Deployment Topology Explorer
Three deployment options. The right choice depends on data, latency, integration, operations and cost.
Cloud
All data flows through cloud
Data leaves your environment
On-Premises
All data stays on-premises
Data never leaves your environment
Hybrid
Sensitive data stays, rest goes to cloud
Data boundary enforced by policy
Data Boundary: What Stays Where
Sensitive data never crosses the boundary. Only non-sensitive operations go to cloud.
Data Boundary
Sensitive data never crosses the boundary. Only non-sensitive operations go to cloud.
3-Year Cost Comparison
Cloud has ongoing costs. On-premises has high upfront but lower ongoing. Hybrid balances both.
3-Year Total Cost of Ownership
Cloud has ongoing costs. On-premises has high upfront but lower ongoing. Hybrid balances both.
The Business and Data Requirements
Not every AI solution belongs in a public cloud. Some data cannot leave your environment. Some operations must stay on-premises. Some need a mix. The starting point is understanding why deployment location matters for you—data sensitivity, latency, compliance, cost or operational control. The assessment determines where the solution should run and why, so the deployment choice is deliberate rather than default.
Cloud, Private, On-Premises and Hybrid Options
Architectures for private cloud, on-premises and hybrid setups. Some components run in your environment; some may run in cloud where appropriate. The architecture is chosen for your constraints, so you get the capability of modern AI without compromising where your data must live.
| Cloud | Private Cloud | On-Premises | Hybrid | |
|---|---|---|---|---|
| Data Control | Low | Medium | Full | Mixed |
| Latency | Varies | Good | Best | Mixed |
| Cost Model | Pay-per-use | Reserved | CapEx | Mixed |
| Maintenance | Managed | Shared | Self | Shared |
| Compliance | Standard | Enhanced | Full | Flexible |
Model and Infrastructure Choices
On-premises and private deployment does not mean no models. Open models can run in your environment; commercial models are used where permitted. The choice is based on quality, cost, licensing and the deployment context, not on ideology.
Running AI on-premises requires the right infrastructure. Compute, memory and storage are sized for your models and your demand, so you do not over-provision and waste budget or under-provision and miss latency targets. Sizing is based on the actual workload.
Cloud
Private Cloud
On-Premises
Data Boundaries, Access and Updates
Data boundaries ensure sensitive information stays where it must. Access controls determine who and what can reach which data, and the architecture prevents unintended exposure. Data boundaries are designed in, not hoped for.
Private and hybrid deployments are monitored and updated in a controlled way, so the system stays current and observable without losing stability. Updates are staged and reversible, because an on-premises system that cannot be updated safely will fall behind.
Cost, Performance and Operational Ownership
On-premises inference must be efficient. Inference is optimized through quantization, batching and serving configuration, so you get the most from your hardware. Private AI must connect to your internal systems. Integration with your data sources, APIs, identity systems and workflows ensures the solution works inside your environment rather than sitting beside it.
3-Year TCO Comparison
What Good Looks Like
Industry-referenced benchmarks for private and hybrid AI deployment. Your results depend on your infrastructure and workload.
Data Stays On-Prem
sensitive data never leaves
Lower Latency
on-prem vs cloud for local users
Uptime
with hybrid failover
Yes, using open models and your infrastructure.
No. We use open or commercial models based on your context and licensing.
Yes, with components split across cloud and your environment as appropriate.
Through data boundaries and access controls designed into the architecture.
Yes. Updates are staged, monitored and reversible.