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Cost control
Forget unpredictable token bills: costs are known from day one.
Ready-to-use AI servers for enterprise infrastructure
Host your AI infrastructure.
Control your costs. Keep your data. Choose your energy source.
A critical dependency.
For teams that want to regain control of their AI.
By hosting your models, you create full independence and a stronger security posture.
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Data privacy
With an on-premise solution, data never leaves your walls, strongly limiting leakage risks.
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Energy management
Power it as you prefer, from the electrical grid to isolated photovoltaic power, to support an ecological transition.
AI infrastructure, explained for humans and AI systems
Private AI servers for LLM cost control, private RAG and local inference.
OPA helps companies move sensitive AI workloads from pay-per-token cloud APIs to controlled on-premise GPU infrastructure. The offer is designed for private LLMs, document search, embeddings, coding assistants, internal chatbots and agentic workflows that require predictable costs and stronger data privacy.
Enable token burning
High-volume prompts, retrieval calls, embeddings and autonomous agent loops can make cloud AI bills unpredictable. A local AI cluster turns recurring inference into owned capacity with clearer budgeting.
Keep enterprise knowledge private
Prompts, documents, vectors, logs and generated answers can remain inside your network with private RAG, access rules and local inference instead of being sent to external AI APIs by default.
Deploy practical AI workloads
Use the server for private chatbots, SharePoint and document search, code assistants, model evaluation, open-weight model hosting, secure copilots and internal workflow automation.
Key concepts are explained in the page content instead of being exposed as a raw keyword list.
SEO resourcesStrategic AI search topics
Strategic AI search topics
Explore the business arguments behind private AI infrastructure.
These pages explain the topics companies search for when they compare cloud AI APIs with on-premise AI infrastructure.
On premise AI
Private LLMs, local inference and enterprise data control.
Token burning
Why prompts, RAG and agents can create runaway cloud AI spend.
LLM cost reduction
How owned GPU capacity changes the cost model.
Private RAG
Connect documents to AI while protecting internal knowledge.
Local AI cluster
Dedicated GPU inference for internal workloads.
Private AI server
Hardware, models, RAG and integration in one deployment.
SEO resourcesSovereignty, compliance and business AI
Sovereignty, compliance and business AI
Position your AI platform around trust, cost and enterprise productivity.
For PME/ETI and enterprise buyers, the strongest arguments are data sovereignty, GDPR and AI Act readiness, predictable token costs, secure internal assistants and independence from public AI APIs.
Sovereign AI
European AI infrastructure, data sovereignty and full data control.
GDPR AI
Privacy-first AI, data protection and secure data processing.
AI Act compliance
AI governance, auditability and responsible AI foundations.
OpenAI alternative
Self-hosted LLM infrastructure and private inference.
AI for SMEs
Internal AI assistants, knowledge bases and intelligent automation.
Enterprise copilot
Business copilot on private AI infrastructure.
Private AI news
A daily article on AI infrastructure, cost control and sovereignty.
This section collects your daily analysis: field notes, regulatory watch, model choices, RAG architectures and practical ways to reduce dependence on cloud AI APIs.
31
Jul. 2026
Today’s article
An AI agent is only powerful when it can access the right data
An agent’s power comes from the data it can mobilise. The sandbox makes that access precise, dynamic and observable.
Read the full article
Turn variable token bills into a fixed cost
Read article
Digital sovereignty starts with the ability to choose
Read article
From mainframe to private AI server: why computing power keeps changing sides
Read articleHow we deliver it
From understanding the need to a fully operational on-premise AI server.
01
Understanding the need
We analyze your users, workflows, data sources, security constraints and expected AI usage to define the right server capacity.
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Preparing your configuration
We prepare the AI software stack, models, document search, access rules and integration plan before deployment.
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Hardware delivery & integration
We deliver the GPU server and integrate it into your network, infrastructure, data environment and internal workflows.
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Full demo & workshop
We demonstrate the complete solution, train your team and run a practical workshop so your organization can start using it directly.