Training data on one provider. Inference on another. Connected.
emma extends its multi-cloud networking backbone to AI workloads. On-demand connectivity between GPU infrastructure across providers — without per-pipeline networking projects.
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59%
of organizations report bandwidth issues with AI workloads — up from 43%. Flexential State of AI Infrastructure
53%
report latency concerns — surged from 32% the year prior. Flexential State of AI Infrastructure
$70–80B
in annual cloud data transfer fees industry-wide. Cross-cloud AI architectures carry a hidden networking tax. McKinsey 2026
High-speed backbone
Low-latency connections between GPU workloads across providers. No manual network configuration per cloud.
Reduced egress costs
emma’s backbone reduces cloud-to-cloud data transfer fees. Cross-cloud AI architectures no longer carry a hidden networking tax.
Distributed AI foundation
GPU compute, data, and serving endpoints can span multiple providers without per-provider network engineering.
emma's networking layer is built into the platform — not added as an integration. When you provision a GPU VM or managed K8s cluster, networking is available from the moment the resource boots.
On-demand virtual networks
Create cross-cloud virtual networks connecting resources across providers. Private IPs. No public internet routing. No manual VPC peering, transit gateways, or route table configuration.
Integrated, not installed
Networking is part of the emma platform — not a separate tool to procure, configure, and maintain. When you provision infrastructure, connectivity is already there.
Observable and governed
Network traffic between GPU workloads is visible in emma's monitoring interface. Data movement across providers happens within your governance perimeter — not outside it.
Training data on AWS. GPU training on Nebius.
Your data sits on S3. The best GPU pricing is on another provider. emma's backbone connects them — no manual data migration, no egress surprise, no networking project.
Model trained on GCP. Inference served from Azure.
Training where the GPU capacity is best. Serving where the customer workloads run. The model artifact moves across providers through the backbone, not the public internet.
GPU preprocessing on one cloud. Feature store on another.
GPU-accelerated ETL on the cheapest GPU instance. Results written to your feature store on a different provider. Connected through private networking — no data landing on the open internet.
Multi-region inference for latency.
Serve inference endpoints on the provider closest to each user population. emma's backbone connects models and data stores across regions and providers without per-region networking configuration.
No public internet transit
GPU workload data moves between providers through emma's private backbone — not over the public internet. Reduced attack surface. No data exposure during transit.
Governed connectivity
Network connections between GPU resources are governed by the same policy layer as everything else on emma. RBAC controls who can create cross-cloud connections.
EU data residency
For regulated workloads, emma's backbone supports data movement between EU-resident providers without routing through non-EU infrastructure. Sovereignty maintained at the network layer.
Public internet
Default for most cross-cloud AI architectures. Highest latency, highest cost, no governance. Every cross-cloud data movement is an egress line item and a security risk.
Cloud-native transit
AWS Transit Gateway, Azure Virtual WAN, GCP Cloud Interconnect — each provider’s own networking solution. Per-provider configuration. No unified cross-cloud view. Complexity scales with each cloud added.
emma’s integrated backbone
Private networking across 15+ providers. Built into the platform. On-demand virtual networks. Private IPs. Governed. Observable. One configuration model regardless of how many clouds you operate.
How does emma's networking backbone work?
emma operates a private networking backbone across 15+ cloud providers. When you provision GPU resources on emma, they can connect to resources on any other emma-connected provider through on-demand virtual networks with private IPs — no public internet routing, no manual VPC peering.
What does "400 Gbps" refer to?
400 Gbps is the aggregate capacity of emma's networking backbone. Individual connection speeds depend on provider, region, and workload — but the backbone is designed for high-throughput, low-latency data movement between GPU workloads across clouds.
How much can I save on egress costs?
Up to 70% compared to public internet routing. The exact savings depend on your cross-cloud data transfer volumes, which providers you use, and which regions. The demo walkthrough can model savings based on your specific architecture.
Can I use the backbone for distributed model training?
emma's backbone connects GPU workloads across providers and reduces data transfer costs. However, the current release does not include latency optimization specifically for distributed training synchronization. It's well suited for moving training data, model artifacts, and serving inference across providers — less suited for tight-loop gradient synchronization across clouds.
Do I need to configure VPCs or peering connections?
No. emma's networking is built into the platform. When you provision GPU VMs or K8s clusters, cross-cloud connectivity is available through on-demand virtual networks. No VPC peering, no transit gateways, no route table management.
Is cross-cloud traffic encrypted?
When traffic between GPU workloads travels through emma's backbone and not the public internet, data in transit is encrypted. The backbone is governed by the same security and compliance policies as all other emma resources.
Does the backbone support EU data residency requirements?
Yes. emma's backbone can route data between EU-resident providers without traversing non-EU infrastructure. For regulated workloads requiring full EU data residency, sovereignty is maintained at the network layer.
How does this compare to using AWS Transit Gateway or Azure Virtual WAN?
Cloud-native transit solutions work within one provider's ecosystem. Each provider has its own configuration model. emma's backbone works across all connected providers — one configuration model regardless of how many clouds you operate. No per-provider networking setup.
See cross-cloud networking in a working multi-cloud topology.
45-minute walkthrough. GPU provisioning, networking, and governed inference. Live.
Get a demo →