Autoscaling.
Demand-driven scaling for CPU and GPU pools — expand for a run, contract when idle, pay for the work.
Policy-driven autoscaling across every compute service. Define the signal — utilization, queue depth, latency or a custom metric — and capacity follows demand up and back down to zero.
- Category
- Compute
- Deployment
- Managed → air-gapped
- Governance
- IAM · encryption · audit
Three steps to running.
Scale on utilization, queue depth, latency or a custom application metric.
Min/max and budgets so capacity tracks demand without outrunning the wallet.
Predictive and scheduled scaling pre-warm for known peaks; idle scales to zero.
Autoscaling, in full.
Scale on CPU, memory, concurrency, queue depth or a custom application metric.
Pre-warm for known peaks and forecasted demand to avoid cold starts.
Idle services cost nothing; capacity returns the moment demand does.
Min/max bounds and budgets so scaling never outruns the wallet.
Provision it in a few lines.
Every service is reachable from the same SDK, CLI and infrastructure-as-code — one identity, one bill, one audit trail across the whole catalog.
# Provision autoscaling on Aether Cloud
aether compute create \
--service autoscaling \
--name app \
--region us-1 \
--deploy managed # or vpc | on-prem | air-gappedAt a glance.
- Signals
- CPU · mem · queue · latency · custom
- Modes
- Reactive · scheduled · predictive
- Floor
- Scale to zero
- Guardrails
- Min/max + budget caps
- Targets
- VMs · containers · GPU pools
Built for real work.
Spiky web and API traffic
Bursty training and batch jobs
Cost-optimized GPU fleets
On one model, not stitched together.
The usual stack runs autoscaling in one product, the model in another and the data in a third — and the seams between them are the cost. Aether Cloud runs it on the same platform that serves the model, governs your identity and deploys into your boundary, with the rest of the catalog one hop away.
No stitching a vector DB to one place, a warehouse to another and a model to a third — autoscaling sits next to the rest of the catalog, one identity, one bill.
The provider that runs Aether runs your autoscaling — so the data and the model never leave the same governed boundary to talk to each other.
Need a capability that isn’t here yet? The model writes and deploys it into the same boundary — the catalog is a starting point, not a ceiling.
Good to know.
Same policy-driven scaling — across CPU and GPU pools, with cost guardrails built in.
Yes — expand a GPU fleet for a run and contract it after, on cost signals.
Min/max bounds and budgets cap scaling so it never outruns the budget.
Your boundary, your choice.
Pairs well with.
On-demand and reserved VMs across CPU and GPU shapes, with per-second billing and live resize.
Latest-generation accelerators by the hour or the cluster, with fast interconnect for training and high-throughput inference.
Event-driven functions that scale to zero — run code and agents without managing a server.
Managed container runtime with autoscaling, health checks and rolling deploys — bring an image, get a URL.
A conformant, managed control plane with GPU scheduling, autoscaling node pools and zero-downtime upgrades.
Massively parallel batch and simulation jobs with scheduling, checkpointing and fault tolerance built in.
Run Autoscaling on Aether Cloud.
Demand-driven scaling for CPU and GPU pools — expand for a run, contract when idle, pay for the work. Deployable managed, in your VPC, on-prem or fully air-gapped — talk to us about the configuration your workloads and your boundary require.