Frontier-scale models
If your workload genuinely needs the largest hosted models beyond what on-prem hardware can run, cloud inference is still the practical choice.
Own the hardware. Own the intelligence.
Most AI vendors sell you access to their cloud. Sovra sells you software that runs on hardware you already own. That single difference changes data residency, latency, offline capability, cost structure, and compliance. Here's the honest comparison.
| Dimension | Sovra AI (on-prem / edge) | Typical cloud AI vendor |
|---|---|---|
| Data residency | Inference, vectors, and logs stay on hardware you control by default. Nothing leaves the device unless you explicitly wire an integration. | Prompts and data are processed on the vendor's cloud infrastructure. Region options vary by provider and plan tier. |
| Latency | Local inference — no network round-trip. <200ms first-token target on validated edge hardware. | Network round-trip to the nearest data center on every request; latency depends on connection quality and provider load. |
| Offline capability | Core assistant flows run fully offline once deployed — no internet dependency for inference. | Requires an active internet connection for every request; no offline mode. |
| Cost structure | One-time hardware spend from $95 (Raspberry Pi) plus a flat software subscription. You own the asset; costs don't scale with token volume. | No hardware spend, but ongoing per-token or per-seat fees that scale with usage indefinitely. |
| Compliance & air-gapping | Air-gapped and EU-data-residency deployments are possible by design — the model never needs outbound network access. | Compliance depends on the vendor's certifications, data processing agreements, and region availability. |
| Embedded & automotive | Runs on automotive ECUs and OEM edge modules for in-vehicle command execution — inside the vehicle's own compute envelope. | Not applicable — cloud inference cannot run within a vehicle or disconnected embedded device. |
This compares deployment models, not a specific named vendor — individual cloud AI providers differ in region options, pricing, and offline features. See hardware pricing for Sovra's own indicative costs.
When cloud AI still makes sense
We'd rather you pick the right tool than switch for its own sake.
No on-site IT
Teams without anyone to rack, patch, or maintain hardware may prefer a managed cloud service, at least initially.
Early prototyping
Validating an idea before committing to hardware spend is often faster on a pay-as-you-go cloud API.
See it running on hardware you can own
Configure a platform from $95, or talk to us about your deployment.