Hybrid architecture
Aquaduck's hybrid architecture keeps your sensitive workloads on local and connected devices, with selective cloud routing only when preferred.
Request a demoWhat it does
Decide what stays on‑device and what can move. The runtime handles placement so applications don't need to fork between local and cloud paths.
Why it matters
Privacy‑sensitive context can remain local while heavier jobs use spare capacity elsewhere.
Deep dive
Hybrid AI combines local, private‑network, and cloud inference behind one execution layer. A private workflow can stay on the machine where it began, a larger job can use models and capacity on approved devices across the network, and a complex non‑sensitive task can move to an approved cloud model when local resources are not the right fit.
Organizations can apply their privacy, capability, capacity, and cost rules as policy. Aquaduck then turns those rules into a runtime decision framework instead of forcing every application to implement its own local, network, and cloud branches.
One policy, three paths
| Execution path | Where the request runs | Best suited for | Data exposure |
|---|---|---|---|
| On‑device | The originating machine | Sensitive prompts, low‑latency work, and offline use | Stays on that machine |
| Private network | One or more approved devices connected to the organization’s network | Larger models, pooled capacity, and shared internal workloads | Stays within the approved network |
| Cloud | An approved external inference provider | Complex, high‑capacity, or specialized non‑sensitive tasks | Sent only when policy permits |
Sensitive data becomes a routing input
Organizations can apply data‑sensitivity thresholds to determine whether a prompt can leave its originating machine. Each device can run Personally Identifiable Information (PII) detection against a prompt locally to determine where it should be processed before sending it elsewhere.
Local inspection matters because sending a prompt to the cloud to determine whether it was safe to send would defeat the purpose. It also gives the organization control over failure behavior. If classification is unavailable or uncertain, a policy can choose to keep the request local.
PII detection is just one of many signals. Organizations should also consider the sensitivity requirements around source code, contracts, credentials, financial records, and confidential business context. Rules can combine content inspection with application identity, user permissions, workload type, data residency, approved models, and allowed destinations.
Rules define the boundary
Organizations can express routing behavior as policy under one framework.
| Example rule | Runtime behavior |
|---|---|
| Keep legal documents on‑device | Use only the originating machine |
| Do not send detected PII externally | Use only the originating machine or approved private devices |
| Prefer existing hardware | Use local and network capacity before considering cloud inference |
| Use cloud for complex non‑sensitive work | Route complex, non‑sensitive tasks to an approved stronger or specialized cloud model |
| Require a specific model or region | Consider only runtimes that satisfy the model and residency requirement |
| Never weaken privacy during failover | Queue or reject the request if no permitted runtime is available |
Start private, expand intentionally
Hybrid AI means every request has access to the execution path that fits its requirements. Local inference provides the smallest trust boundary. The private network adds models and capacity from hardware the organization controls. Cloud inference remains available for approved tasks that need capabilities the private pool cannot provide efficiently.
With Aquaduck, those choices live in the runtime. Applications keep one integration while the organization decides what may move, where it may run, and when cloud capacity should be used.