Alternatives
Beyond NVIDIA Run:ai: a full multi-tenant AI cloud
NVIDIA Run:ai, now open source, schedules GPU workloads on Kubernetes. What a provider still needs around it, and where FusioNative fits next to NVIDIA's stack.
Last updated · Fusionative
Short answer
NVIDIA Run:ai schedules and shares GPUs across Kubernetes workloads, and since October 2026 it is open source. Its multi-tenant documentation leaves infrastructure isolation, provisioning and billing to the operator. FusioNative provides that cloud layer: tenants, self-service, quotas, metering and chargeback, VMs, storage, networking and model serving, alongside or instead of Run:ai.
Why teams look
Why teams look for a NVIDIA Run:ai alternative
Scheduling is one layer
Run:ai and the KAI Scheduler handle GPU quotas, fair share and fractional GPUs. Selling or sharing a GPU estate also needs tenant provisioning, isolation, a catalog and billing.
NVIDIA leaves isolation to the host
Run:ai's multi-tenant mode gives untrusted tenants dedicated clusters and states that infrastructure isolation and provisioning are the host's responsibility.
Mixed accelerators and VMs
Teams running AMD GPUs or many VMs alongside NVIDIA GPUs look for a vendor-neutral layer over the whole estate.
Checklist
What to look for in a replacement
- Tenant provisioning and isolation for untrusted customers
- Metering and billing, not only usage reports
- Support for NVIDIA and AMD accelerators
- VMs, storage and networking in the same platform
- Self-hosted and air-gapped operation
Fit
Where FusioNative fits as a NVIDIA Run:ai alternative
The cloud layer NVIDIA leaves to partners
Tenant onboarding, self-service catalog, quotas, metering and chargeback.
Works with NVIDIA software
Serves models with NVIDIA NIM alongside vLLM, SGLang and Ollama on NVIDIA and AMD GPUs.
Whole estate
Kubernetes, KubeVirt VMs, Ceph and Cilium in one suite.
When FusioNative is not the right choice
- You run only internal research teams on one NVIDIA cluster and need scheduling, not a cloud.
- You want NVIDIA's own supported stack end to end, such as Mission Control on DGX systems.
Migration
How teams move
- 1
Keep the scheduler if it works
Run:ai or KAI can continue to schedule GPU jobs within tenant clusters.
- 2
Add tenancy and billing
Use FusioNative to onboard tenants, publish services and meter usage.
FAQ
Frequently asked questions
Is NVIDIA Run:ai open source?
Yes. On 13 October 2026 NVIDIA released the full Run:ai platform as open source as part of its DSX AI Factory platform; a support-entitled release is still sold.
Does Run:ai include billing?
Run:ai documents per-tenant quotas and usage reports. Its documentation does not describe billing or chargeback, which providers add from other software.
Does FusioNative replace NVIDIA AI Enterprise?
No. FusioNative can serve NVIDIA NIM models and runs alongside NVIDIA software, adding multi-tenant cloud operations around it.