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Maximize GPU utilization and revenue with smart overcommit

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Reviews onSourceForge
67 reviews tracked

The Bottom Line

Entry price

Paid plans only

Biggest pro

Significantly increases GPU utilization and profitability through overcommit.

Biggest con

Pricing is based on VRAM managed and consumed, which might require careful monitoring for cost optimization.

TL;DR - hosted·ai

  • Optimizes GPU utilization and profitability for service providers offering GPUaaS.
  • Features GPU overcommit to multiply revenue and margins by overselling resources.
  • Provides a turnkey solution with multi-tenant provisioning, a marketplace, and customer portal.
Pricing: Paid only
Best for: Enterprises & pros
4.3/5 across review platforms

What is hosted·ai?

Editorial review
hosted·ai is a turnkey software platform designed for service providers to offer GPU-as-a-Service (GPUaaS). It provides tools for GPU pooling, multi-tenant provisioning, and a built-in GPU marketplace, alongside a customer portal, to ensure maximum utilization and profitability from GPU cloud infrastructure. The platform addresses common challenges like GPU underutilization by implementing smart GPU scheduling and elastic resource provisioning. The platform's core innovation lies in its GPU overcommit feature, allowing providers to oversell GPU resources for significantly higher revenue and margin per card compared to traditional GPU passthrough methods. It supports various overcommit ratios (2x to 10x) and manages task allocation based on workload priority, utilizing system RAM if VRAM is insufficient. This approach helps reduce CAPEX requirements and enables providers to scale their GPU businesses efficiently, improving ROI for Neocloud infrastructure.

Available on: Web

Pros & Cons

Pros

  • Significantly increases GPU utilization and profitability through overcommit.
  • Lowers entry barriers for new GPUaaS offerings by reducing CAPEX.
  • Provides a comprehensive, turnkey platform for managing and selling GPU resources.
  • Combines the benefits of VMs and Kubernetes for flexible GPU orchestration.

Cons

  • Pricing is based on VRAM managed and consumed, which might require careful monitoring for cost optimization.
  • Requires existing GPU infrastructure or investment in GPUs to utilize the platform.

Ratings Across the Web

4.3(67 reviews)

Ratings aggregated from independent review platforms. Learn more

Preview

Key Features

Software-defined GPU with poolingMulti-tenant GPU resource allocationConfigurable GPU overcommit (2x to 10x)Elastic GPU resource provisioningRebrandable self-service user portalsIntegration with WHMCS and billing enginesHardware-agnostic (servers, GPUs)Unified GPU orchestration for Kubernetes and VMs

Pricing Plans

Pricing checked Jun 12, 2026

Base

$750 / m

  • VRAM managed: $0.0005 / GB-h
  • VRAM consumed: $0.0030 / GB-h
  • 24x7 support included
  • 15min SLA

Reviews

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4.3/5

Across 67 verified user reviews on SourceForge

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hosted·ai FAQ

How does hosted·ai achieve 5x profitability for GPUaaS providers?

hosted·ai achieves higher profitability through its GPU overcommit feature, which allows providers to sell virtual GPU resources at 2x to 10x the physical capacity. This maximizes revenue and margin per card by ensuring near 100% utilization and efficient resource allocation, even when VRAM is insufficient by leveraging system RAM.

Can hosted·ai be integrated with existing cloud infrastructure?

Yes, hosted·ai can be added to existing cloud infrastructure as a standalone GPUaaS solution. Alternatively, it can be deployed as a full hyperconverged stack. It is also hardware-agnostic, supporting various servers and GPUs.

What tools does hosted·ai offer to help providers with market analysis and business planning?

hosted·ai provides a suite of Neocloud Tools including a GPU Price Index to benchmark GPUaaS pricing, a Business Planner (P+L), a Funding + Cashflow Planner, a Marketing ROI calculator, and a GPU Picker to assist providers with strategic planning and market insights.

How does hosted·ai handle GPU resource allocation for different workloads?

The platform assigns physical GPUs to pools, and customer workloads access the combined resources of these pools. hosted·ai manages task allocation based on workload priority and can utilize system RAM if there is insufficient VRAM available, ensuring efficient and adaptive scheduling.

Source: hosted.ai