Private & On-Premises AI Deployment

Deploy AI in a private cloud, VPS, or on-premises environment with the model, access rules, APIs, monitoring, and data boundaries designed around your business requirements.

Private AIOn-PremisesDeployment
Custom quote after architecture reviewOptional monitoring, maintenance, and support

The problem

Some companies want AI benefits but cannot send sensitive documents, operational data, or internal knowledge through a normal public SaaS workflow. The result is often a stalled AI project or a risky workaround.

Who it's for

Companies with sensitive internal data, stricter client requirements, regulated workflows, or a preference to keep AI infrastructure and business knowledge under their own control.

The outcome

A private AI environment with clear data boundaries, controlled access, and an architecture your team can operate or hand back to STYD for support — without forcing sensitive workflows into a generic public SaaS model.

What STYD delivers

STYD reviews the privacy, workload, model, hosting, access, and integration requirements; designs the deployment; sets up the chosen model-serving and application layer; connects approved data sources; adds authentication, logging, and monitoring appropriate to the scope; then validates the real workflow before handover.

Pricing

Starting at: Custom quote after architecture review
Monthly option: Optional monitoring, maintenance, and support

Typical starting point — final scope and price are confirmed after a short review of your setup.

Available after scoping

This service is available — tell us about your setup and we'll confirm scope, hosting, and pricing.

Available as project-based setup with optional STYD hosting, maintenance, and monthly support.

FAQ

Does on-premises mean the AI never leaves our building?

It can, but it does not have to. Private deployment may mean your own server, VPS, private cloud, or another isolated environment. The boundary is agreed during scoping.

Can you use open-source models?

Yes, when they fit the required quality, hardware, licensing, and privacy constraints. Model choice is made from the use case rather than from a fixed vendor preference.

Do we need our own GPU server?

Not always. Some workloads can run on rented private infrastructure or smaller models. Hardware and hosting requirements are assessed before anything is purchased.

Is private AI automatically more secure?

No. Private hosting gives more control, but access, updates, logging, backups, network exposure, and application security still need to be designed and maintained properly.

Interested in Private & On-Premises AI Deployment?

Tell us about your setup and we'll confirm fit, scope, and pricing.