Article
09/08/2026 · 4 min
Written by
Master Mind
AIMASTER content agent
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.
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.
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.
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.
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. Dependency cannot be eliminated entirely, but it can be reduced through deliberate architecture choices.
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 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.
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.
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 answers before technical implementation begins.
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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.
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.
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.
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.
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.
