# Cloud Adoption Is Accelerating in the EU – How Growth Companies Assess Vendor Dependency Before an AI Project

> 52.74% of EU firms used cloud services in 2025 (Eurostat). How growth companies assess cloud vendor dependency before an AI project with Master Layer.

- Published: 2026-08-09
- Author: Master Mind
- Canonical: https://aimaster.fi/en/artikkelit/pilvipalveluiden-kaytto-kasvaa-eussa-nain-kasvuyritys-arvioi-toimittajariippuvuu

52.74% of EU enterprises used paid cloud computing services in 2025 – up 7.42 percentage points from 2023 (Eurostat, 2026). A growing share of a growth company's data, systems, and now AI agents live on the infrastructure of one or a handful of external vendors.

That is not a problem by itself. The problem starts when a growth company builds AI capability without knowing what it is committing to at the same time – and what happens if that vendor raises prices, changes terms, or discontinues the service.

## Why is cloud vendor dependency now a core AI strategy question?

When a company deploys an AI agent, it is not just using a cloud service – it is anchoring a business process on top of it. The agent needs a model, compute, and often the vendor's own proprietary interfaces. The deeper the agent integrates, the harder and more expensive it becomes to switch vendors later.

The same pattern shows up at scale: cloud adoption in the EU is growing faster than ever. According to Eurostat, the increase from 2023 to 2025 was 7.42 percentage points – the largest jump recorded to date. As usage grows, so does dependency, unless it is managed deliberately.

## What does vendor dependency actually mean day to day?

Vendor dependency means a company's data, models, or processes are locked to one cloud or AI provider so tightly that switching is not feasible within a reasonable time or cost. In practice it shows up in three ways: data is stored in a proprietary format, integrations are built only against one vendor's interfaces, and staff skills concentrate around a single platform.

A growth company does not need to avoid cloud services – that is not realistic. It needs to know exactly what it is committing to, and structure its data so it can be moved when necessary.

## How does a growth company assess its own dependency?

Assessing dependency starts with three questions: In what format is the data stored? Can it be exported in a standard format without the vendor's proprietary tools? And what happens to the business process if the vendor cuts the service off unexpectedly?

| Risk factor | Low dependency | High dependency |
| --- | --- | --- |
| Data storage format | Open, standard (e.g. SQL, CSV, JSON) | Vendor's proprietary closed format |
| Integrations | Built through a middle layer | Built directly against one vendor's API |
| Model usage | Multiple models or alternatives tested | One model, no fallback plan |
| Export feasibility | Data movable within days | Export would require a months-long project |

This is the same logic that drives a multi-model strategy for AI models – see [One AI Model Is Not Enough](https://aimaster.fi/artikkelit/yksi-tekoalymalli-ei-riita-nain-kasvuyritys-rakentaa-moni-malli-strategian-toimi). Dependency cannot be eliminated entirely, but it can be reduced through deliberate architecture choices.

## Why is the fix a data layer, not a vendor switch?

The most common mistake is trying to solve dependency by switching vendors – which usually just shifts the problem to a different vendor. A durable fix is to build a layer in between that keeps data and integration logic under the company's own control, regardless of which cloud or AI model runs underneath it.

[Master Layer](https://aimaster.fi/tuotteet/master-layer) is a data foundation layer that connects a company's existing systems (CRM, ERP, documents) securely for AI to use. It acts as a buffer between company data and an external AI vendor: data stays manageable and portable, even if the model or cloud provider changes.

In practice, this means a growth company can adopt a new AI agent or model without rebuilding the entire integration. Dependency shifts from an entire external vendor to one managed layer the company owns itself.

## Where to start if dependency has never been mapped?

The first step is not a technology switch but a mapping exercise: where does the data live, in what format, and which processes would collapse if a single vendor disappeared tomorrow. This mapping is part of a broader AI strategy – see [Data Inventory Before an AI Project](https://aimaster.fi/artikkelit/data-inventaario-ennen-tekoalyhanketta-nain-kasvuyritys-kartoittaa-mita-dataa-si).

Once mapped, the next step is prioritization: which systems should be opened behind the data layer first, and which produces the most value measured in euros. That is exactly the question [Master Plan](https://aimaster.fi/tuotteet/master-plan) answers before technical implementation begins.

## Frequently asked questions

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## Frequently asked questions

### What does cloud vendor dependency mean in practice?

Cloud vendor dependency means a company's data, models, or integrations are locked to one provider so tightly that switching is not feasible within a reasonable time or cost. It stems from proprietary data formats, vendor-specific interfaces, and skills concentrated around a single platform.

### Should a growth company avoid cloud services to reduce dependency?

No. Cloud adoption in the EU is growing fast (52.74% of enterprises in 2025, Eurostat), and avoiding it is not a realistic strategy. What matters more is knowing exactly what you are committing to and structuring data so it can be moved when needed.

### How does Master Layer reduce vendor dependency?

Master Layer is a data foundation layer that connects a company's existing systems securely for AI to use, acting as a buffer between company data and an external AI vendor. Data stays manageable and portable even if the underlying model or cloud provider changes.

### Where should a growth company start assessing its dependency?

The starting point is a data inventory: where the data lives, in what format, and which processes would collapse if a single vendor disappeared tomorrow. After mapping, prioritize which systems to open behind the data layer first.

### Is relying on a single AI model a dependency risk?

Yes. Committing to one model without a fallback plan increases dependency in the same way as committing to one cloud vendor. A multi-model strategy combined with a well-built data layer reduces both risks at once.