The GPU cloud optimized for R&D teams who refuse to wait

Built for researchers in academia and industry whose needs aren't met by hyperscalers, local clusters, or neo-clouds.

  • Instant GPU access
  • Flexible and ultra-low pricing
  • Intuitive UX

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DevRev Inc.

"We switched due to their competitive pricing and reliability" - Anshu Avinash, Tech Dir, AI and Search

Collinear.ai

"Love Cloudexe for their pricing and stellar support!" - Soumyadeep Bakshi, Co-founder

CMU Robotics Institute

"Deployments become effortless with intuitive interface." - Prof. Fernando De La Torre

BITS Pilani, India

"Ideal UX for managing multi-campus education/research infrastructure" - Prof. Snehanshu Saha

Arizona State University

"Runtime abstraction is clean...provided excellent support throughout..." - Dr. Rolando Garcia, Presidential Fellow

DevRev Inc.

"We switched due to their competitive pricing and reliability" - Anshu Avinash, Tech Dir, AI and Search

Collinear.ai

"Love Cloudexe for their pricing and stellar support!" - Soumyadeep Bakshi, Co-founder

CMU Robotics Institute

"Deployments become effortless with intuitive interface." - Prof. Fernando De La Torre

BITS Pilani, India

"Ideal UX for managing multi-campuseducation/research infrastructure" - Prof. Snehanshu Saha

Arizona State University

"Runtime abstraction is clean...provided excellent support throughout..." - Dr. Rolando Garcia, Presidential Fellow

Why DevCloud?

Research teams face unique challenges that traditional cloud platforms just can't address. The demands of small, fast-moving teams require a tailored solution.

Challenges with Current Solutions:

  • Levels of abstraction (e.g. training-as-a-service) aren't appropriate for research workflows.
  • Constant data movement like uploading, downloading, and syncing code or datasets disrupts momentum.
  • Difficulty of debugging on remote/opaque clusters is time-consuming and frustrating.
  • Dependency capture (and packaging) of workload for remote execution is burdensome.
  • Overburdened researchers without a dedicated infra team end up acting as de facto admins.
  • Complex hyperscaler UX is designed for IT pros, not for the needs of researchers.
  • Bare-bone UI offered by neoclouds doesn't scale beyond solo practitioners.
  • Opaque and consumption-based pricing leads to surprising cost overruns.
  • Oversubscribed clusters put paper deadlines or customer deliverables at risk.

What is DevCloud?

DevCloud is the first end-to-end cloud platform specifically designed for the research community, offering an optimized experience for both academia and industry.

Key Features

  • Seamless WorkflowStay in the flow with local-like development and debugging. Say goodbye to dependency capture or syncing code/data.
  • Easy Admin UIOnboard users, provision compute resources, assign quotas, monitor usage, and prioritize projects with ease.
  • Predictable PricingNever run into surprising cost overrun, even while using burst resources.
  • Unmatched AvailabilityNever wait to access a GPU. Get the resources you need, when you need them.
  • Security FirstRest easy about data security with the SOC-II compliant base platform.
  • Hybrid DeploymentRun workloads on your own servers, or on our servers, or on a mix of both.

Who is this for?

If you are managing a team of researchers or developers who use GPUs in many different ways (training models, running simulations, classic ML workloads, etc.), this product is for you.

DevCloud target audience

How does DevCloud work?

DevCloud's unique architecture is based on patented Hardware/Software Disaggregation, which unbundles the "server" concept into separate components:

SoftwareOS, libraries, frameworks, filesystem, env vars, network routes, etc.
+
HardwareCPU, GPU, storage, NIC, etc.

This disaggregation enables the dynamic pairing of software environments with hardware over ultra-low-latency networks, improving flexibility, resource utilization, and cost efficiency.

Structural Advantages of DevCloud

Architectural Change User Implication
No explicit server acquisition/release (GPUs only used during workload execution) Improved GPU fleet utilization and reduced customer costs.
No setup required for GPU servers (access to multiple neocloud partners) Better GPU availability and further cost reduction.
No need to capture dependencies or sync uploads/downloads Faster iterations and debugging, leading to improved user experience.

Pricing

Subscription

  • • Choose a time-based subscription (1 month to 1 year) for one or more GPUs, with better discounts for longer commitments.
  • • The subscription is shared across your entire organization, and pricing varies based on the GPU generation and model.
  • • No limit on use of subscribed GPUs.

Burst access

  • • Access additional GPUs on-demand without reserving upfront.
  • • Billed by GPU-hours, with clear pricing provided at the time of job execution. Admins can control and limit burst GPU usage.

Storage

  • • Multi-terabyte storage is included with your GPU subscription. Additional storage is available at competitive prices, and it's shared across all team members.

Payment options

  • • We offer both credit card payments (in USD) and invoice billing for larger institutions.
  • • We can work with your institution to be onboarded as an approved vendor and help with your grant proposals.

Free trials

  • • Extended free trials are available to ensure your workloads run smoothly before committing to a subscription.

Sample pricing

  • • $600/H100/Month.
  • • $866/H200/Month.

About us

We are a team of experienced engineers and entrepreneurs with deep expertise in building GPU cloud infrastructure at leading companies like Intel, NVIDIA, Google, and others. We shipped the first CUDA GPU at NVIDIA, the first Xe GPU at Intel, and built the core tech for Google's Stadia Cloud Gaming service. Based in Silicon Valley, we are passionate about solving the unique challenges faced by researchers in academia and industry.

Contact Us

For corporate inquiries, reach us at info@cloudexe.tech.

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