Never wait for capacity.
Never overpay for it.
And never migrate to get there.
MultiCloud attaches to the Kubernetes cluster you already run — EKS, GKE, AKS, or self-managed — and finds every workload the best capacity that exists.
The capacity you need exists. Your cluster just can't reach it.
Karpenter, Cluster Autoscaler, and your cloud's node auto-provisioning all stop at one cluster, in one region, of one cloud.
GPUs exist. Just not in your region.
GPU availability and quota are per-region bottlenecks, and GPU spend is the fastest-growing slice of cloud bills. Your autoscaler can't go where the capacity is — so launches wait.
Most of the CPU you pay for is never used
Industry telemetry shows typical workloads use under 25% of requested CPU and under half of requested memory — and about half of organizations saw cloud spend rise after adopting Kubernetes.
A core is not a core
Per-core performance differs 10–100× across instance families, but Kubernetes packs by core count — you're often paying full price for cores that do a third of the work.
Your cluster stays the front end
Workloads you mark for offload schedule onto a virtual node in your cluster and run on capacity your autoscaler can't reach. Nothing about how you work changes.
No migration, no foreign machines
Offloaded pods stay visible in your kubectl, your dashboards, and your CI/CD — exactly like local pods. No machine ever joins your cluster; the connection is outbound-only and encrypted. Your control plane, IAM, and support relationship with your cloud don't change.
A market, not a region
Behind the virtual node sits a production scheduler that prices every placement against 232,000 options — instance × region × cloud × pricing model — with per-zone spot-risk scores and benchmark-normalized performance. Your cloud's cheaper regions come first; other clouds when a workload qualifies.
One contract, every provider
Run in your own cloud accounts — your discounts, commitments, and credits apply — or in ours, reaching clouds and GPU neoclouds you have no contracts with. A new cloud is months of procurement; through us it's a scheduling decision. Chosen per workload, mixed freely.
Use Cases
The workloads that wait for capacity or overpay for it today — and what happens whenMulticloud places them instead.
Training & Fine-Tuning Bursts
GPUs sourced from every region of your cloud — and from providers you have no contracts with — when local quota or capacity runs out. Capacity availability, not just price.
Async & Bursty Inference
Models serve from the cheapest qualifying GPUs within your latency budget, and scale to zero when idle. GPU COGS down; cold capacity never blocks a launch.
CI & Build Farms
Jobs spill to the cheapest spot capacity anywhere; nodes exist only while jobs run. Burst capacity at spot prices, zero idle.
Batch, ETL & Simulations
Overnight runs land wherever compute is cheapest that night — usually a sibling region of your own cloud. The largest, easiest savings delta, with no app changes.
Multi-Region Serving
Run replicas near your users, served directly from the pool's managed edge with DNS latency steering. Lower user latency and lower cost.
Resilience & DR
Warm replicas in another region or cloud with health-checked DNS failover. Survive an outage your own cluster can't — without running a second platform.
Proof before permission
You adopt one phase at a time. Each has its own standalone payoff —
and its own small, explicit access grant.
1 · Audit
A read-only agent (get/list/watch, no Secrets) produces the Compute Audit: what each workload would cost on the optimal instance, region, cloud, and pricing model — performance-normalized. Days, zero risk.
2 · Optimize
Continuously tuned configuration for the autoscaler you already trust — delivered as reviewable pull requests to your GitOps repo. The in-region share of the audited savings, captured and kept captured. Remove us anytime.
3 · Extend
The virtual node + gateway go in (outbound-only, namespace-scoped RBAC). Burst, batch, CI, and GPU workloads spill onto the cheapest qualifying capacity — with per-workload guardrails: latency budgets, allowed regions, spend caps.
4 · Serve
Offloaded HTTP services get a managed edge — automatic DNS, TLS, ingress, and load balancing — with replicas near your users and health-checked failover that survives a region outage your cluster cannot.
One workload, four prices
Equal-or-better benchmarked compute and memory at every step. Live catalog rows, not projections — and the top row is yours to change.
| Option | Benchmark (multi) | RAM | $ / month | Savings |
|---|---|---|---|---|
| What you run today — change it, the ladder recomputes | 4,629 | 64 GB | $561 | Baseline |
| Same cloud, cheaper region, still on-demand — r6a.2xlarge, ap-south-1 | 6,278 (+36%) | 64 GB | $209 | −63% |
| Same cloud, spot, low-risk zone — x8g.xlarge, eu-north-1b | 6,262 (+35%) | 64 GB | $39 | −93% |
| The whole board — AZURE Standard_F16ams_v6 spot, newzealandnorth | 13,429 (2.9×) | 128 GB | $24 | −96% |
How Multicloud finds these rows
The savings pay for it
A flat platform fee, priced under your audited savings — never per-CPU, never a percentage of savings. The audit comes first precisely so the fee is justified by a number you measured.
Why you can believe the promise
The pool behind your cluster is not new software. It's a substrate we operate in production today — your cluster is the new front door to it.
One scheduler, multiple clouds
A single production scheduler provisions, places, and reaps capacity across multiple clouds — AWS, Azure, and Google Cloud today, more as we add them. One decision engine, not per-cloud integrations bolted together.
A catalog you can browse right now
3,280 instance types and 232,000 priced SKUs, continuously refreshed — with benchmark-normalized performance and per-zone spot-risk scores. It's public: see the live catalog.
A network that spans clouds
A flat, WireGuard-encrypted network across clouds and regions, with automated VPC, firewall, and DNS lifecycle. Your pods never know they crossed a cloud.
Spot reclaim, survived
Preemption detection with disruption-budget-respecting drain and automatic reschedule — across zones, regions, even clouds. Spot prices without spot surprises.
Serverless, already live
Knative Serving runs in production on the pool: scale to zero, wake on request, public edge with automatic DNS and TLS.
We operate it ourselves
GitOps delivery, observability, and SSO built in. The substrate runs our own production every day — operated by us, on your cloud accounts or ours. Enterprise teams can have the entire stack run inside their own accounts.
Deploy the way you already deploy
No new CLI. No proprietary manifest format. Your cluster is the interface to Multicloud — and the tools you trust today keep working, unchanged.
# Nothing new to learn — your existing pipeline is the integration
$ kubectl apply -f k8s/ # your manifests, unchanged
$ helm upgrade --install my-app ./chart # your charts, unchanged
$ kn service create api --image=my/api # Knative: scale-to-zero on the poolkubectl & the YAML you already have
Deployments, StatefulSets, Jobs — they run as-is. Marking a workload for offload is a label on the pod spec, not a rewrite, and it stays visible in kubectl like every other pod.
Helm & GitOps
Charts install unchanged, and ArgoCD or Flux stays your delivery pipeline. When we optimize, we show up as reviewable pull requests to your repo — never as a parallel control plane.
Knative for serverless
HTTP services scale to zero on standard Knative Serving, running in production on the pool today. Pay for requests, not for idle.
Start with the audit
Read-only, days, zero risk — the Compute Audit shows what every workload you run would cost on the best capacity that exists. Create an account or join the waitlist for early access to Multicloud.
Questions or want to learn more?
contact@multicloud.io