hosted·ai

Name: hosted·ai
Brand: hosted·ai

AI Native

Turn your GPU infrastructure into a profitable AI cloud

Est 2024|Valuation $66.7M|Raised $18.7M|1 Ref Customers

LinkedIn 4K|Web traffic 2K|FTEs 42|HQ San Jose, CA

Where hosted·ai is likely to be useful: - For providers wanting an integrated route to define GPU products/packages, provide customers a portal, meter and bill usage, and increase utilization via pooling and policy-based scheduling. Where alternatives are preferable: - Run:ai and internal schedulers: appropriate for organizations that are not selling GPUaaS but need to manage GPU clusters efficiently. - Retail GPU clouds (retail GPU cloud providers): appropriate if the objective is to run workloads rather than create a GPU business. What is hard to validate today: - Whether hosted·ai consistently achieves the claimed utilization or margin improvements across mixed workloads - How isolation and security behave under multi-tenant contention - Real-world operational burden versus vendor positioning

01Competitive Landscape

Narrative analysis

Where hosted·ai is likely to be useful:

For providers wanting an integrated route to define GPU products/packages, provide customers a portal, meter and bill usage, and increase utilization via pooling and policy-based scheduling.

Where alternatives are preferable:

Run:ai and internal schedulers: appropriate for organizations that are not selling GPUaaS but need to manage GPU clusters efficiently.
Retail GPU clouds (retail GPU cloud providers): appropriate if the objective is to run workloads rather than create a GPU business.

What is hard to validate today:

Whether hosted·ai consistently achieves the claimed utilization or margin improvements across mixed workloads
How isolation and security behave under multi-tenant contention
Real-world operational burden versus vendor positioning

	hosted·ai	Run:ai (GPU scheduling layer)	“Buy GPU capacity” (e.g., Lambda / CoreWeave / RunPod)
Starting Price	Not public (estimate/demo; priced by VRAM managed/consumed)	Enterprise pricing typically quote-based	Published hourly rates that vary by provider/region/GPU
Best For	Service providers launching GPUaaS with portals, metering, and billing integration	Organizations optimizing GPU utilization for internal ML teams	Teams that need compute capacity without building a provider platform
Key Strength	Monetization layer, multi-tenant GPU pooling/overcommit, provider UX	Mature scheduling and cluster utilization tooling	Fast time-to-value; no need to operate a GPUaaS stack
Key Weakness	Limited public third-party validation; security certifications unclear; success depends on tuning	Not a full "sell GPUaaS" business stack	Less control over unit economics; margins accrue to the cloud provider

hosted·ai

01Competitive Landscape

Narrative analysis

02Market Map

01Competitive Landscape

Narrative analysis

02Market Map