Start with the level of infrastructure and sovereignty your organization actually needs.
Facthory runs a dedicated enterprise environment while your organization keeps governance over users, data and approved AI use.
Dedicated environment
Enterprise identity
Regional controls
Managed operations
For organizations with established cloud governance, BYOC places the platform inside the customer's own cloud environment. Your enterprise retains direct control over infrastructure policy, network connectivity, regional placement, cloud consumption and integration with existing platform operations.
Select supported data and model processing regions according to enterprise residency requirements.
Connect Facthory through enterprise-controlled network paths instead of exposing critical systems unnecessarily.
Integrate corporate SSO, MFA and role policies so existing identity governance remains authoritative.
Separate production and non-production environments according to enterprise release and risk controls.
Use governed, repeatable infrastructure deployment patterns instead of manual environment configuration.
Align responsibilities across customer platform teams and Facthory support from rollout through production operation.
Enterprise sovereignty is not only about where data is stored. It also includes which AI models are permitted to process that data, where inference runs, how models are versioned and which workloads can use them. Facthory supports a governed model portfolio rather than silently coupling the platform to one provider. Enterprises can define approved models, regions and model classes according to security, performance, cost and regulatory requirements. This makes it possible to combine enterprise-approved commercial models, customer-hosted models and suitable private or local models within one governed operating model.
Choose managed, BYOC, private cloud or on-premises architecture and approved regions.
Provision the agreed environment through standardized deployment and configuration controls.
Verify identity, connectivity, access, model, region and operational requirements before go-live.
Manage releases, upgrades, models and environment changes under defined operational responsibilities.
Data location alone does not create sovereignty. Facthory lets enterprises govern where the platform runs, where data stays and which models are permitted to process it.
Keep enterprise data under defined residency, access, retention and ownership policies.
Choose approved model providers, versions, regions and private inference options.
Control identity, networks, infrastructure, release governance and responsibility boundaries.
The right deployment depends on regulation, data sensitivity, existing cloud strategy, connectivity and operational ownership.
For enterprises wanting dedicated operation without taking on platform infrastructure management.
For organizations that want Facthory inside their existing cloud governance and billing boundary.
For stricter infrastructure isolation, dedicated networking and controlled enterprise operating boundaries.
For environments requiring local execution and maximum infrastructure ownership inside the customer's estate.
Infrastructure choice should not fragment the product experience. The objective is to give enterprises one Facthory platform while allowing the operating boundary to change as security and sovereignty requirements increase. A team can begin with a managed enterprise deployment, move selected workloads into customer-controlled cloud infrastructure, or adopt a more isolated architecture when regulation, data classification or organizational policy demands it. The platform remains governed around the same core concepts: enterprise identity, approved data access, controlled models, bounded agents, human authority and traceable operation. This gives CIOs and CISOs a practical path to enterprise AI adoption without forcing a permanent architecture decision on day one.
Sovereign enterprise AI means controlling the data, the models, the infrastructure and the authority to operate them.
Define use case, security classification, residency, integration and availability requirements.
Choose the deployment and model governance boundary that fits enterprise policy.
Integrate enterprise identity, networks, source systems and approved AI services.
Provision the agreed environment through controlled, repeatable deployment processes.
Test access, isolation, regional controls, integrations and operational responsibilities before production.
Run updates, models and platform changes under the agreed governance and support model.
Keep data processing within approved supported regions and enterprise policy boundaries.
Control where approved AI inference executes and which model endpoints are permitted.
Use customer-owned infrastructure where direct cloud control and billing are required.
Integrate with enterprise connectivity, segmentation and private access patterns.
Keep enterprise identity, MFA and role governance at the center of platform access.
Coordinate upgrades and platform changes according to enterprise operating procedures.
Maintain a governed portfolio of approved models and versions for enterprise workloads.
Define clear ownership between enterprise teams and Facthory for secure ongoing operation.
Facthory is designed so enterprise AI strategy is not permanently tied to one hosting model or one model provider. As requirements evolve, organizations can increase infrastructure control, change approved models or move workloads into stricter operating boundaries without redesigning the entire operational intelligence layer.
Deployment boundaries
Consistent platform
Data and model control
Model portfolio
Choose the deployment, region, model portfolio and operating model that match your security architecture and sovereignty requirements.
