Orama Studio · deployment

Your cloud, on-prem, or dedicated

Orama Studio deploys in your own cloud, on-premises, or on dedicated infrastructure. Wherever it runs, the cluster underneath is true hybrid: CPUs, GPUs, and ASICs from any vendor, across operating systems, architectures, and multiple clouds at once, squeezing the most out of whatever hardware you give it.

01 · Where it runs

Deployed onto infrastructure you run.

Your cloud

Provision Orama Studio nodes into your own AWS, Azure, GCP, or Oracle Cloud account, or your Kubernetes cluster. The platform runs under your identity provider and your controls.

On-premises

Run entirely within your own data center for the most regulated environments, with the same workflow and the same governance, and no dependency on a public cloud.

Dedicated

Run on dedicated infrastructure isolated to your organization. Start on a single machine to validate a use case end-to-end, then promote the same graph to a shared target.

A control plane coordinates distributed nodes running across your cloud, on-prem, and dedicated targets, one cluster across all three.

02 · The payoff

Control over cost, flexibility, and resilience.

Because the cluster is never bound to one vendor's hardware, prices, or availability.

Cost

Run each workload on the cheapest hardware that fits, and spend capacity you already own or have idle before renting more. Burst into a cloud only when needed, under a cost cap. No premium for standardizing on one vendor.

Flexibility

Mix any vendor, architecture, and cloud in a single cluster. Adopt new silicon as a backend, not a re-architecture, and place each workload where it runs best.

Resilience

An outage or slowdown in one cloud, region, or GPU vendor does not take the cluster down. The cluster self-heals, healthy nodes pick up the pending work, and the job keeps running on whatever is still up.

03 · What it spans

Every major vendor, in one cluster.

Heterogeneous compute: native execution on every backend for high performance at lower cost

Supported compute backends and the hardware each targets
BackendHardware
CPUx86-64 · ARM
CUDANVIDIA GPU
ROCmAMD GPU
MLXApple silicon
VulkanCross-vendor GPU
SYCLIntel · oneAPI / cross-vendor
GCP TPUGoogle ASIC
AWS NeuronTrainium · Inferentia
Platforms & architectures
macOSLinuxWindowsx86ARM
Clouds & deployment
AWSGCPAzureOracle CloudKubernetesBare metal

Talk to sales

Start with the outcome

Tell us what you want to predict or automate and where the system has to run. We will scope the first production use case with you.