Clouds are power plants. Multicloud is the grid.
Never beg for quotaNever wait for capacityNever 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 you choose to offload the best capacity that exists.
- Optimal instance type
- Any region, any cloud
- Risk-free audit: no migration, no foreign machines
The capacity you need exists.
And it's probably way cheaper than you think.
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. There are over 3,000 unique instance types. You can't benchmark them all, and Kubernetes packs by core count, so you're often paying full price for cores that do a third of the work.
Electricity has a grid.
Compute doesn't.
So every team that outgrows its autoscaler
gets pushed into one of two options — and both dead-end.
Build it yourself
Wire your own grid — platform engineering on Kubernetes.
- Full control · any cloud · no markup
- A platform team you staff forever
- You become a platform company — not a product one
- Still under 25% utilization, still blind to the 10–100× cross-cloud spread
Buy a PaaS
Rent their appliances, never the power — Heroku, Vercel, Fly, Modal, Render.
- Magical simplicity — ship in minutes
- Opinionated, limited, proprietary — and one cloud
- A markup on every CPU-hour — worse as you scale
- Hit a wall — scale, cost, GPU — with no path down to the metal
Simplicity or control — never both.
Either way you don't get the grid: wire your own forever, or rent one plant's appliances.
29% of cloud spend was wasted in 2026 — and rising.
There has never been a third option.
So we built the Compute grid: Multicloud.io
A grid standardizes, commoditizes, and connects.
All three are built and in production across every region of AWS, Azure & GCE — OCI + Neoclouds are WIP.
a declared need — never a machine
Operated for you
~30 operators & control loops · day-2 ops included — none on your on-call
- provisioning — AWS · Azure · GCE (OCI + Neoclouds WIP)
- node autoscaling
- spot self-healing
- right-sizing
- live capacity catalog
- VPCs & firewalls
- WireGuard mesh
- cross-cloud pod network
- ingress & load balancing
- DNS
- GitOps deploy
- registry pull-through
- serverless runtime
- service discovery
- zone-aware routing
- TLS certificates
- SSO & auth
- mTLS identity
- logs · metrics · traces
- dashboards & cost visibility
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.
Your cloud · your tooling · unchanged
Every switch works both ways — flip any workload back, anytime.
One direction — nothing comes into your cluster.
Endless supply · every corner of every cloud
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, where your discounts, commitments, and credits apply, or in ours, reaching other major clouds you have no contracts with. A new cloud is months of procurement; through us it's a scheduling decision. Chosen per workload, mixed freely.
Sovereign by design
The attachment is outbound-only and fully isolated: nothing inbound, and no foreign machine ever joins your cluster. For regulated and sovereignty-minded teams, the entire stack — workloads and the control plane — can run inside your own accounts, perimeter, and chosen jurisdictions, with no external SaaS in the critical path. You govern the orchestration layer, not just where your data sits.
Use Cases
The workloads that wait for capacity or overpay for it today,
and what happens when Multicloud places them instead.
Training & Fine-Tuning Bursts
GPUs sourced from every region of your cloud, and from clouds 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. Spot-aware burst capacity across clouds, regions and instance types (configurable). Zero idle.
Batch, ETL & Simulations
Overnight runs — genomics pipelines, VFX render farms, ETL, Monte Carlo sweeps — 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 — insurance for the outage your own cluster can't survive, without the cost or headcount of running a second platform yourself.
Proof before permission
You adopt one phase at a time. Each has its own standalone payoff,
and its own small, explicit access grant.
- 1Auditread-only · no Secrets
- 2Extendkubeconfig-only · no cloud IAM
- 3Servemanaged edge
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.
Extend
A virtual node drops into your cluster — kubeconfig-only, no cloud IAM. Send one bounded burst or GPU job to the cheapest qualifying capacity, with per-workload guardrails: latency budgets, allowed regions, spend caps. Pull the node any time and you're exactly where you started.
Serve
Offloaded HTTP services get a managed edge (automatic DNS, TLS, ingress, and load balancing) with replicas near your users and health-checked failover built to keep serving through 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.
The savings pay for it
A flat platform fee, priced well below 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.
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 propose a change, it arrives as a reviewable pull request 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