Accelerate your next discovery with research‑grade Kubernetes

Spin up secure, GPU‑ready clusters in minutes with Spectro Cloud Palette, so you can meet grant deadlines and publish faster.

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Kubernetes for higher education

Do more with less

Your AI models, simulations and data pipelines grow and shrink with every experiment, and building bespoke HPC clusters steals precious hours from science — unacceptable when you’re facing immovable grant funding and review deadlines.

Cloud-native technologies like Kubernetes make some attractive claims:

Horizontal scaling: Automatically grow or shrink compute pools as your job queue changes.
Powerful scheduling control: Pin GPU, CPU and memory resources to the right job, respecting priorities and deadlines.
Reproducibility: Declarative manifests guarantee identical environments on‑prem or in the cloud.
Cost efficiency: Tear clusters down when the project ends, staying within budget.

Hurdles to adoption

But as always, the reality is a little different. Kubernetes is challenging to deploy and manage, particularly when you’re dealing with specialized GPU hardware and complex research software stacks.When you choose a management platform, you need to know that your clusters are in safe hands.

  • 2,500–3,000 HPC professionals shortfall in North America by 2030. (Hyperion Research)

  • 1.9 hours median queue wait-time for jobs — access to compute is slowing research. (Intersect360 survey)

  • Kubernetes is now the second-most-deployed cluster resource-manager across the TOP500 sites, trailing only Slurm.

  • 79% of NSF facilities said it was “challenging” to rearchitect for cloud operations. 60% cited lack of technical expertise. (CI Compass Cloud Survey)

“University HPC systems typically remain constrained by funding limitations, traditional architectural models, and fragmented governance structures. This imbalance poses a significant challenge for academic research institutions seeking to maintain competitiveness in cutting-edge research areas.”
Survey of HPC in US Research
Institutions, June 2025

Research workloads made easier with Palette

Palette from Spectro Cloud is a management platform that makes cloud-native infrastructure easy for you and your research teams. It gives you:

Ready‑to‑run research stacks

Use Palette to define version‑controlled bundles including OS, Kubernetes distro and science libraries for consistent, reproducible  one‑click cluster creation.

Multi-environment support

Whether you’re building a cluster  airgapped, on-prem in the lab or bursting to cloud GPUs for extra power, Palette gives you centralized control.

Day‑2 automation



Palette handles upgrades, patching and backups across bare metal, VMs or cloud, with safe rollbacks, so you spend less time on infrastructure toil.

GPU‑aware scheduling

We simplify deployment of NVIDIA software stacks and AI tooling like Kubeflow and Flyte, enabling smart scheduling with maximum utilization.

Secure multi‑tenant isolation

RBAC, network policies and secrets management keep projects safely separate while sharing hardware. You can give teams isolated, quota-controlled workspaces for teaching and collaboration.

Grant‑friendly cost insights

Attribute resource consumption to projects and export reports for funding bodies.

Whether you’re running climate modeling, genomics pipelines or training LLMs, Palette puts the accelerated infrastructure you need at your fingertips.

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Demanding environments visual

Proven in the most demanding environments

We help research organizations achieve lofty goals every day. With one  US national defense entity, we’re managing 10,000 bare metal HPC nodes across two data centers, serving specialized workloads like trajectory analysis — all running on a core cloud-native stack with components like Ubuntu and Flyte. What could we do for you?

From the lab to the far edge graph

From the lab to the far edge

Palette isn’t just ideal for large-scale clusters in the lab or research cloud — it’s a trusted name in edge computing, too. If you need to deploy sensing, decisioning or other AI workloads on nodes across campus or across the world, our platform can help.

Edge projects typically run into challenges around provisioning, operations in disconnected environments, and securing sensitive data and models. Put simply, we have solved all these challenges and more, and today we enable teams across defense, oil and gas, industry, retail, pharmaceuticals and healthcare orchestrate workloads wherever they need to run.

Download our handy edge project checklist
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