Searches for a specific named product like "Zanusai private AI solution" usually come from the same place: someone has seen the name in a search result, an ad, or a vendor comparison list, and wants to know what it actually is before spending time evaluating it. AIDEVGEN has no relationship with Zanusai and cannot speak to its specific features, pricing, or claims — what follows instead is a neutral framework for evaluating any packaged private AI product, whether that is Zanusai or one of the many other vendors marketing similar offerings.
Treat this as a checklist to run against any private AI vendor, not an endorsement or description of one in particular — the same questions apply whether the name in front of you is Zanusai, another vendor, or a custom development partner.
What "Private AI Solution" Should Actually Mean
The term gets used loosely in marketing. Before evaluating any vendor's product, confirm what it specifically means for that vendor:
- Where does the model actually run? On your own infrastructure, in a private-cloud tenancy dedicated to you, or on the vendor's own shared servers with extra access restrictions layered on top — these are meaningfully different things sold under the same "private AI" label.
- Who can access the infrastructure? A truly private deployment should mean the vendor does not have standing access to your data once it's running, beyond agreed support arrangements.
- Is the underlying model open-weight or proprietary? This affects whether you could ever run it independently of the vendor, or whether you are permanently tied to their platform.
- What does "solution" include beyond the model? Some products are just a hosted model with a private label; others include the surrounding application, integrations, and support.
Questions to Ask Any Vendor Directly
- Can you show us exactly where our data will be processed and stored?
- Who at your company can access our deployment, and under what circumstances?
- Can our own technical or security team review the deployment before we commit?
- What happens to our data and configuration if we stop using the product?
- Is pricing structured around a dedicated deployment, or does it scale like a shared service?
A vendor confident in its privacy claims should be able to answer all of these directly, without vague reassurance in place of specifics. If a sales conversation cannot get concrete answers to these questions, that in itself is useful information about how the product actually works.
Packaged Product vs. Custom Build
| Packaged Private AI Product | Custom Private AI Build | |
|---|---|---|
| Time to deploy | Often faster, if your use case fits | Longer, shaped to your exact needs |
| Fit to your workflows | Depends on the product's design | Built specifically around them |
| Vendor lock-in | Often higher | Lower — you own the deployment |
| Transparency into how it works | Varies by vendor | Full visibility, since it's built for you |
Get the power of AI without your data ever leaving the building.
Tell us about your data — we'll tell you whether private AI fits and what it needs.
Where AIDEVGEN Fits, Honestly
We are one option among several for private AI, not a Zanusai alternative in any specific sense beyond both being part of the same broader private AI category, and we would rather a prospective client compare us fairly against other options than take our description of the market at face value. What we offer is a custom-built deployment — shaped to your workflows and data from the start, rather than a packaged product adapted to fit. If you are comparing a named vendor's product against a custom build, our on-premise AI page explains how AIDEVGEN approaches deployment, and the questions above apply equally whether you evaluate us or anyone else. Our private AI models page also covers how to evaluate the underlying model behind any packaged product, which is one of the harder things for a vendor's marketing to obscure.
Frequently asked questions
Is AIDEVGEN affiliated with Zanusai?
No. AIDEVGEN is not affiliated with, endorsed by, or a reseller of Zanusai or any other named private AI vendor. This page is a general guide to evaluating packaged private AI products, written independently.
What should 'private AI solution' mean when a vendor uses that term?
At minimum, it should mean the model runs on infrastructure the vendor does not have standing access to — your own servers or a private-cloud tenancy you control — rather than a shared multi-tenant cloud service the vendor manages. Always ask a vendor to confirm this specifically rather than assuming it from the name.
How do I verify a vendor's private AI claims instead of just taking their word for it?
Ask exactly where the model and data will run, who has administrative access to that infrastructure, whether the underlying model is open-weight or proprietary, and whether you can have the deployment reviewed by your own technical or security team before committing.
Is a packaged private AI product better than a custom-built one?
It depends on fit. A packaged product can be faster to deploy if your use case matches what it was built for. A custom build takes more upfront work but is shaped exactly to your workflows, data, and integration needs rather than the other way around.
What red flags suggest a 'private AI' vendor isn't actually offering a private deployment?
Vague answers about where data physically runs, an inability to explain who has access to the infrastructure, pricing that scales like a shared multi-tenant service rather than a dedicated deployment, and reluctance to let your own technical team review the setup before you sign.