01
Private accounts do not belong in sensitive processes
Free or private accounts are not the same as a controlled company environment. Sensitive company data needs a setup with fitting contract terms and clear rules on data processing.
Cloud AI
Cloud AI when the task is demanding and the frame is right
Cloud AI gives access to especially capable models without you running GPU hardware of your own. For complex analysis, large document sets, code or demanding automations, models from OpenAI, Anthropic, Google or Microsoft are often ahead of local models.
In a company what counts is not the private chat, but a fitting API, a suitable contract and controlled data processing. I wire cloud AI into n8n, Microsoft 365 or existing systems, without sending confidential company data into a private chat without review.
Billing
A user plan or an API in the automation
ChatGPT Plus, Claude Pro or comparable plans are for individual users who work in the browser and upload files. For automation with n8n, ERP or your own apps, those plans are not the right technical base.
APIs usually bill by tokens used. Input and output are charged separately. Small models are enough for many classification and extraction tasks and can be very cheap. More capable models cost more accordingly.
Actual prices change regularly. The decision should therefore not rest on the price per token alone. What counts is how many requests arise, how large the inputs are and which model the task actually needs.
For automations the API is the right path. The user plan stays useful for work at the screen.
The problem
The private chat is not a company setup
Anyone pasting customer data, contracts or internal documents into a private AI chat without review quickly creates questions on data protection, internal policy and accountability.
A company therefore needs a controlled environment with a fitting contract, clear data processing and defined access rights.
02
A user plan does not replace an interface
A browser plan can be an excellent tool for staff. Automatic processing of emails, invoices or ERP data still needs an API.
03
Internal permissions must be right too
Cloud AI does not fix problems in your own file store. If staff can open too many SharePoint files, an AI assistant may pick those up as well. Permissions, data structure and access rights therefore belong before every AI rollout.
Vendors
Which cloud AI can make sense in a company
The brand alone does not decide. What matters is model, task, contract, data processing and region.
OpenAI
OpenAI offers capable models for text, code, analysis and multimodal work. For companies you have to distinguish a normal user account from an API or enterprise setup.
Anthropic
Claude fits especially well for demanding text work, code and tasks with large document sets. For companies, running it via fitting cloud platforms such as Amazon Bedrock or Google Vertex AI can be interesting.
Gemini offers large context windows and a close link to Google Workspace and Vertex AI. For companies with existing Google infrastructure that can simplify the integration.
Microsoft
Microsoft 365 Copilot sits directly in apps such as Word, Excel, Outlook, Teams and SharePoint. Azure OpenAI or Azure AI Foundry is aimed more at developers and automations that connect their own apps to AI.
Qwen, DeepSeek and other models
Open-weight models can also run via APIs. For some tasks they can be an interesting alternative. Before production, model quality, licence, data processing and compliance should still be checked.
The solution
Enterprise API instead of the browser tab
I integrate cloud AI into the existing process via a fitting API. Depending on the infrastructure you have, that may be Azure OpenAI, Vertex AI or another suitable vendor. Data processing, contract, region and access rights are part of the architecture from the start. The actual application stays as independent of the model vendor as possible.
That means: if the model or vendor later changes, ERP, SharePoint or n8n do not have to be rebuilt from scratch.
With US vendors additional legal questions remain. Processing in the EU does not automatically remove every question about the vendor and its legal space. For some companies an enterprise setup with EU processing is enough. Others need a fully local or European setup. Which variant makes sense depends on the concrete data risk and use area.
What I watch for
- API or enterprise setup instead of a private free account
- A DPA and clear rules on how data is used
- Processing in the EU if the use case requires it
- Send only the data the model actually needs
- Check access rights in Microsoft 365, SharePoint or other systems before rollout
- Plan human oversight for critical decisions
This is not legal advice and does not replace a review by a data protection officer or correspondingly qualified legal counsel.
Microsoft
Copilot and Azure OpenAI are two different approaches
Microsoft 365 Copilot works directly in apps such as Word, Excel, Outlook, Teams and SharePoint. Azure OpenAI or Azure AI Foundry is aimed more at your own apps and automated processes.
For automated incoming payments, document classification or an ERP process, an API setup is usually the better fit. Companies already on Microsoft 365 can then use existing infrastructure and contracts instead of building a completely new environment. What still decides are the concrete licence terms, data processing and internal permissions.
If you do not want data processed outside your own infrastructure at all, the fitting alternative is on the local AI page.
Gemini can also be wired into company processes via Google Workspace or Vertex AI. What decides here too is the chosen company setup, not a private Google account.
In practice
Who I recommend cloud AI to, and who not
Incoming payments show that the same task can be solved differently depending on the data risk.
01
Companies with fitting enterprise infrastructure
API via Azure OpenAI or Vertex AI, controlled data processing and only the information the model needs. Capable cloud models can be more reliable on complex matching than smaller local models.
02
Companies with especially sensitive finance data
Balances, IBANs and other sensitive information stay internal. A local model can handle the processing while n8n or the ERP stays on your own network.
03
Demanding analysis and agents
Very large document sets, complex code, multimodal content or demanding agents often benefit from capable cloud models.
04
Hybrid setups
Sensitive data is first processed locally. Only the information needed for the task, reduced as far as possible, is sent to a stronger cloud model.
Questions
Common questions about cloud AI
May company data be processed with cloud AI?
It depends on the concrete vendor, contract, data-processing model and use. For many companies, enterprise APIs with a DPA and controlled data processing are on the table. For especially sensitive data a local setup can be the better choice.
Is ChatGPT Plus enough for automation?
No. A user plan is meant for work in the browser. Automated processes such as invoice handling, email classification or ERP flows need an API and a fitting technical and contractual base.
Are enterprise setups automatically GDPR-compliant?
No. Enterprise features can create important preconditions, but they do not replace a review of the concrete use. Data flows, legal basis, access rights and internal processes have to be looked at as well.
Why do companies still use Microsoft or Google?
Because many companies already run Microsoft 365 or Google Workspace. A fitting enterprise AI setup can then integrate more easily, technically and organisationally.
When should you stay local?
When sensitive data must not leave your own infrastructure, or when a local model can already solve the task well enough. More on the local AI page.
When is cloud AI more economical?
When many users work at once, complex models are needed or you do not want to run AI hardware of your own. Then the cloud can be the more economical setup despite ongoing API costs.
Orientation
When local is the better fit and where data protection sits
Cloud models are stronger on many demanding tasks. Local AI in return gives maximum control over data processing.
The right decision therefore does not rest on whether cloud or local is better in general. What counts is which setup fits the process, the data risk and the budget.
Advantages of local AI
Prompts and files stay on your own hardware. There is no external AI vendor in the processing path. For structured tasks with sensitive data that can be the most sensible setup.
To local AIClarify the data risk first
Not every process needs AI and not every process needs a cloud model. The AI check places use case, data and human oversight before vendor and infrastructure are chosen. Orientation, not legal advice.
To the AI checkIn 15 minutes, see whether cloud AI makes sense for you
Process, data risk, existing Microsoft or Google infrastructure and the automation you want. Non-binding.
After that it is clearer whether a cloud API, local AI or a hybrid setup makes sense for your use case.