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Inferonomics

Hybrid GPU Fabrics: On-Prem First, Cloud Burst

  • hybrid
  • GPU
  • on-prem
  • cloud
  • InferoFabric

Many enterprises want to run AI and ML workloads on-premises first—for data gravity, compliance, or cost—but still need the option to burst to the cloud when capacity or region is insufficient. InferoFabric by Inferonomics is built for this on-prem first, cloud burst model: one fabric, one API, multiple back ends.

Why on-prem first

Reasons to keep the primary GPU footprint on-prem include:

  • Data gravity — Training or inference on large, sensitive datasets is simpler when data does not leave the datacenter.
  • Compliance — Regulated industries often require certain workloads to run in controlled environments; cloud may be allowed only for specific, approved burst cases.
  • Cost — At steady state, owned or leased GPUs can be cheaper than list-price cloud; cloud is then used for peaks, not baseline.

The challenge is making on-prem and cloud feel like one pool: same APIs, same images, same policies, so that "burst" does not mean a separate stack or a rewrite.

Single fabric, multiple back ends

InferoFabric models GPUs as a single logical fabric. You register on-prem clusters and cloud accounts (e.g. AWS, GCP, Azure) as capacity pools. Scheduling and placement are handled by the control plane; workloads are submitted once and can be placed on-prem or in the cloud based on policy and availability.

How InferoFabric unifies on-prem and cloud

InferoFabric uses a control plane that knows about all registered pools (on-prem Kubernetes clusters with GPU nodes, cloud accounts/projects). Workloads are defined in a cloud-agnostic way: container image, resource requests (e.g. GPU type and count), and optional placement hints or constraints.

  1. —

    Register capacity pools

    On-prem: install the InferoFabric agent on your Kubernetes cluster and register it as a pool. Cloud: connect an account or project via OIDC or API keys. The control plane discovers GPU capacity and health in each pool.

  2. —

    Submit workloads once

    You submit jobs or deploy inference endpoints to InferoFabric, not to a specific cluster or cloud. The scheduler places them according to policy: e.g. "prefer on-prem; burst to cloud only if on-prem is full or unavailable."

  3. —

    Burst and failover without app changes

    When on-prem is at capacity or a pool is down, the same workload spec can be placed in the cloud. No change to your code or container image; only the execution back end changes.

Placement can be driven by cost, compliance, or availability. Example placement policy for hybrid burst:

placement:
  default: on-prem
  burst:
    when: on_prem_utilization > 0.85
    target: cloud
    regions: [us-east-1, eu-west-1]
  failover:
    when: pool_health != healthy
    target: cloud

Consistent runtime

Whether a job runs on-prem or in the cloud, InferoFabric uses the same container runtime, same image registry, and same networking model. Operators get one place to set policies (e.g. auto-suspend, quotas) that apply everywhere.

Operational considerations

  • Networking — Burst workloads in the cloud need access to data or services. InferoFabric supports VPN or private link patterns so that cloud-run jobs can reach on-prem or shared data stores in a controlled way.
  • Credentials — Image pull and secrets are managed by the control plane; the same credentials can be used for on-prem and cloud pools, or you can scope them per pool for stricter isolation.
  • Cost visibility — InferoFabric tracks usage per pool and per workload, so you can see exactly what ran on-prem vs. cloud and attribute burst cost to teams or projects.

Data and compliance

Bursting to the cloud may involve moving or copying data. Ensure your data classification and compliance rules are reflected in placement policies (e.g. "no PII in cloud" or "only these regions") so InferoFabric does not place restricted workloads in the wrong pool.

Summary

InferoFabric by Inferonomics gives you a hybrid GPU fabric with on-prem as the default and cloud as burst and failover. One control plane, one API, and one set of policies across all pools reduces operational complexity and keeps workloads portable without rewriting them for each environment.