AI Infrastructure

AI Infrastructure

AI adoption is moving faster than most organizations expected. New use cases appear every month, teams are experimenting, and AI is becoming part of daily work across the business.

The challenge is that AI often enters the organization without the controls that exist for the rest of the technology landscape. As adoption grows, so do the questions. Which models are people using? What data is leaving the organization? How quickly will costs increase? And how do you avoid becoming dependent on a single provider?

We help organizations answer those questions by treating AI as an infrastructure capability, not a collection of disconnected tools. From governance foundations and AI landing zones to sovereign AI deployments and managed operations, we build the platforms that help you scale AI with confidence.

get your free AI & Azure assessment

AI adoption is moving faster than AI governance

People use AI because it helps them get work done. They summarize documents, find information faster, and analyze data. When approved tools do not meet those needs, public alternatives are only a browser tab away.

This creates a challenge many organizations recognize from the early days of shadow IT:

Teams adopt AI tools independently.
AI spending becomes fragmented across departments.
Data is shared with tools that were never formally approved.
Pilot projects quietly become production systems.
Costs increase faster than governance can keep up.

Policies remain important, but they are only part of the solution. Organizations also need platforms, controls, and processes that make the approved path easy to follow.

get your free AI & Azure assessment

What AI infrastructure helps you achieve

Control where AI runs

Choose the right environment for each workload, from external AI services to private or sovereign infrastructure running open-weight models.

Control what AI costs

Gain visibility into AI spend, forecast future consumption, and apply budgets before costs become surprises.

Control who uses AI and how

Understand which teams use which models, for what purpose, and at what cost.

Control how your data is handled

Keep sensitive information within approved environments and explore sovereign AI options when data residency and compliance matter.

One gateway, multiple AI models

Most organizations are quietly creating dependencies on several AI providers at once. Today's quick integration has a habit of becoming tomorrow's architecture. An AI gateway changes that.

Instead of connecting every application directly to a model, you create a single governed entry point. The gateway routes requests to the most appropriate model, applies policies, tracks usage, and helps prevent unnecessary costs.

That gives you flexibility today and freedom tomorrow. When a better model becomes available, you can adapt without rebuilding every integration.

When AI needs to stay closer to home

Not every workload needs to rely on a public AI service. Requirements around sensitive data, sovereignty or infrastructure control can make a more private approach relevant.

With LLM-as-a-service, organizations can run open-weight AI models on European GPU-based infrastructure, while an AI gateway provides governed access across the wider AI landscape.

The objective isn't to move everything to private AI. It's to create an architecture that gives you a meaningful choice about which workloads run where.

Look beyond today's AI costs

Many organizations already struggle to get a complete view of what AI costs today. Understanding what happens as adoption scales is even harder. AI spending is spread across models, tools, teams and use cases, while consumption continues to evolve as more people start using AI. What begins as experimentation can gradually become a significant operational expense.

Our AI & Azure assessment helps you understand:

what you're spending today
how that spend could evolve as usage grows
where optimization opportunities exist
where different model and deployment choices may make sense

Because effective AI governance isn't only about security. It's also about predictability.

get a better view of your AI costs

Managed Services & Operations

Platform Engineering

Microsoft Azure

AI

Why organizations choose delaware

AI strategy gets a lot of attention. But someone still has to build the platform, run it, secure it, and explain next month's bill.

Business-first approach

We start with governance, risk, cost, and business outcomes, not technology for technology's sake.

From design to operations

We help you assess, design, build, and operate AI platforms throughout their lifecycle.

Enterprise reality, not AI hype

We focus on practical decisions about security, costs, resilience, and adoption, supported by your own data.

One partner

Our infrastructure, platform engineering, security, cloud, and managed services teams work together to help you move from experimentation to enterprise-scale AI.

Free assessment: understand the true cost and health of your AI landscape

Get a free assessment of your Azure and AI environment. You'll receive insight into your current costs, future cost trajectory, architectural health, security posture, and opportunities to optimize.

Fill in the form on the right, and one of our experts will contact you within the next 48 hours.

Frequently Asked Questions

Organizations reduce shadow AI by providing approved, easy-to-use alternatives that offer the same convenience while maintaining governance and visibility.
AI infrastructure consists of the platforms, controls, governance mechanisms, and operational foundations that allow organizations to deploy and manage AI securely and at scale.
An AI landing zone is a governed environment designed specifically for AI workloads. It provides standardized security, compliance, networking, and operational controls.
The first step is visibility. By monitoring AI usage and forecasting future consumption, organizations can understand where costs originate and apply appropriate controls.
An AI gateway acts as a central access layer between users, applications, and AI models. It helps manage routing, governance, security, and cost control.
Sovereign AI refers to AI solutions that keep data, processing, and governance within approved jurisdictions or controlled environments.
It depends on your usage patterns. High-volume and repetitive workloads may reach a point where privately hosted models become more cost-effective. An assessment can help determine that crossover point.
Production-ready AI requires governance, security, monitoring, cost management, operational ownership, and a scalable platform foundation. This is where AI infrastructure and platform engineering come together.
Yes, we can: we offer GPU-based European AI infrastructure able to run top open-weight models and AI gateways