Production-grade AI infrastructure
Vector databases and GPU servers are the two pillars of modern AI stacks. We have both. Qdrant or ChromaDB for embeddings, RAG and semantic search; GPU servers for training, fine-tuning and inference of your own models. For Polish companies: data stays in the EU, VAT invoices in PLN, GDPR compliance from minute one.
Vector databases — RAG, embeddings, search
Vector databases are the foundation of LLM-powered apps. They store document embeddings and find similarities in milliseconds. Pick your preferred database — we host all of them in the EU with VAT invoicing in PLN. Each comes with a preinstalled Python client and example Jupyter notebooks.
Qdrant
Rust-based, high performance, production-ready. Ideal for RAG apps with millions of embeddings. HNSW index, metadata filtering, snapshots.
ChromaDB
Easiest-to-use vector database. Great for prototypes and smaller AI apps. Python-first, automatic embeddings from OpenAI or Sentence Transformers.
Weaviate
Data schema with metadata. Hybrid search (vector + tag filter). Built-in generative AI modules, GraphQL API.
pgvector
PostgreSQL extension. If you already use Postgres, add vectors without a new database. Full integration with existing tables and SQL transactions.
GPU servers — H200, RTX PRO 6000, RTX 4090
Professional NVIDIA cards with a ready CUDA environment. For model training, LLM inference, computer vision and 3D rendering. Hourly or monthly billing — pick what fits your workflow. All GPUs come with CUDA 12.4, cuDNN 9, PyTorch 2.4, TensorFlow 2.18, JAX, Hugging Face Transformers preinstalled.
Cost of typical AI workloads
Example scenarios and their monthly cost on our infrastructure. All prices in PLN, including 23% VAT. Compared to OpenAI / AWS Bedrock / Anthropic rates — in many cases your own infrastructure pays off at 50-100k requests per month.
| AI workload | Setup | Cost/month |
|---|---|---|
| RAG for 1M documents (doc chat) | VPS 32G + Qdrant + OpenAI API | from 229 PLN |
| Semantic search for a shop (50k SKUs) | VPS 16G + pgvector + Sentence Transformers | from 119 PLN |
| Llama 3 70B inference (~5k queries/day) | GPU server RTX PRO 6000 + vLLM | from 1,990 PLN |
| Training your own OCR model (CV) | GPU server RTX 4090 (hourly) | 12 PLN/hr |
| Production LLM 70B with load balancing | 2× GPU server H200 + Kubernetes | from 7,990 PLN |
Production AI pipeline — 5 stages
Here's what a typical production AI project looks like from concept to running app. For each stage we have ready infrastructure and patterns you don't need to invent from scratch.
Data preparation
Document indexing, text cleanup, normalization. Python scripts on a VPS, output to MinIO (S3-compatible) or PostgreSQL.
Embedding generation
OpenAI text-embedding-3-large, Cohere, or a local model (Sentence Transformers, BGE-M3). Embeddings written to Qdrant or pgvector.
Retrieval + RAG
A FastAPI/Next.js app sends a question, the vector DB returns top-K docs, context is appended to the LLM prompt.
LLM inference
Pick: Claude/GPT via API (fastest start), or a local LLM on GPU (full control, lower cost at scale).
Monitoring and evaluation
Query logs in Loki, latencies in Prometheus, OpenAI cost in Grafana. Quality eval via LangSmith or your own benchmark.
What you can build
Company assistant (RAG)
Index company docs in Qdrant, connect Claude or GPT via API, return context-aware answers. Sub-200 ms latency. Polish tokenizer, multilingual embeddings.
Semantic search
Instead of keyword search — search by meaning. Polish e-commerce sees 30%+ conversion lift. Handles Polish noun inflections gracefully.
Hosted LLM (Llama, Mistral)
Your own model on a GPU server. No data sent to OpenAI/Anthropic. Full control, GDPR-compliant. vLLM or Ollama for easy inference.
Computer vision in production
Defect detection, OCR, object recognition. Train on your own dataset with an RTX PRO 6000. YOLO, Detectron2, Segment Anything Model.
GDPR compliance for AI apps
AI is a sensitive area for GDPR — training data, embeddings, query logs. Our solution minimizes risk by keeping all data in the EU and giving you full control over processing.
- All data (documents, embeddings, logs) in EU DCs — no transfer to the US, UK or Asia
- Option to use local LLMs (Llama, Mistral) instead of OpenAI/Anthropic — no data transfer to the US
- Query logs retained max 90 days, with on-demand earlier deletion
- Embeddings are derived data — full deletion possible by re-indexing after source anonymization
- Audit log for every vector DB access (who, when, what query)
- Free Data Processing Agreement (DPA) compliant with GDPR Article 28
Frequently asked questions
Can I use OpenAI / Anthropic with a vector DB in the EU?
Yes. You keep the vector DB (Qdrant/ChromaDB) with us in the EU. To the LLM you send only the top-K context + user question. Our examples show how to minimize data transfer.
How large a model can I run on your GPU?
Llama 3 8B / Mistral 7B run smoothly on RTX 4090. Llama 3 70B needs RTX PRO 6000 or H200. Mixtral 8x22B and larger — H200 or a multi-GPU setup. We help pick.
How long does AI environment setup take?
The environment is preinstalled — CUDA, PyTorch, TF, vLLM, Ollama. First inference in 5 minutes. For custom setups (specific model, fine-tuning) typically 1-3 hours with our help.
Do you support fine-tuning custom models?
Yes. RTX PRO 6000 (96 GB VRAM) handles fine-tuning of up-to-70B models with LoRA/QLoRA. For full training of GPT-class models — H200 or a multi-node cluster.
Can I use Pinecone while Qdrant is with you?
Yes, but Qdrant locally is cheaper and faster. Pinecone is ~$70/mo minimum; Qdrant on our VPS 16G is 119 PLN/mo. Pinecone → Qdrant migration — we help for free.
Pick a GPU or VPS with a vector database
GPU servers from 1,290 PLN/mo. VPS with preinstalled Qdrant from 119 PLN/mo. Hourly billing on GPU — try without commitment.
See GPU plans →
