AI Infrastructure

Artificial intelligence is moving from experiment to production — but public API dependencies create cost unpredictability, data sovereignty concerns and vendor lock-in. innade.cloud AI infrastructure lets you deploy large language models, run GPU-accelerated workloads and host vector databases on private infrastructure in EU data centres. Keep your data under your control while building AI-powered products on production-proven cloud foundations.

Ideal For

Companies requiring data sovereignty for AI workloads
Teams deploying private LLMs instead of public APIs
Startups building AI-native products and features
Enterprises with GDPR-sensitive AI applications
Developers running RAG pipelines and vector search
Organisations seeking predictable AI infrastructure costs

Overview

AI workloads have unique infrastructure demands: GPU compute for model inference and training, high-memory instances for large models, fast storage for model weights and datasets, and vector databases for retrieval-augmented generation. Generic cloud hosting is not designed for these requirements.

innade.cloud AI infrastructure provides the specialised compute, storage and networking your AI projects need — integrated with the broader platform for databases, containers, monitoring and security. Deploy open-source models like Llama, Mistral and Stable Diffusion on dedicated GPU instances. Host vector databases for semantic search and RAG applications. Expose AI capabilities through private APIs without sending sensitive data to third-party providers.

Whether you are fine-tuning a model, running inference at scale or building a complete AI product, our engineers understand both the infrastructure and the application layer — because OSCA Solutions builds AI-assisted features into its own products on this same platform.

What's Included

GPU computeDedicated GPU instances for model training, fine-tuning and inference.
LLM deploymentHost open-source and custom models with managed serving infrastructure.
Vector databasesManaged vector storage for embeddings, semantic search and RAG pipelines.
AI APIsPrivate API endpoints for model inference without third-party data exposure.
High-memory instancesRAM-optimised servers for large model loading and batch processing.
Container supportDeploy AI workloads in Docker or Kubernetes with GPU passthrough.
Model storageFast SSD storage for model weights, datasets and checkpoint management.
Monitoring & scalingGPU utilisation tracking and auto-scaling for inference workloads.

Benefits

Private AI infrastructure gives you control over cost, data and performance — advantages that become critical as AI moves from prototype to production.

Data sovereigntyKeep sensitive data in EU data centres, never sent to external AI APIs.
Cost predictabilityFixed infrastructure costs instead of per-token API billing.
Low latencyLocal inference without round-trips to remote API endpoints.
Model flexibilityRun any open-source or custom model without provider restrictions.
Platform integrationConnect AI workloads to managed databases, monitoring and security.

Common Use Cases

Private chatbot and assistant deployment
Document analysis and summarisation
RAG-powered knowledge bases
Code generation and review tools
Image generation and processing
Custom model fine-tuning

Technologies

NVIDIA GPUs Llama / Mistral PyTorch / TensorFlow Ollama / vLLM Vector DBs Docker / Kubernetes Python / CUDA

We support the AI tooling ecosystem your team already uses — from Hugging Face model hubs to LangChain orchestration frameworks. GPU instances are available with NVIDIA drivers pre-installed, and our container platform supports GPU passthrough for flexible deployment options.

Grow with innade.cloud: AI infrastructure builds on Cloud Hosting & Compute for base compute, Managed Kubernetes for orchestrating AI microservices at scale, Managed Databases for vector and traditional data stores, and Managed Security to protect AI endpoints and sensitive model data.

Frequently Asked Questions

Can I run my own LLM instead of using OpenAI or similar APIs?

Yes. That is a primary use case for our AI infrastructure. Deploy open-source models like Llama, Mistral or custom fine-tuned models on dedicated GPU instances with private API endpoints — your data never leaves your infrastructure.

What GPU options are available?

We offer NVIDIA GPU instances suitable for inference and training workloads. Specific GPU models and availability depend on data centre capacity — contact us to discuss your compute requirements and we will recommend the right configuration.

Do you support vector databases for RAG applications?

Yes. Vector database hosting is available for embedding storage and semantic search. These integrate with your LLM deployment for retrieval-augmented generation pipelines — a common architecture for private knowledge bases and document Q&A systems.

Is my data processed within the EU?

Yes. AI infrastructure runs in EU data centres. When you deploy private models, all inference and data processing occurs on your dedicated infrastructure — no data is sent to external AI providers.

Can I start small and scale up?

Absolutely. Begin with a single GPU instance for prototyping, then scale to multi-GPU clusters or Kubernetes-orchestrated inference services as your AI product grows. The innade.cloud platform supports this progression without changing providers.

Topics

AI infrastructure GPU hosting LLM deployment private AI vector database RAG hosting EU AI hosting

Building with AI?

Deploy models on infrastructure you control — private, scalable and EU-hosted.

Explore AI Infrastructure