We have published a comparison on the SAIE website showing how our approach differs from open-source solutions and hyperscaler services. When an organization from a regulated sector chooses an AI platform, it quickly discovers that two of the three available paths come with built-in limitations that cannot be worked around
Open source is limited by accountability. The code is transparent, but maintenance, security, compliance and development fall entirely on the organization's own team. There is no service, no guarantee and no other party to the contract. For a company, this is not a platform — it is a project it has to run on its own, indefinitely
A hyperscaler is limited by control. Data flows through the provider's infrastructure, model selection ends with its catalogue, and pricing and licensing terms can be changed by the other party at any time. For an organization that cannot send data outside its environment, this path is closed by definition — regardless of how convenient it appears
We built SAIE as AI for business without these limitations: architectural transparency and freedom to choose models as in open source, service, predictability and a contract as in an enterprise solution — while data remains in the client's environment. The comparison sets these three approaches against seven dimensions: cost control, data protection, AI Act compliance, vendor lock-in, freedom to choose LLM models, software service and integration with the client's environment