ClusterMAX™ evaluates AI cloud providers across 10 dimensions. This guide maps each criterion to the specific capabilities in vCluster, vNode, and vMetal that help you improve in that area, including the Security criterion that now explicitly names vCluster as a requirement.
ClusterMAX, published by SemiAnalysis, has become the de facto standard for evaluating GPU cloud infrastructure. Enterprise AI teams consult it before selecting a provider. Improving your rating directly impacts deal velocity and customer trust.
ClusterMAX scores providers across Security, Lifecycle, Orchestration, Storage, Networking, Reliability, Monitoring, Pricing, Partnerships, and Availability, covering the full infrastructure stack enterprise customers care about.
The updated Security criterion now explicitly lists “vCluster or similar isolation beyond container-based only” as a requirement, a direct signal to enterprise buyers that cluster-level isolation is non-negotiable.
Many providers plateau in their ratings not because of hardware, but because they lack a managed platform layer. vCluster, vNode, and vMetal directly address the software and operational gaps ClusterMAX evaluates.
Use it as a roadmap: identify where your current offering falls short, and understand exactly which product to deploy to close the gap.
ClusterMAX evaluates dozens of specific requirements across 10 dimensions. Choose one of the 10 ClusterMAX dimensions on the left, then select a subcategory on the right to see the criteria vCluster covers and how.
Fill out the form to explore detailed insights into how vCluster can help you meet requirements across all ten dimensions.
vCluster provides each tenant with a dedicated Kubernetes control plane — isolated API server, RBAC, CRDs, and controllers. This eliminates cross-tenant API access entirely. Pair with vNode for kernel-level workload isolation and Private Nodes for GPU workloads requiring exclusive physical access.
vNode enforces kernel-native isolation using Linux user namespaces, seccomp profiles, and cgroup separation. Container escape vulnerabilities are contained to the affected workload — they cannot reach other tenants or the host.
vNode protected against these vulnerabilities even without patching. Not just about patching existing container breakouts — also about preventing attacks for future zero-days which are not yet known. vCluster Platform automates installation of GPU Operator / NVIDIA Container Toolkit and makes it easy to keep it up to date.
Fully automated rollout of new NVIDIA Container Toolkit versions is possible with vCluster Templates.
Not applicable to vCluster Labs as a software vendor.
Per-tenant network isolation for RoCE is automated at provisioning time via the network fabric partner. vCluster coordinates and instructs the underlying networking tool, including Netris, OpenStack Neutron, Multus / SR-IOV for RDMA. Provides simple webhooks for homegrown network automation tools as well.
Partition Key assignment per InfiniBand tenant is automated at provisioning time via the network fabric partner. vCluster integrates with Netris, which orchestrates PKey assignment through NVIDIA UFM.
InfiniBand fabric key management is handled at the network infrastructure layer via NVIDIA UFM and OpenSM. vCluster integrates with Netris, which provides configuration guidance for M_Key and VS_Key within UFM.
SHARP Aggregation Manager key configuration is handled at the fabric layer by the network infrastructure partner when SHARP is deployed. Only applicable if SHARP-capable Mellanox switches are present.
vCluster Private Nodes is focused on bare metal rather than VMs for AI clusters, eliminating the need for SR-IOV VF partitioning. This is a host-layer concern for hypervisor-based environments.
vCluster is self-hosted software — vCluster Labs does not process or store customer data but helps with SOC 2 requirements using software features such as automated backups via vCluster Snapshots for example.
vCluster is self-hosted software — vCluster Labs does not process or store customer data but helps with SOC 2 requirements using software features such as automated backups via vCluster Snapshots for example. ISO 27001 obligations reside within the operator's own deployment environment.
Compliance obligations reside within the operator's deployment environment. vCluster provides the technical controls required to operate within compliant environments: RBAC, audit logging, network isolation, and FIPS-compliant images (Enterprise). FIPS version of images is particularly relevant for federal-related business.
vCluster's self-hosted model supports air-gapped, on-prem, and sovereign deployments — customer data never leaves the operator's environment. FedRAMP and ITAR eligibility depends on the operator's own certifications.
Third-party penetration testing has been conducted on vCluster Platform and vNode. Reports available under NDA on request. This does not directly help the operator with their own audit but ensures our software passes through cleanly.
Third-party penetration testing has been conducted on vCluster Platform and vNode. Reports available under NDA on request.
Fabric-layer security assessments require a partner with InfiniBand/Ethernet expertise. vCluster works with operators to validate tenant isolation at the control plane and networking layers.
vCluster supports AI cloud providers with this by enabling an automation-first approach (Templates, etc.) and vCluster maintains a published vulnerability disclosure process.
FAST — vCluster Platform reduces time-to-first-cluster to minutes, eliminating the weeks typically required for manual Kubernetes infrastructure setup. Tenant onboarding can be fully automated with vCluster Platform.
LIKE HYPERSCALERS — Everything can be fully automated AND all standard IaC/GitOps provisioning tools and flows are supported, plus a great UX in the Platform UI.
vCluster provisions a fully managed Kubernetes environment in seconds via UI, API, or Kubernetes CRDs — no new physical infrastructure, no manual Terraform. vMetal automates the full bare metal lifecycle: PXE boot, OS provisioning, and node registration.
vCluster Templates delivers production-ready GPU tenant clusters with GPU Direct RDMA configured out of the box. GPU Operator, Network Operator, and nvidia-peermem deployed via standard Helm or GitOps workflows. No new physical infrastructure required; works on any vCluster with bare metal GPU nodes.
vCluster provisions a fully managed Kubernetes environment in seconds via UI, API, or K8s CRDs — customers receive a kubeconfig immediately with no manual handoff. vMetal automates bare metal GPU node provisioning end-to-end: PXE boot, OS install, and cluster registration — replacing Terraform complexity with a declarative self-service workflow that is fully GitOps-compatible.
Our automations make things more predictable and fast. Additionally our Customer Engineering Team is here to support complex new data center setups.
vCluster Templates manages MOFED driver and NVIDIA Container Toolkit lifecycle fleet-wide via the Network Operator. No per-node manual intervention required. Driver versions stay consistent across nodes and updates roll out through standard Kubernetes operator reconciliation.
Automated performance testing for new tenant environments is on the roadmap. Currently, vCluster helps with manual performance testing but an in-product solution is coming soon.
vCluster will launch a SLURM solution in H2 2026 providing fully automated SLURM cluster provisioning and day 2 operations. Coming soon.
vCluster has worked with some of the biggest AI cloud providers such as CoreWeave since 2021 and gained valuable experience since the earliest days of the AI cloud industry.
vCluster Platform exposes all metrics and the Kubernetes API audit logging for all resource lifecycle events (create, update, delete) and administrative actions. Integration into billing systems can be done custom today but in-product automated integration is coming soon. Each log entry includes actor identity, resource type, action, and timestamp.
vCluster Platform captures full actor attribution in audit logs via standard Kubernetes audit policy: user ID, source IP, action verb, target resource, and request status (success/failure). This meets ClusterMAX's actor-attribution requirement for each logged event.
Audit logs are accessible via the Kubernetes API. Full filtering by resource type, user, and date range depends on the log aggregation backend the operator connects (e.g., Loki, OpenSearch, Elasticsearch). vCluster Platform surfaces the audit log stream — additional queryability at scale can be achieved with a connected aggregation layer.
Audit logs can be persisted to databases and systems considered industry standard. Retention periods can be set individually without limitations.
vCluster Platform's RBAC layer allows configuration of who can see the audit log. Default permissions are restricted to platform administrators only — tenant users cannot access audit logs by default. No additional usage charges apply for audit log access.
vCluster provisions a fully managed Kubernetes environment via CRDs, API, or UI in seconds — no manual cluster setup, no Terraform, no infrastructure provisioning required. The entire setup is declarative and GitOps-compatible.
vCluster is a fully managed Kubernetes environment provisioned on demand via API or UI. Each tenant gets their own API server, RBAC, and namespaces — isolated from all other tenants — without requiring separate physical clusters or manual cluster management by the provider.
On provisioning, vCluster automatically generates and delivers a kubeconfig to the customer. They have immediate kubectl access and can use Helm, Lens, k9s, or any standard Kubernetes tooling — no SSH setup, no firewall rules, no manual steps from the operator.
Standard kubeconfig works with any Kubernetes tooling out of the box — Lens, k9s, kube-dashboard, Headlamp all connect normally. No additional configuration required from the operator or the tenant.
CUDA_VISIBLE_DEVICES is automatically configured by the NVIDIA device plugin (part of GPU Operator) when GPUs are requested via Kubernetes resource limits. The device plugin injects GPU device assignments as environment variables into containers at scheduling time — no manual configuration required.
PVC and hostPath storage work natively in vCluster. PVCs are synced to the host cluster by default, and vCluster includes a local path provisioner for dynamic PVC provisioning via hostPath with no additional setup. S3-compatible object storage is also accessible via an S3 CSI driver deployed on the host cluster.
vCluster Platform provides fully featured user management. Users can be manually onboarded via email/password but SSO is also supported. OIDC/SAML and other SSO integrations enable secure and automated user onboarding and offboarding. Automated permission and key management ensures secure access for new users.
Each tenant cluster has its own isolated RBAC layer and OIDC/SSO integration. Providers connect their existing identity provider (Okta, GitHub, Azure AD) so customers authenticate via SSO and receive role-scoped kubeconfigs — no shared identity namespace between tenants.
vCluster eliminates SSH-based cluster access entirely. Customers receive a kubeconfig and authenticate via OIDC/SSO — no SSH key distribution, no per-node access management, no operator involvement for adding or removing cluster access.
Kubernetes RBAC controls storage access at the namespace and PVC level. Per-tenant isolation means tenants cannot access PVCs outside their own cluster — cross-tenant storage access is structurally prevented.
Pyxis is a container runtime plugin for SLURM, built by NVIDIA. Not currently supported.
vCluster supports any storage systems including Weka, DDN, VAST, or any other CSI driver. Procuring and installing the filesystem is the responsibility of the operators as a prerequisite to use it in vCluster.
S3-compatible object storage is the operator's responsibility to provision and expose. vCluster does not manage object storage directly. Tenants can access operator-provisioned S3 endpoints from within their tenant cluster using standard Kubernetes secrets and environment variables.
vCluster supports any storage systems including Weka, DDN, VAST, or any other CSI driver. PVC provisioning is identical to a native cluster — no extra configuration required. Procuring and installing the filesystem is the responsibility of the operator as a prerequisite.
CSI passthrough means mounts are handled by the tenant cluster's CSI driver — no extra configuration required.
vCluster Platform provides backup and restore for tenant cluster state and PV data via volume snapshots, with configurable scheduling and retention. Application-specific (database) backups are recommended to be configured additionally.
vCluster Platform multi-region mode provides control plane DR across regions. Storage-layer cross-region replication is determined by the operator storage infrastructure and requires appropriate configuration of the storage system.
vCluster supports PV snapshots — tenants trigger VolumeSnapshots via standard Kubernetes APIs and vCluster handles the rest. Automated backups can be configured on a time interval or run via CRON schedule.
vCluster Platform provides automated backups and exposes any status and metadata information about backups including providing the ability to verify backup validity.
vCluster works with any high-performance storage solution and our team supports hands-on in configuring the storage-related integrations and automations for particular data center setup.
InfiniBand and RoCEv2 fabric automation is provided by the network infrastructure partner. vCluster integrates with Netris, which automates east-west fabric configuration, tenant isolation, PKey assignment via UFM, and Spectrum-X host networking via the NHN plugin.
vCluster's Private Nodes model runs MPI workloads directly on bare metal — no virtualization layer between the MPI processes and the network fabric. MPI, TorchElastic, Ray, and JAX perform as they would on a native cluster with zero overhead added. HPC-X installation and configuration is the operator's responsibility.
NCCL configuration (/etc/nccl.conf) is set by the operator in their GPU node OS image. vMetal provisions nodes using the operator's own ISO — no OS-level modifications are made. Operators include NCCL config in their base image or deploy it via init scripts.
NCCL_IB_GID_INDEX=3 is a pre-NCCL 2.21 requirement. Since NCCL 2.21, GID index is auto-selected based on active link layer — manual configuration is no longer needed. For operators running NCCL 2.21+, this is handled automatically.
vCluster Platform and vMetal do not set NCCL_MIN_NCHANNELS, NCCL_PROTO, or NCCL_ALGO. vMetal provisions nodes using the operator's own OS image without modification — no NCCL environment variables are injected at any layer. NCCL auto-tuning runs unobstructed.
SHARP (Scalable Hierarchical Aggregation and Reduction Protocol) accelerates NCCL collective operations when NVIDIA Quantum InfiniBand switches are present. Configuration is managed via NVIDIA UFM through the network fabric partner.
The 4-node NCCL bandwidth test is a provider validation benchmark — the operator runs and certifies this against their hardware configuration. vCluster's Private Nodes model ensures no platform overhead is added to the NCCL data path, but benchmark execution and pass/fail certification is the operator's responsibility.
PyTorch distributed performance benchmarking is a provider validation responsibility. vCluster adds no overhead to the GPU compute or network data path — PyTorch workloads on Private Nodes run at native bare-metal performance.
NCCL communication observability is available via NVIDIA DCGM Exporter (deployed as part of GPU Operator) for GPU-level metrics, and via NVIDIA's NCCL Inspector Profiler Plugin (released December 2025) for per-communicator, per-collective performance monitoring. Both integrate with Prometheus and Grafana.
Automated performance testing for new tenant environments is on the roadmap. Currently, vCluster helps with manual performance testing but an in-product solution is coming soon.
NCCL communication observability is available via NVIDIA DCGM Exporter, which deploys automatically as part of GPU Operator.
Network stability monitoring across the IB and Ethernet fabric is managed by the network infrastructure partner. Netris provides continuous switch-level health monitoring including interface status, BGP state, topology/wiring errors, and hardware health across all managed nodes.
InfiniBand and Ethernet link flap monitoring is handled at the network infrastructure layer by the fabric partner. Netris provides continuous interface up/down monitoring across all managed switches and surfaces events to the operator.
InfiniBand and Ethernet link flap events are monitored at the fabric layer by the network infrastructure partner. Netris provides continuous interface status monitoring across managed switches, alerting on link state changes.
InfiniBand health monitoring — link status, error counters, and PKey consistency — is managed at the fabric layer via NVIDIA UFM, orchestrated by the network infrastructure partner. Netris integrates with UFM for IB fabric management and monitoring.
DCGM Exporter tracks NVLink bandwidth and error metrics per GPU. Critical for NVL72 configurations. Deployed via GPU Operator on vCluster — operators configure Prometheus alerts for NVLink error thresholds.
InfiniBand link status monitoring is managed at the fabric layer via NVIDIA UFM, orchestrated by the network infrastructure partner. Netris automates fabric configuration. Operators requiring ibstat-level granularity must run IB diagnostic tooling directly.
PKey consistency across InfiniBand nodes is maintained by Netris via its NVIDIA UFM integration. The Netris-UFM reconciliation loop (10-second interval) continuously verifies and enforces PKey assignments across all fabric nodes.
SLA commitments are the operator's responsibility to define and publish. vCluster Platform's architecture enables higher SLAs: control plane HA (multi-replica with embedded etcd or external DB), automated node lifecycle via vMetal, and tenant isolation ensuring one tenant's failure does not affect others.
NVIDIA DCGM Exporter (deployed via GPU Operator on vCluster) tracks XID error codes including GPU bus fault events. Operators connect Prometheus to receive alerts. vCluster tenant isolation scopes blast radius to the affected node only.
DCGM Exporter tracks PCIe replay counters and bus error events. Deployed automatically via GPU Operator on vCluster — operators connect Prometheus scraping.
DCGM Exporter tracks GPU and memory temperature on all GPU nodes. Operators connect Prometheus and configure thermal threshold alerts. NVIDIA's default Grafana dashboard includes thermal panels.
DCGM Exporter tracks GPU ECC error rates per GPU. Deployed via GPU Operator on vCluster. CPU memory ECC monitoring requires a separate IPMI exporter deployed by the operator.
DCGM Exporter tracks XID and SXID error codes per GPU. Deployed via GPU Operator on vCluster — XID events surface through Prometheus alerting. vCluster tenant isolation scopes impact to the affected tenant only.
NCCL operation observability is available via NVIDIA's NCCL Inspector Profiler Plugin. Operators include the inspector .so library in their GPU workload container image and set NCCL_PROFILER_PLUGIN and NCCL_INSPECTOR_ENABLE=1 in their pod specs. The plugin runs entirely in-process — no cluster-level changes, no DaemonSet, no privileged containers required. Works natively inside vCluster pods.
vCluster surfaces Kubernetes node health conditions through the virtual control plane API server — including node Ready, MemoryPressure, DiskPressure, and PIDPressure conditions. Platform-level node health is further enriched by vMetal's bare metal lifecycle monitoring. Operators access node health via standard kubectl or any K8s observability tooling.
GPU Operator enforces consistent NVIDIA driver and toolkit versions as a DaemonSet across all GPU nodes in the cluster. Version drift is detected and corrected by the operator reconciliation loop automatically.
DCGM Exporter tracks single-bit and double-bit ECC errors per GPU. Deployed via GPU Operator on vCluster — operators configure Prometheus alerts for ECC thresholds.
DCGM Exporter tracks GPU temperature and thermal throttling events. Operators configure Prometheus alerting rules for threshold detection. NVIDIA's default Grafana dashboard includes thermal monitoring panels.
DCGM Exporter tracks GPU power draw and total energy consumption per GPU. Operators connect Prometheus for per-tenant power utilization dashboards and chargeback via PromQL aggregation by namespace.
DCGM Exporter tracks XID and SXID error codes per GPU via GPU Operator. XID events surface through Prometheus. vCluster tenant isolation scopes blast radius to the affected tenant only.
DCGM Exporter tracks PCIe bus health and power state metrics. Deployed via GPU Operator on vCluster — operators configure Prometheus alerting for anomalies.
IPMI/BMC telemetry for fan speed and hardware health is the operator's responsibility to configure. vMetal (Metal3) uses BMC for node provisioning and power management but does not expose ongoing IPMI telemetry. Operators deploy a standalone IPMI exporter on their nodes to feed hardware metrics into Prometheus.
GPU-layer error counters (retries, ECC errors) are available via DCGM Exporter deployed through GPU Operator. Network-layer packet drop and retry counters are the operator's responsibility — Netris provides network automation but not deep flow-state telemetry.
NCCL operation health tracking is available via NVIDIA's NCCL Inspector Profiler Plugin. Operators include the inspector .so library in their GPU workload container images and enable it via environment variables (NCCL_PROFILER_PLUGIN, NCCL_INSPECTOR_ENABLE=1). The plugin runs entirely in-process with no Kubernetes footprint — works natively inside vCluster GPU pods.
vMetal handles the full bare metal node lifecycle today — including deprovisioning, reimaging, and returning nodes to the available pool for reassignment. What is on the roadmap is automated health-based triggering: detecting a GPU fault via DCGM, automatically draining the node, deprovisioning, and reprovisioning without operator intervention. Today that remediation chain requires operator action.
ncu is available via the operator's container image / NVIDIA toolkit.
kube-prometheus-stack (including Grafana) deploys normally inside each tenant cluster. DCGM Exporter integrates with Prometheus to surface GPU utilization, memory, power, and error metrics per tenant — all scoped to the individual tenant with no cross-tenant visibility. Operators can offer managed Grafana dashboards as a platform feature.
TFLOPs estimation is the operator's responsibility to benchmark and publish. DCGM Exporter surfaces GPU utilization and SM clock data that operators can use to derive effective TFLOPs, but vCluster does not compute or track TFLOPs natively.
Real-time GPU and system monitoring is available via DCGM Exporter (GPU metrics at ~1s granularity) and kube-prometheus-stack — both deployable inside each tenant cluster. Operators connect their Prometheus instance to scrape DCGM metrics and visualize in Grafana in real time.
Per-tenant GPU performance tracking is available via DCGM Exporter — surfacing GPU utilization, SM occupancy, memory bandwidth, and power draw per namespace. Operators aggregate metrics by tenant cluster namespace for per-tenant performance visibility.
Per-tenant resource utilization monitoring is available by deploying GPU Operator, DCGM Exporter, and kube-prometheus-stack inside each tenant cluster, with ServiceMonitors configured to scope metrics by namespace. vCluster's tenant isolation ensures each tenant only sees their own metrics.
Prometheus Alertmanager is the alerting layer — deployable alongside kube-prometheus-stack inside each tenant cluster. Operators configure alert rules against DCGM metrics (XID errors, temperature thresholds, ECC errors, utilization). Per-tenant isolation ensures alert rules and notification channels are scoped to individual tenants.
Passive health checks are available via DCGM Exporter (GPU metrics) and Kubernetes node conditions (Ready, MemoryPressure, DiskPressure) — both accessible within each tenant cluster. Active health checks require operator-configured tooling such as scheduled NCCL tests or GPU burn-in jobs.
GPU burn-in testing and documentation is the operator's responsibility. vMetal provisions bare metal nodes but does not run or document burn-in tests — it is Metal3 under the hood (PXE, OS provisioning, lifecycle management only).
Comprehensive passive GPU health monitoring is available via DCGM Exporter deployed through GPU Operator on vCluster — covering temperature, power, ECC errors, XID codes, PCIe health, NVLink status, and utilization. Kubernetes node conditions provide host-level passive health. Together they cover the full passive health check surface.
Automated active health checks (GPU burn-in, DGEMM benchmarks, NCCL tests) are the operator's responsibility to configure and schedule. vCluster supports running these as Kubernetes Jobs or CronJobs within tenant clusters — the isolation model ensures health check jobs do not interfere with other tenants.
Operators have full kubectl access to each tenant cluster for standard Kubernetes diagnostics. GPU diagnostics are available via DCGM (XID codes, ECC errors, health validation). NVIDIA GPU Operator includes a validator component that runs diagnostic checks at node startup.
vMetal supports node deprovisioning and reimaging today — nodes can be drained, wiped, and returned to the available pool. The automated fault-detection-to-action pipeline (detect GPU fault via DCGM, automatically drain, deprovision, reprovision) is on the roadmap via Auto Nodes + vMetal + vCluster. Today operator action is required to trigger remediation.
AI-based failure prediction is the operator's responsibility to configure. vCluster provides the observability data layer (DCGM metrics, K8s events) that an ML-based failure prediction system can consume.
vCluster Platform provides per-cluster resource quota enforcement and usage metering via vBilling. Operators can expose per-tenant utilization as showback dashboards via Grafana. Chargeback and invoicing to end customers requires connecting an external billing platform. vBilling provides the metering data layer.
GPU pricing is the operator's hardware and business decision. vCluster reduces operational overhead — fewer ops engineers needed to manage multi-tenant K8s at scale — which can lower total platform cost and improve margins. vMetal reduces time-to-provisioned-node, reducing idle GPU costs.
Contract and consumption models are the operator's commercial decision. vCluster Platform's metering layer provides the usage data needed to support any billing model — on-demand, reserved, or tiered — but the contracts themselves are the operator's responsibility.
vCluster Platform provides per-tenant resource quota enforcement and usage metering via vBilling, giving operators the granular consumption data needed to support unbundled billing for compute, storage, and GPU resources. Integration with an external billing system is required for actual invoicing and chargeback.
Contract expansion and renewal is a commercial decision for the operator. vCluster Platform makes it operationally easy to scale existing tenants — adding nodes, increasing resource quotas, or spinning up new tenant clusters requires no new physical clusters.
GPU hardware acquisition and availability timelines are the operator's business decision. vMetal (Metal3) accelerates time from physical node to registered Kubernetes worker — any new GPU SKU is supported without platform changes. The operator controls the hardware roadmap.
Performance kernel libraries are the operator's responsibility to bundle in their base OS image or make available to tenants. vCluster does not ship or manage ML kernel collections. Tenants can install libraries inside their tenant cluster environments independently.
NVIDIA is a close technology partner with vCluster, not a current investor.
NCP (NVIDIA Cloud Partner) is a certification program exclusively for GPU cloud infrastructure providers — vCluster Labs as a software vendor is not eligible and does not claim NCP status. vCluster helps AI cloud operators satisfy the technical requirements of NCP certification: GPU Operator compatibility, DCGM integration, IB/RoCEv2 networking stack, and multi-tenant isolation. Operators using vCluster can reference this compatibility when pursuing NCP certification.
AMD Cloud Alliance membership is a provider-level certification. vCluster Platform supports AMD GPU workloads via the AMD GPU Operator on Kubernetes — hardware-agnostic at the platform layer. AMD Cloud Alliance status is the operator's credential to pursue.
No current SchedMD partnership. SLURM support is on the roadmap for H2 2026.
The full Kubernetes and NVIDIA GPU ecosystem works inside a tenant cluster without modification — GPU Operator, Network Operator, DCGM, KAI Scheduler, Prometheus, Grafana, ArgoCD, and all standard CNCF tooling. vCluster is CNCF-compatible and does not require custom integrations or ecosystem modifications.
vCluster Labs actively participates in major GPU infrastructure and Kubernetes events — including KubeCon North America, KubeCon Europe, NVIDIA GTC, and SC (Supercomputing). The team presents on GPU tenant isolation, vNode security, and AI infrastructure architecture.
vMetal supports any bare metal GPU node — H200, B200, GB200, and NVL72 hardware register into the platform without software changes. New GPU SKUs require no platform redesign.
Current GPU model availability is the operator's hardware decision. vMetal supports any bare metal GPU node — H100, H200, A100, L40S, and MI300X hardware all register into the platform identically. Hardware-agnostic provisioning means no per-SKU configuration.
Hardware acquisition timelines are the operator's business decision. vMetal supports any new GPU SKU without platform changes.
Fleet size is the operator's hardware investment decision. vCluster Platform enables scale without cluster sprawl — hundreds of tenant clusters run on a single control plane cluster, keeping per-cluster operational overhead flat regardless of GPU count.
Geographic reach is the operator's infrastructure and business decision. vCluster Platform's multi-region mode supports distributed control planes across regions.
Uptime and utilization SLAs are the operator's commitment. vCluster Platform improves utilization economics — shared infrastructure with strong tenant isolation reduces idle GPU waste versus cluster-per-tenant approaches. Control plane HA ensures platform availability independent of individual node health.
vCluster Platform enables capacity planning without linear operational scaling — adding new tenants, expanding resource quotas, or onboarding new GPU nodes requires no new physical clusters. Operators manage all tenants from a single control plane with full visibility into resource allocation and utilization across the fleet.
GPU acquisition roadmaps are the operator's hardware and financial planning responsibility. vCluster Platform makes adding new nodes to existing tenant clusters operationally simple — no new clusters or configuration required.
Enterprise AI teams use ClusterMAX to make six- and seven-figure infrastructure decisions. Each rating improvement translates directly to pipeline and contract value.
Enterprise buyers filter ClusterMAX tiers before shortlisting providers. A higher rating gets you into more RFPs and reduces time spent on procurement due diligence.
vCluster turns raw GPU nodes into a fully managed Kubernetes offering in under a day. Faster time-to-market means more customers onboarded before competitors catch up.
vCluster is now explicitly cited in the ClusterMAX Security criterion. Customers reading the criteria see your implementation listed as the requirement, not an alternative.
vCluster lets you serve 100 enterprise tenants on a shared GPU fleet without managing 100 separate physical clusters, keeping OpEx flat as you scale to meet Availability scoring.
Private Nodes and dedicated control planes give each customer the isolation of a dedicated environment at a fraction of the cost, directly improving Security and Orchestration scores.
GPU Operator, NCCL, MIG, DCGM, and distributed training frameworks all work natively inside vClusters, satisfying the Orchestration, Storage, Networking, and Monitoring criteria simultaneously.
A blueprint for bringing cloud-grade elasticity and automation to NVIDIA DGX systems.
vCluster and Netris integrate Kubernetes and network automation.
Learn how to deliver enterprise-grade Kubernetes for AI workloads and improve ClusterMAX™ rating.
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