Place AI work where latency, cost, and control make the most sense.
VortexAQ Compute is the execution layer for model serving, agent workloads, retrieval jobs, and policy-sensitive automation across cloud, edge, and private estates.
Give platform teams a clean way to assign work to the right execution substrate without fragmenting the developer experience.
Declare where work belongs before the scheduler touches it.
Compute should feel like an execution fabric, not another hosting tile. The useful artifact is a placement contract: latency budget, data boundary, runtime profile, capacity signal, and rollout record.
~$ vortex deploy workload agent-runner --target edge-us-17 --profile gpu-small[ok] Resolving compute contract...[ok] Bound signal: Execution layerworkload: name: agent-runner runtime: wasm-worker placement: latency_budget_ms: 85 data_boundary: restricted-us routes: - gateway: nexus - inference: aegis-localArtifacts: placement manifest · capacity signal · rollout record- Declare latency, cost, and data-boundary constraints.
- Map gateways, agents, and inference jobs to the right execution profile.
- Promote workloads across hosted, private, and disconnected targets.
- placement manifest
- capacity signal
- rollout record
Give platform teams a clean way to assign work to the right execution substrate without fragmenting the developer experience.
Deployment briefing ↗Primitives your teams ship against.
Concrete behaviors and interfaces so security, platform, and product teams share one operational picture.
Workload placement across hosted, private, and disconnected targets
Runtime profiles for gateways, inference, batch jobs, and operator tasks
Capacity-aware routing signals for cost and reliability
Deployment patterns for enterprise and government enclaves