Deployment options
Choose deployment by control, speed, and workload placement
AIPay supports on-premises, managed, and hybrid deployment with explicit data location and operating responsibilities.
Global models
Local models
No single deployment model fits every enterprise
Control versus speed
Greater control generally requires more internal infrastructure and operational capability.
Different workload needs
Sensitive audit, local models, and general model access may belong in different environments.
Unclear responsibility
Upgrade, monitoring, backup, and incident responsibilities must be explicit.
Place workloads before choosing infrastructure
AIPay designs the deployment mix around data sensitivity, existing systems, model location, timeline, and operating capability.
On-premises
The enterprise controls infrastructure, data, and administration; AIPay provides deployment and contracted support.
Managed service
AIPay operates infrastructure and upgrades while enterprise administrators manage users and policy.
Hybrid
Keep sensitive workloads and local models inside while connecting other capabilities by policy.
Migration and scale
Expand teams, models, policies, and Agents after a measured pilot.
Typical deliverables
- Deployment and workload assessment
- Responsibility matrix
- Network, identity, and data design
- Launch and rollback plan
Who this is for
- CIOs and CTOs evaluating enterprise AI architecture
- Organizations with local models or data-residency requirements
- Teams that want to pilot quickly and expand deliberately
Next step