Built for inference and training
One platform for GPU orchestration, cost control, and hybrid placement.
Cost caps + auto-suspend
Set spend limits and idle thresholds. Workloads suspend automatically so you never overspend.
Inference endpoints + autoscaling
Deploy served models with scale-to-zero and configurable scaling policies. Pay only for what you use.
Training jobs + checkpoints
Run distributed training with managed checkpoints and spot-aware scheduling. Resume from last state.
Verified stack blueprints
Pre-validated environments (CUDA, frameworks, drivers) so you ship faster with fewer runtime surprises.
Hybrid placement (on-prem first, cloud burst)
Place workloads on your own clusters first; burst to cloud when capacity or policies require it.
How it works
Three layers: one control plane, agents on your clusters, and policies that enforce cost and placement.
Console + control plane
Manage orgs, projects, and environments from the InferoFabric console. The control plane handles placement, quotas, and policy.
Agent + worker on clusters
Lightweight agents run on your Kubernetes clusters (on-prem or cloud). Workers execute GPU workloads and report usage back.
Policies + observability + cost enforcement
Define placement rules, cost caps, and autoscaling. Monitor usage and spend; enforcement runs automatically.
Quick ROI estimate
Built for platform teams
Control, visibility, and governance without slowing down ML teams.
Single pane of glass
One console for all GPU workloads: inference endpoints, training jobs, and environments.
Policy-driven placement
Define rules like on-prem first or cost-cap per project. The control plane places workloads automatically.
Observability and cost attribution
See usage and spend by project, team, or workload. Enforce caps and get alerts before limits are hit.
Learn more
Watch our videos to understand the company, product, and technical architecture.
Vision & Differentiation
Why Inferonomics built InferoFabric and how we approach GPU orchestration differently.
Product Explainer
A walkthrough of InferoFabric: control plane, workloads, and key capabilities.
Technical Architecture Deep Dive
Architecture and features: hybrid placement, blueprints, cost control, and observability.
See InferoFabric in action
Get a tailored walkthrough: hybrid placement, cost caps, and verified stacks.
Frequently asked questions
Common questions about InferoFabric and GPU orchestration.