Pricing

See Orion in action

Pricing

See Orion in action

Products

Pricing

Solutions

Integrations

Company

See Orion in action

Run every workload like it's one machine.

Run every workload like it's one machine.

Bring your GPUs, VMs, containers, and bare metal under one API. 60-second provisioning, typically 2–4× more capacity from the hardware you already own, no cloud dependency required.

Bring your GPUs, VMs, containers, and bare metal under one API. 60-second provisioning, typically 2–4× more capacity from the hardware you already own, no cloud dependency required.

Bring your GPUs, VMs, containers, and bare metal under one API. 60-second provisioning, typically 2–4× more capacity from the hardware you already own, no cloud dependency required.

See Orion in action

Deploy free ↓

The problem

Running GPU and CPU workloads has meant choosing between two compromises.

Running GPU and CPU workloads has meant choosing between two compromises.

Neither option was designed for a world where GPU access is table stakes, data sovereignty matters, and 10–15% utilization is no longer acceptable.

Neither option was designed for a world where GPU access is table stakes, data sovereignty matters, and 10–15% utilization is no longer acceptable.

Option A

Hyperscaler GPU rental

Fast to start. Expensive to scale. Your data leaves your environment. Egress costs compound. You're renting capacity on someone else's terms, with no path to sovereignty.

Option B

DIY Kubernetes + VMware glue

You own the hardware. But you've stitched together five tools to manage it and need three engineers just to keep the lights on.

How Orion changes the math

Two types of teams come to Juno. Here's what changes for each.

Cloud & AWS

You're over-provisioning. Every month.

Cloud-first teams provision for peak and pay for idle the rest of the time. Orion replaces that with per-request autoscaling — the right node size spins up when demand arrives, GPU operators install automatically, and the cluster scales back when the work is done. No stair-step. No idle spend.

On-Prem & Data Center

Your end users shouldn't need to know Kubernetes.

On-prem teams own the hardware. But every workload request still bottlenecks through an engineer who knows K8s. Orion removes that. Workload templates define the environment once. Users request what they need, and a workstation, container, or VM is running in 60 seconds. No YAML. No ticket queue.

Not sure which fits you? Most teams are running both — cloud for burst, on-prem for everything else. Orion handles that from a single compute plane.

One compute plane. Every workload type.

One compute plane. Every workload type.

GPU orchestration, VM management, and bare metal scheduling from the same cluster. No stack rewrite needed.

GPU orchestration, VM management, and bare metal scheduling from the same cluster. No stack rewrite needed.

Pod Resource Monitor
pods[5] </prod>
NAMESPACENAMEPFREADYSTATUSRESTARTSCPU%CPU/RMEM%MEM/RAGE
prodjupyter-nb-train-0●1/1Running0272527238333746m28s
prodalphafold-pred-0●1/1Running0227322742314134m6s
prodchrome-ws-danny●2/2Running04066172m6s
prodopenfoam-cfd-0●1/1Running033914257271758m
prodmaya-render-v12●1/1Running0369378428262s
CPU: 43% MEM: 26%K8s: v1.34.1

Live pod allocation across the cluster *

Compute Resource Slicing

Compute Resource Slicing

Orion installs native GPU operators and configures slicing automatically. Multiple concurrent workloads share a single node without contention or manual setup. R3D Studios achieved 2:1 GPU density without adding a single node.

Orion installs native GPU operators and configures slicing automatically. Multiple concurrent workloads share a single node without contention or manual setup. R3D Studios achieved 2:1 GPU density without adding a single node.

GPU Time-Slice Monitor
Node / GPUUserUtilizationJob
● gpu-node-01
GPU 0
alice
40%
jupyter-nb-train
GPU 1
bob
33%
gromacs-md-sim-04
GPU 2
alice
0%
—
GPU 3
bob
57%
chrome-workstation
● gpu-node-02
GPU 0
alice
66%
alphafold-pred-03
GPU 1
alice
50%
openfoam-cfd-run7
GPU 2
bob
24%
jupyter-nb-infer
GPU 3
charlie
39%
maya-render-v12
3 users · 7 GPUs active · 0 queuedUpdated now

GPU time-slice allocation — real-time *

Many Users, One GPU

Every workload request is a scheduling event. Orion allocates GPU slices in real time as requests arrive. Each user gets exactly the resources their job requires, when they need them. No pre-allocation. No idle hardware waiting for work that isn't coming.

Every workload request is a scheduling event. Orion allocates GPU slices in real time as requests arrive. Each user gets exactly the resources their job requires, when they need them. No pre-allocation. No idle hardware waiting for work that isn't coming.

* Visualizations represent live cluster state and are illustrative of Orion's orchestration behavior.

Deploy Today

This won't take long.

This won't take long.

Helm install. Any CNCF-conformant Kubernetes distribution. Running in under two minutes.

curl -sL "$(curl -s https://api.github.com/repos/juno-fx/Juno-Bootstrap/releases/latest | grep browser_download_url | grep orion-install-helper | cut -d '"' -f 4)" | bash -

Full install guide →

See Orion in action

Watch Orion turn idle compute into productive capacity — native GPU operators, Helios containerized workstations, and provisioning that takes seconds, not hours.

Watch more on YouTube ↗

What teams are saying about Juno

Built-in management tools that keep your team aligned

Hear how teams doubled GPU capacity without adding hardware.

Old tools keep your finance team reactive. Vectura helps you lead with clarity.

Donald Strubler

Head of Technology, R3D Studios

"Orion shifted our focus from finding stability to using the stability to iterate."


~40%

Compute cost reduction

60 sec

User request to workload running

Breakthroughs run on Juno.

R3D runs production stereoscopic 3D conversion on Orion. What's your team's breakthrough?

Start your breakthrough →

Start your breakthrough →

Orion demo video