Own your inference stack.
Deploy InferX on your own GPUs, Kubernetes cluster, private cloud, bare metal, or GPU cloud. Reduce inference infrastructure costs by 30–70%* with fast cold starts, scale-to-zero, isolated workloads, and OpenAI-compatible APIs.
Owning GPUs is not the same as operating inference.
Capacity gap
Idle GPU capacity
Provisioned capacity waits between workloads.
Slow model activation
Cold paths add latency before traffic is served.
Low utilization
Useful work is separated from available hardware.
Fragmented serving stacks
Teams operate separate serving paths.
Security and isolation concerns
Controls get harder as deployments spread.
How InferX fits into your environment.
Your models, traffic, and infrastructure stay under your control.
View Trust Center →Deploy where your infrastructure already lives.
Kubernetes
EKS, GKE, AKS, OpenShift, on-prem Kubernetes.
Bare metal GPU servers
Dedicated GPU servers without moving workloads.
Private cloud
Keep model traffic and operations private.
On-prem enterprise environments
Deployment planning for regulated environments.
GPU cloud providers
An inference layer across rented GPU capacity.
Restricted environments
Reviewed during discovery where applicable.
Built for production inference workloads.
Fast cold starts from restored runtime state
Scale-to-zero when idle
OpenAI-compatible APIs
Production endpoint experience from hosted InferX runtime
Real enterprise/private deployment conversations
How deployment works.
Discovery
Map workloads, models, GPUs, access, and security needs.
Pilot
Install InferX on a limited GPU pool.
Validation
Measure cold starts, latency, utilization, and API compatibility.
Production Rollout
Expand across the approved deployment boundary.
Ongoing Support
Support, monitoring, upgrades, and capacity planning.
Platform pricing that matches your infrastructure.
InferX platform pricing is structured around a platform license, deployment/support package, and cluster size or GPU footprint.