When should you choose private AI?
Your employees already use ChatGPT or Copilot, with or without a policy. The question is not whether you use AI, but which data leaves the building when you do. Four questions decide whether a public service is enough, or whether you will soon have to explain something to the auditor that you cannot explain.
Four questions that decide it
01
How much text and data is involved?
AI pays off where a lot of text or unstructured data is involved: email, case files, documents, legislation. A process with fixed rules or a dashboard is just software; it does not need AI.
02
May this sit with a US provider?
Client files, personal data, contracts, or knowledge that defines your competitive position do not belong with a US provider. Public information and marketing copy do.
03
Can you tell the auditor where your data is?
For ISO 27001 or a supplier assessment, you need to explain where your data is stored and who has access. If it sits outside the EU, you have no conclusive answer for an auditor, municipality, or client.
04
How consistent does it need to be?
Should the same question still give the same answer in six months? Public providers switch models on their schedule. With private AI, the same model runs until you decide to switch.
Private AI or ChatGPT, Copilot, and Azure OpenAI?
Public AI is not wrong. The difference is where your data lives and who makes the decisions.
Where your data lives
Legislation
Costs
Who controls the model
Model quality and pace
Entry
When public AI is perfectly fine
For public information, marketing copy, or general writing without client or company data, a public service is often sufficient. Private AI pays off as soon as AI works with confidential text or data: client files, contracts, procedures, or trade secrets.
Still unsure which way to go after this comparison? Tell us about your situation.
Schedule a no-obligation callWhat does private AI cost?
A custom AI project starts at €25,000, depending on the application. We start with a single process and expand once the return is proven. Some solutions are available as SaaS with a monthly subscription, which lowers the entry point.
Considering building something yourself with Claude, Codex, or Copilot? Read when building it yourself makes sense
What it delivers in practice
Four applications run in production on our own GPU cluster in the Netherlands, from support tickets and sales to a knowledge assistant for municipalities with 800,000+ legal articles.
View the applications →Frequently asked questions
What are the risks of doing nothing?
Employees often already paste client emails, quotes, and case files into ChatGPT. That is a data leak you cannot see, a GDPR risk, and for municipalities a BIO question. Your own environment makes that use safe instead of forbidden.
Can we use public and private AI side by side?
Yes. Many organisations use public AI for general writing and private AI for anything involving client or company data. What matters is that employees know which data may go where.
How quickly can we start?
On Wabber's shared cluster, quickly; a dedicated or on-premise environment needs more preparation. In a first conversation we decide which process and which model fit.
Are we locked in to Wabber afterwards?
No. Private AI runs on open-source models. You decide when a model is replaced, and you are not bound to the terms of a single vendor. And if a public provider retires a model or changes its prices, it does not affect you. Want to bring the environment in-house later? That is open for discussion: you buy and host the hardware, we set it up and maintain it.
Does Wabber also use public AI?
Yes, when that is responsible and sensible. For public information or writing without company data, we choose a public model too. We only recommend private AI where it pays off.
Do you know which data leaves the building today?
Visit us in Etten-Leur: we show you what runs in production with us and tell you honestly whether private AI pays off in your situation.