Kinesis connects datacenters, clouds, and customer hardware into one compute grid. Run AI, data processing, and applications through a shared deployment workflow.
Use Kinesis-managed capacity, bring your own, or combine both. Standard containers keep workloads portable; one console brings performance and cost into view.
Choose usage-based Serverless compute or dedicated hardware for steady workloads.
The control plane matches each workload to available hardware, then adjusts placement, capacity, and recovery as conditions change.
One orchestrated grid
Clouds, datacenters, and your own machines use the same deployment, networking, and monitoring tools.
A control plane that decides
Placement and recovery respond to workload requirements and live conditions. Your team can see performance and cost across providers.
Connect clouds, your own hardware, and partner datacenters. Deploy and monitor workloads through the same controls across providers and locations.
Scheduling, utilization, cost, and recovery share one control plane, even when the hardware belongs to different operators.
The scheduler weighs cost, latency, and availability across clouds, partner datacenters, and customer hardware.
Balance load across the grid to use more of each processor without overcommitting capacity.
Choose placements that meet latency targets, cost ceilings, capacity limits, and policy requirements together.
Detect failing nodes, isolate them, and reschedule affected workloads on healthy hardware within placement constraints.
Each deployment adds data about demand, hardware, and failures. That history helps refine placement, recovery, and cost estimates.
Better placement
Workload history improves placement predictions for latency, utilization, and hardware fit.
Better failure handling
Failure patterns inform detection, isolation, and recovery.
Better pricing
Live supply and demand inform cost models.
Better experience
Deployment history helps refine defaults and automate recurring setup.
