LLM inference, RAG і model API близько до company data

Architecture for private LLM inference, RAG, embeddings, cache і API on DataHouse infrastructure.

LLM/RAG platform

LLM inference, RAG і model API близько до company data

The biggest AI cost is often not model itself, but poor data flow: indexes, embeddings, cache, queues, authorization, monitoring і no fallback plan.

Коротка відповідь

The biggest AI cost is often not model itself, but poor data flow: indexes, embeddings, cache, queues, authorization, monitoring і no fallback plan.

RAG as data layer

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

Inference і latency

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

GPU, CPU або API

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

Security і logs

Private model API needs logs, limits, data filtering, access roles, retention і clear policy which data can reach the model.

Практичний чеклист

  1. Define questions, knowledge sources, documents, access roles і required source citations.
  2. Choose model: API, local model, SPARC mini-model, CPU, GPU або hybrid architecture.
  3. Design indexes, embeddings, cache, queues, request limits і cost metrics.
  4. Separate AI layer from production database and prepare fallback without model.
  5. Test answer quality, hallucinations, latency, data access і log security.

Найчастіші питання

Чи RAG always needs GPU?

Ні. Indexing і smaller queries often run on CPU or API. GPU is needed for heavy inference or bigger scale.

Can data stay in Poland?

Yes, architecture can use DataHouse infrastructure and restrict data flows.

What to measure in LLM inference?

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