hallucinations, latency, data access and log security.

Frequently asked questions

Does RAG always need GPU?

No. Indexing and smaller queries often run on CPU or via API. GPU is needed for heavy inference or larger scale.

Can data stay in Poland?

Yes. The architecture can use DataHouse infrastructure and restrict data flows according to client requirements.

What should be measured in LLM inference?

Latency, tokens per second, queues, errors, request cost, GPU/CPU usage, answer quality and sources.