Data scientists wait days for GPUs.
It should take minutes.
GPU VMs and managed Kubernetes clusters — pre-validated, production-ready, across five providers. Self-service within guardrails. No driver debugging. No ticket queues.
GPU budgets are tripling.
Where is the money going?
GPU spend attributed per project and provider — in one dashboard. Utilization metrics show whether expensive GPUs are working or idle. Get cost visibility before the bill arrives.
Five clouds. Five consoles for AI infrastructure.
One platform engineering team.
Each provider has its own provisioning API, networking model, and compliance surface. emma consolidates all of it into a single operational layer.
GPU infrastructure is running outside your governance perimeter.
Fix that.
RBAC, tagging, cost attribution, and audit trails — applied to GPU workloads the same way they're applied to everything else. Governed inference templates. No shadow AI infrastructure.
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See it running. 45 minutes.
GPU provisioning, monitoring, networking, and governed inference — live. No slides.
No procurement process. No driver setup. No provider-specific workflow. Select, provision, and deploy — across any of five GPU providers.
GPU VMs across five providers
Select GPU type, provider, region, and image in the emma VM wizard or via API. Same flow across AWS, GCP, Azure, emma, and Nebius.
Pre-validated NVIDIA images
VMs launch with driver-optimized ML/DL images. Working environment from first boot — not a day of driver debugging.
GPU K8s in under 5 minutes
Fully managed Kubernetes with GPU node pools across AWS, Azure, and GCP. Pre-validated CUDA. No cluster ops.
86% of enterprises expect AI infrastructure budgets to more than triple. emma gives you attribution and utilization data so the money goes where it should.
Unified cost dashboard
One view across AWS, GCP, Azure, emma, and Nebius. Filter by project, by provider, by GPU type. No spreadsheet reconciliation.
Utilization as evidence
Three metrics per GPU VM. Nine per K8s cluster. The data you need to right-size before the next bill, not after.
Cost preview before deployment
Inference workflow templates include cost preview. Every deployment attributable. See costs before the workload runs.
Your team shouldn't need per-provider expertise to operate GPU infrastructure. emma absorbs the API, networking, and compliance differences so one workflow works everywhere.
Single control plane
Provision GPU VMs and managed K8s across AWS, GCP, Azure, emma, and Nebius from one interface. Same workflow regardless of provider.
High-speed cross-cloud connectivity
GPU workloads connected across providers through emma's private networking backbone. Training on one provider, inference on another — low latency, no manual config.
Reduce egress cost
Data transfer via emma's backbone instead of public internet routing. The hidden networking tax of multi-cloud AI architectures — eliminated.
Every GPU VM, every cluster, every inference deployment — inside your governance perimeter from the moment it's provisioned. Not retrofitted after an audit finding.
Same RBAC, same policies
GPU VMs can be assigned your existing governance model. Tags can be enforced at provisioning.
Governed inference templates
Reusable deployment templates with guardrails — instance limits, parameter constraints, RBAC. Self-serve within policy.
Full audit trail
Every GPU VM lifecycle event, every cluster provision, every inference deployment is auditable. Governed by design.
Ready to make this real?
Book a demo →One platform. Five operational layers.
emma is not a cloud provider. We don't own your infrastructure, lock you in, or compete with your cloud vendors. We operate across them.