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. Your data stays in the EU, invoices carry EU VAT with B2B reverse charge, and GDPR compliance is there 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 EU VAT invoicing. 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 — RTX PRO 4000 and RTX PRO 6000
Professional NVIDIA cards with a ready CUDA environment. For fine-tuning, LLM inference, computer vision and 3D rendering. Monthly or annual billing, with 10 DevOps hours included every month. 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. Prices come live from our current plans, excluding 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 240 EUR |
| Semantic search for a shop (50k SKUs) | VPS 16G + pgvector + Sentence Transformers | from 100 EUR |
| Llama 3 70B inference (~5k queries/day) | GPU server RTX PRO 6000 + vLLM | from 4,000 EUR |
| Training your own OCR model (CV) | GPU server RTX PRO 4000 (monthly) | from 800 EUR |
| Production LLM 70B with load balancing | 2× GPU server RTX PRO 6000 + Kubernetes | from 8,000 EUR |
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. Low response latency. Multilingual embeddings, tokenizer tuned to your language.
Semantic search
Instead of keyword search — search by meaning. E-commerce customers see 30%+ conversion lift. Handles inflected languages 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 PRO 4000 (24 GB), suited to models up to around 14B parameters. Llama 3 70B needs RTX PRO 6000 (96 GB). Mixtral 8x22B and larger: several GPU servers. 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. RTX PRO 4000 (24 GB) covers light fine-tuning of small models. Full training of GPT-class models does not fit on a single GPU server.
Can I use Pinecone while Qdrant is with you?
Yes. Pinecone is an external service billed by usage; Qdrant runs on your own VPS in the EU, the data never leaves your server and the cost is the VPS fixed monthly price. We handle the Pinecone to Qdrant migration from your included DevOps hours.
Pick a GPU or VPS with a vector database
GPU servers with RTX PRO 4000 or RTX PRO 6000 and VPS with preinstalled Qdrant; current prices are on the plans page. GPUs billed monthly or annually, with 10 DevOps hours a month.
See GPU plans →