In vendor documentation and offers, differences usually concern not the model itself but how data processing works, who is responsible for security and whether the contract allows control on the company's side. Based on practical vendor checklists and materials about choosing an AI vendor, it is worth starting with three things: evaluation criteria, DPA terms and data location, rather than the product feature list.
According to Webalize and TechHuman, a sensible vendor assessment should cover not only deployment but also integration, security, quality testing and post-launch maintenance. This matters because in practice “AI for business” works only when it is possible to verify what the system does with data, how it reacts to errors and who can stop or correct it.
Webalize says a good vendor should be able to go through the whole process: from discovery, through POC and pilot, to rollout and post-deployment support. TechHuman adds operational criteria: quality of tests, data and privacy, security and human control, integration with company architecture, continuity of operations, total costs and the ability to exit the solution without operational lock-in.
In practice, ask about:
Warp and Allata also highlight ownership of data and models, lack of vendor lock-in, choice of infrastructure and price transparency as particularly important. This is a good filter when comparing offers, because the most expensive part of deployment often appears not in the initial project but later - in maintenance, tokens, integrations and model changes.
If AI will process personal data, the question about the DPA should be raised at the start, not at the end of commercial talks. The material from TwojaFirmaWAI explicitly states that the possibility of concluding a data processing agreement can open or close the path to deployment, and alongside it one should check how data are used, retention time and processing location.
According to TechHuman and Engineered With AI, it is necessary to determine where processing takes place, whether input data are retained by the vendor and whether they can be used to improve models. In practice, request a clause covering:
The best answer is not a general assurance of “security” but documentation that can be verified: user roles, access logs, two-factor authentication, integration with the corporate identity provider and data deletion policies. If a vendor cannot clearly answer whether data go into training, where they are processed and under what conditions a DPA can be signed, the deployment risk grows faster than the benefit from automation itself.
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Source: https://webalize.me/blog/how-to-choose-ai-automation-vendor