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Author: Steve Fenton

  • neocloud masterclass

    hosted·ai’s CEO, Ditlev, and CTO, Julian, provide guidance on launching your own high-margin neocloud using the hosted·ai platform and GPU Mesh.

    Based on our own #BuildingInPublic experience with packet.ai, we share real numbers, strategies, gotchas and experiences, plus a hands-on demo and answers to plenty of FAQs.

    Speakers:

    Ditlev Bredahl, CEO of hosted·ai, presenting insights on launching GPU-as-a-Service (GPUaaS) solutions for cloud hosting service providers.

    Ditlev Bredahl

    CEO

    hosted·ai

    Julian Chesterfield, CTO of hosted·ai, smiling in a blazer and sweater, featured in a webinar replay about GPU-as-a-Service strategies.

    Julian Chesterfield

    CTO

    hosted·ai

  • GPU infrastructure feature launch announcement highlighting major updates, including Baremetal GPU, unified provisioning, and bug fixes for enhanced GPUaaS solutions.

    hosted.ai 2.2.1 update – Infrastructure flexibility with bare metal, browser SSH, and stronger GPU service automation

    hosted.ai has rolled out a major platform update v2.2.1 that makes it easier for teams to deploy, manage, and scale AI infrastructure across GPU services, virtual machines, and bare metal.

    Let’s get into it!

    #BuildingInPublic – the platform story so far

    What’s new in hosted·ai v2.2.1?

    1. Unified provisioning across GPU services and VMs

    hosted.ai now delivers a more consistent deployment and runtime experience across GPU services and virtual machines.

    This means teams can use a more standardized way to provision workloads, reuse service templates more effectively, and manage operational capabilities more consistently across compute environments.

    Why is this important?

    • reduces operational complexity
    • improves reliability and consistency
    • makes it easier to scale GPU services across regions and deployment types

    Seems interesting? Learn more about our GPU cloud platform or get in touch for a demo.

    2. Bare metal GPU instances

    hosted.ai now supports bare metal GPU instances as a first-class compute option.

    Customers can provision dedicated hardware with lifecycle management, hardware discovery, integrated pricing and billing, and direct access from the hosted.ai platform.

    Why it matters:

    • creates a smoother path for customers moving into higher-performance or specialized environments
    • supports workloads that require full hardware control
    • expands deployment flexibility beyond containers and VMs

    This is something you are looking for? Learn more about our GPU cloud platform or get in touch for a demo.

    3. Browser-based SSH access

    Users can now open an SSH session directly from the browser for GPU services, VMs, and bare metal instances.

    This removes the need to install external SSH clients or work around restricted environments just to access infrastructure.

    Why is this important?

    • reduces friction for first-time users
    • speeds up time-to-access
    • creates a more consistent access experience across compute types

    Learn more about our GPU cloud platform or get in touch for a demo.

    4. Stronger automation for GPU service deployment

    hosted.ai has expanded automation support, so teams can configure GPU service environments more consistently during deployment.

    This includes stronger support for software stack customization and operational setup as part of workload provisioning.

    Why is this important?

    • reduces friction for first-time users
    • speeds up time-to-access
    • creates a more consistent access experience across compute types

    Want to learn more? Visit our GPU cloud platform or get in touch for a demo.

    5. Expanded support for containerized and Kubernetes-style workloads

    hosted.ai now supports more advanced runtime patterns for GPU services, including capabilities that make it easier to run container-based and Kubernetes-oriented workloads in a more VM-like way.

    Why is this important?

    • expands the range of workloads customers can run without needing a full VM for every case
    • helps providers package more usable GPU services for end users
    • creates more flexibility in how AI infrastructure is delivered

    Want to learn more? Visit our GPU cloud platform or get in touch for a demo.

    6. Real-time Kubernetes service health monitoring

    hosted.ai now includes a Kubernetes service integrity dashboard in the admin experience, with live service visibility, warnings, and audit tracking.

    Why is this important?

    • improves operational visibility
    • speeds up issue detection and troubleshooting
    • reduces reliance on manual diagnostics

    Want to learn more? Visit our GPU cloud platform or get in touch for a demo.

    7. Better platform performance and scalability

    Under the hood, hosted.ai has also reworked key orchestration components and improved concurrency in internal execution flows.

    These changes help the platform process compatible operations more efficiently and improve long-term scalability.

    Why is this important?

    • improves performance under scale
    • reduces blocking from long-running operations
    • creates a stronger foundation for future platform growth

    Want to learn more? Visit our GPU cloud platform or get in touch for a demo.

    Additional improvements customers will notice

    Alongside the major platform capabilities above, this update also improves day-to-day usability and billing accuracy across the platform.

    This includes improvements such as:

    • improved branding support in system email delivery
    • cleaner service creation flows
    • more reliable edit and clone behavior
    • more accurate workspace billing visibility
    • preservation of billing history after workspace deletion
    • fairer billing behavior for pending subscriptions
    • stronger scheduling reliability
    • better support for complex node and network configurations

    Want to learn more? Visit our GPU cloud platform or get in touch for a demo.

    Who benefits most from this update

    This update is especially relevant for:

    • AI teams running GPU-backed applications and services
    • infrastructure providers offering GPU services to customers
    • operators managing mixed environments across GPU services, VMs, and bare metal
    • teams deploying Kubernetes, distributed training, or advanced containerized workloads

    A stronger foundation for what comes next

    This release is not just about adding features. It is about making hosted.ai more consistent, more flexible, and more scalable across the full infrastructure lifecycle.

    With unified provisioning, stronger automation, browser-based access, bare metal support, and deeper operational visibility, hosted.ai is continuing to make it easier for teams to deliver AI infrastructure that works in the real world.

    If you want to see these capabilities in action, contact the hosted.ai team for a walkthrough.

    It’s in final testing now –subscribe to get updates!

  • hosted·ai logo and Creandum logo, representing collaboration in GPU-as-a-Service solutions for the cloud infrastructure market

    hosted·ai closes $19M seed round to transform GPU infrastructure economics

    San Jose, 19th March 2026– hosted·ai has closed a $19M seed funding round to accelerate its mission: making AI infrastructure simple, efficient and affordable for service providers and their developer and enterprise customers. The round was led by Creandum, with Repeat VC following, and participation from existing investors People Ventures, Z21 Ventures, Golden Sparrow, Hersir Ventures and Tekton.

    The problem: GPU infrastructure is broken

    AI depends on GPU infrastructure – but today’s GPU is profoundly wasteful. Unlike traditional cloud compute, which scales dynamically to match demand, GPUs are static: customers must rent fixed instances based on estimated peak workload requirements. This creates three compounding problems:

    • CAPEX and profitability: service providers operating GPU infrastructure (“neoclouds”) must make enormous upfront investments to meet customer demand, making profitability a persistent challenge.
    • Utilization and waste: AI workloads consume only around 40% of GPU capacity on average, meaning roughly 60% of the capacity that neoclouds invest in – and customers pay for – sits idle.
    • Scarcity and access: as AI shifts from model training to inference, companies need local, low-latency, sovereign GPU infrastructure. High CAPEX requirements and GPU scarcity outside hyperscalers make it extremely difficult for regional service providers to meet that demand.


    The solution: the hosted·ai software stack

    The hosted·ai software stack tackles these problems directly, transforming the way GPUs are orchestrated, managed, sold and used:

    • hosted·ai: our core GPUaaS software platform. Through GPU pooling, optimized multi-tenant workload placement and GPU overcommit, hosted·ai delivers up to a 5x improvement in GPU utilization – reducing CAPEX requirements 5x, boosting profitability, and removing entry barriers that have kept regional service providers on the sidelines. Next on the roadmap: GPU Mesh, a unique resource exchange that enables service providers to buy and sell spare GPU capacity, extending their reach – or launching a full neocloud service – without additional hardware CAPEX.
    • packet·ai: a neocloud powered by our customers’ optimized GPU infrastructure, delivering GPUaaS at market-leading prices. packet.ai generates direct demand for hosted·ai customer GPUs, and forms the foundation of an open-source neocloud portal that customers can deploy at launch to accelerate their time to market.
    • GPUaaS.com: a wholesale GPU matchmaking service connecting enterprise buyers with hosted·ai customers and partners that can fulfil custom GPU cluster requirements at scale.

    Ditlev Bredahl, CEO of Hosted.ai said: “The GPU market has a waste problem, not a scarcity problem. We’ve spent 25 years building infrastructure software that makes service providers competitive – and the GPU opportunity is the biggest we’ve seen. This funding lets us move faster: more platform, more partners, more regions. We’re building the operating system for the GPU economy, and this round puts us in a strong position to do exactly that.”

    hosted·ai was founded by a team with deep experience of infrastructure technologies and service provider ecosystems. Ditlev Bredahl, Narendar Shankar, Julian Chesterfield and James Withall were previously instrumental in scaling UK2 Group, and founding OnApp, to bring cloud infrastructure as a service to the mainstream service provider market. They have also led numerous infrastructure technology, market development, AI and ecosystem initiatives at companies including VMware, Expedia, XenSource and NVIDIA.

    About hosted·ai

    hosted·ai is building the operating system for the GPU-powered AI economy. Through optimized GPU utilization and multi-tenant/federated GPU resource sharing, hosted·ai transforms GPU economics to deliver profitability for AI infrastructure operators and significantly more cost-effective, consumption-based GPU cloud for AI developers and enterprise users. hosted·ai was founded in 2024, launched publicly in 2025, and has teams across the US, EMEA and Asia-Pacific. For more information, visit https://hosted.ai. 


  • Dashboard interface of hosted·ai showing GPU management and total GPU count, emphasizing GPUaaS infrastructure and resources.

    GPU cloud + GPUaaS demos, clips, interviews

  • GPU Cloud Platform Guide featuring key fundamentals for GPUaaS vendors, including GPU multi-tenancy, infrastructure flexibility, and service provider strategies.

    GPU Cloud Platform Guide

    GPU CLOUD PLATFORM GUIDE

    Five fundamentals to discuss with your
    GPU cloud platform provider

    Traditional service providers need to evolve their offering from IaaS to GPUaaS. Neoclouds need to build the platform right, first time, to ensure a successful future. The GPU orchestration platform / control plane you choose is critical.

    This guide helps you assess GPU cloud platform features against the success criteria of multi-tenant service providers (neoclouds, CSPs, hosts, telcos).

    GPU Cloud Platform Guide cover featuring title "Five fundamentals to discuss with your GPU cloud platform vendor," with a grid background and graphical elements illustrating GPU multi-tenancy and service provider concepts.

    How to use this guide

    Each section of the guide provides questions you can ask platform vendors about five fundamental feature categories that determine those success criteria:

    Success criteria:

    Control of service design, deployment model, branding, access, location

    Flexible pricing – ability to price GPUaaS tiers for specific customer segments

    Inference ready – enabling GPU to flex and scale like cloud for variable workloads

    Ecosystem integration – ability to slot into existing provider workloads and tools

    Profitability – optimal utilization, minimal operational overheads, maximum margins

    Simple UX for admins and customers – easy management and consumption

    GPU cloud platform fundamentals:

    GPU cloud deployment model

    GPU multi-tenancy

    GPU utilization

    GPU metering + billing

    End-user experience

    Download now

  • Multi-tenant GPUaaS workload scheduling diagram showing Tenant A and Tenant B resource allocation in a GPU pool.

    GPU cloud – how we schedule multi-tenant GPUaaS workloads

    hosted·ai was created for service provider GPUaaS. It is multi-tenant by design. Instead of just selling GPU on a card-by-card basis (one card per user) or a bare metal basis (one GPU server per user), you can assign physical GPUs to virtual GPU pools, and sell the resources of the pool to multiple tenants at once.

    Admin dashboard of hosted·ai displaying GPUaaS accelerator pools in New York, featuring NVIDIA Tesla T4 GPU types, pool names, time quantum, sharing ratios, and current statuses.

    In the 2.0.1 release of hosted·ai we updated the hosted·ai task scheduler, which is the part of the platform that handles the crucial task of assigning GPU resources to user workloads. Let’s take a look at the task scheduler and how it works – but first, some basics…

    The basics: how hosted·ai handles multi-tenant GPU virtualization

    The GPU pooling concept is the foundation of our GPU cloud multi-tenancy. Through the hosted·ai admin panel, you create your regions, add your servers, and automatically detect the GPUs attached to those servers. Then you assign those GPUs to virtual GPU pools.

    You can create private pools for the exclusive use of a single tenant – the GPUaaS equivalent of virtual private cloud. You can create GPU pools that are shared by multiple tenants – the GPUaaS equivalent of public cloud.

    (with hosted·ai you can also sell GPU cards using traditional passthrough virtualization, if you deploy the platform as a full HCI stack running on KVM, but in this article we will just focus on the true multi-tenant GPUaaS scenarios.)

    Each tenant can have its own team with multiple users, and each of those users can have their own quotas and access rights. Each user sees their own GPU device, can run the full CUDA tool stack, can run NVIDIA SMI and access the full capacity of each card. It’s fully software-defined GPU – you can read more about that in this software-defined GPU whitepaper.

    Multi-tenant GPU task scheduling options

    Ok, so how are all of these tenants and user tasks given access to the GPU resources they need, the GPU cycles, the VRAM and TFLOPs?

    That’s the job of the task scheduler. It is an extremely efficient system that provides workloads with access to GPU resources.

    The type of access is controlled by a range of settings the provider can configure for each GPU pool.

    Using a few intuitive sliders, you can configure shared resources for optimal performance, security, or a blend of both.

    By configuring different pool settings you can create GPUaaS for a wide range of customer use cases.

    1. GPU pool sharing ratio

    This is where you can choose how much overcommit/oversubscription you allow for the GPU pool. Another way of thinking about it, is how many virtual GPUs can be created from each physical card in the pool.

    Slider interface displaying a sharing ratio of 6 for GPU resource allocation, illustrating GPUaaS configuration settings for optimal performance and oversubscription.

    With a sharing ratio of one, it’s a one-to-one mapping: you can share the resources of the pool once – i.e. provision the resources of the pool one time, to one team. You can also set this from 2 to 10, allowing oversubscription of the GPUs in the pool between 2 and 10 times – so with 4 GPUs in a pool, and a sharing ratio of 4, you’re actually presenting 16 virtual GPUs to your users.

    As with any infrastructure as a service scenario, oversubscription (overselling) makes use of idle capacity to serve additional customers. It’s the basis of efficient and profitable service delivery.

    Why would you need a sharing ratio of one?

    At first glance you might think this is a redundant option. However, it’s very different from just providing one card per user (the passthrough model) because you’re actually sharing the entire pool of GPUs with a team of users.

    There is more flexibility, because the team can have varying levels of access to multi-GPU resources. There is more scalability, because the pool can be expanded by adding more GPUs; and there is more redundancy, because if a GPU fails, the rest of the pool is still available.

    2. GPU pool optimization

    This slider controls how the scheduler allocates the resources of the GPU pool to workloads, to prefer performance, security, or a blend of both.

    Slider graphic for GPU pool optimization, illustrating performance, balanced, and security settings, with labels for temporal scheduling and memory zeroing enabled, emphasizing highest security.

    Security: using temporal scheduling, hosted·ai swaps user tasks in and out of the GPU. It’s a little like time-slicing, except for a crucial difference: each task has full isolated access to the GPU during its allocated time. The scheduler context-switches tasks extremely rapidly; at no time do user tasks co-exist in the GPU.  

    Performance: using spatial scheduling, hosted·ai fits user tasks into the GPU resources available to maximize utilization. User tasks co-exist on the GPU. This is a little like MIG, but the resource allocation is dynamic and scales to the requirement of the workloads. Because there is no task-switching this improves performance, which can benefit latency-sensitive inference workloads.

    Balanced: this uses temporal scheduling but relaxes the context-switching. It’s more secure than performance mode, and more performant than security mode.

    3. GPU pool time quantum

    For performance and balanced pool settings (i.e. temporal scheduling) this setting controls the time a task has access to the full resources of a GPU.

    Time quantum slider set to 90 seconds, illustrating GPU resource allocation for performance and balanced scheduling in GPUaaS.

    The scheduler takes this into account for user workloads accessing the pool, and adjusts their access accordingly: lower settings equate to lower latency.

    Future scheduler enhancements

    As more enterprises adopt AI, especially inferencing, and as more Neoclouds, CSPs and Telcos build multi-tenant GPUaaS to serve this market, we’re adapting our scheduler to cater for this fast-evolving landscape.

    Coming next is a new credit-based scheduling system that provides a kind of automatic balancing of mixed inference/training workloads, and we’ll be sharing more details soon.

    Multi-tenant GPU FAQs

    True multi-tenant GPU is a new approach and you’ll certainly have questions. If you’d like to know more about how this works, why not get in touch for a discussion and a demo?

    In the meantime, here are some FAQs…

    What happens when we run out of VRAM?

    What happens to context-switched tasks?

    What about the performance overhead?

  • hosted·ai logo with performance, balanced, and security slider for GPUaaS version 2.0.1 optimization

    hosted·ai v2.0.1 – now it’s easy to optimize GPUaaS

    We’re excited to announce the availability of hosted·ai v2.0.1, with new features to make GPUaaS even easier to manage and sell. Let’s get into it!

    Screenshot of the hosted·ai control panel showing GPU infrastructure orchestration

    #BuildingInPublic – the platform story so far

    What’s new in v2.0.1?

    1. Tune your GPU pools for different workloads

    hosted·ai handles GPU very differently to other GPUaaS platforms. Our GPU control plane enables 100% GPU utilization by combining individual GPUs into pools, and allowing the resources of the entire pool to be shared with multiple tenants at once.

    The hosted·ai platform has an extremely efficient task scheduler that context-switches tasks in and out of physical GPUs in the pool – but, how is this scheduling controlled?

    Introducing… the new GPU optimization slider.

    GPU optimization slider demonstrating performance, balanced, and security settings for hosted·ai GPUaaS platform.

    When you create a GPU pool, you assign GPUs to the pool and choose the sharing ratio – i.e. how many tenants the resources of the pool can be allocated/sold to. For any setting above 1, the new optimization slider becomes available.

    Behind this simple slider is a world of GPU cloud flexibility. The slider enables providers to configure each GPU pool to suit different customer use cases. Here’s a quick demo from Julian Chesterfield, CTO at hosted·ai:

    • GPUaaS optimized for security
      Temporal scheduling is used. The hosted·ai scheduler switches user tasks completely in and out of physical GPUs in the pool, zeroing the memory each time. At no point do any user tasks co-exist on the GPU. This is the most secure option, but comes with more performance overhead.
    • GPUaaS optimized for performance
      Spatial scheduling is used. The hosted·ai scheduler assigns user tasks simultaneously to make optimal use of the GPU resources available. There is no memory zeroing. This is the highest-performance option, but it doesn’t isolate user tasks – they are allocated to GPUs in parallel.
    • Balanced GPUaaS
      Temporal scheduling is used, but without fully enforced memory zeroing. This provides a blend of performance and security.

    Why is this important?

    Service providers need the flexibility to handle any mix of heterogeneous workloads. That’s just as true for GPUaaS as it is for traditional IaaS cloud and hosting.

    Your GPUaaS needs to cater to a range of price/performance targets for different AI model training, tuning and inference use cases, as well as customers in simulation, gaming, research and so on.

    The hosted·platform enables you to configure your infrastructure and services accordingly:

    • Creating pools of different GPU classes
    • Configuring the sharing, security and performance characteristics of each pool
    • Configuring pricing and other policies for each pool
    • Enabling customers to subscribe to any mix of pools for different workloads

    2. Self-service GPU / end user enhancements

    Also in this release, some handy improvements for end users running their applications in your hosted·ai environments:

    GPU application service exposure

    We’re made it easier to expose ports for end user applications and services through the hosted·ai admin panel (and coming soon, through the user panel).

    Now your customers can choose how they present their application services to the outside world, through configurable ports

    Self-service GPU pool management

    We’ve added new management tools for your customers too. Each GPU resource pool they subscribe to can be managed through their user panel, with visibility of the status of each pod; the ability to start, stop and restart pods; and logs with information about the applications using GPU.

    GPUaaS management interface displaying Tesla A100 pool details, including vRAM, TFLOPS, instance status, and options to start, stop, or restart GPU instances.

    Why are these things important?

    These enhancements are part of our mission to make GPUaaS as fast and frictionless as possible for your end users (e.g. nobody should be forced to use CLI and dive into K8s just to restart a service)

    3. Furiosa device integration

    In July 2025 we announced our partnership with Furiosa, a semiconductor company specializing in next-generation AI accelerator architectures and hardware. We’ve been working to bring Furiosa device support to hosted·ai and this is now available in v2.0.1.

    Now service providers can create regions with clusters based on Furiosa, as well as NVIDIA. Once a region has been set up for Furiosa, it can be managed, priced and sold using the same tools hosted·ai makes available for NVIDIA – and in future, other accelerator devices.

    Dashboard interface of hosted·ai showing NPU devices under Accelerator Node section, featuring device names and statuses for generative AI workloads.

    Why is this important?

    Furiosa’s Tensor Contraction Processor architecture is designed to maximize performance per dollar and per watt for generative AI workloads.  The hosted·ai platform is designed to be accelerator-agnostic to give service providers flexibility in the AI infrastructure they build and sell. It’s all about flexibility to meet different use cases at different price/performance points.

    More information:

    Coming next:

    In final testing now – subscribe for updates:

    • Full stack KVM – complete implementation, replacing Nexvisor
    • Scheduler credit system – expanding GPU optimization with a credit system to deliver consistent performance for inference in mixed-load environments
    • Billing enhancements – more additions to the hosted·ai billing and metering engine – more ways to monetize your service
    • Infiniband support

  • Neocloud Survival Guide cover featuring the title "How to 5x your GPU revenue + margin," emphasizing profitability challenges in the GPU market, with grid background and logo elements.

    Neocloud Survival Guide

    NEOCLOUD SURVIVAL GUIDE

    How to 5x your GPU revenue + margin

    Most Neoclouds have a problem: it’s hard to see a future where the business is actually profitable.

    Some companies in this space have already folded. Many more are struggling to make the numbers add up, because super-high GPU capex + low utilization + price erosion + commoditization = little or no ROI.

    Cash flow chart over five years showing total revenue of $3.31M, total net income of -$2.36M, average margin of -71.3%, and total cash flow of -$0.36M, illustrating financial challenges for GPUaaS businesses.

    Let’s fix that.

    Everyone agrees that AI is the future, but how do you build an infrastructure business for AI that will still be in business next year, let alone in five years’ time?

    Get your copy of the Neocloud Survival Guide, and learn how to 5x your GPU revenue and margin.

    Neocloud Survival Guide cover featuring "How to 5x your GPU revenue + margin," with contents list including profitability issues and ROI strategies, emphasizing GPU as a Service solutions.

    Contents

    The Neocloud profitability problem

    Rethink the madness: change 4 things

    Your new game plan

    5x ROI illustrated

    Next steps

  • Co-founders Naren Shankar, James Withall, Julian Chesterfield, and Ditlev Bredahl of hosted·ai discussing AI infrastructure solutions, with text highlighting "AI infrastructure made simple & profitable."

    One year of hosted·ai: AMA with James, Julian, Naren and Ditlev

    We celebrated our first anniversary in August 2025. To mark the occasion, we hosted our four co-founders for a coffee and chat. We touched upon their journey so far, the service provider industry, and their aspirations with hosted·ai.

  • hosted·ai and Maerifa Solutions logos showcasing strategic partnership for Neocloud infrastructure solutions.

    hosted·ai and Maerifa form strategic partnership to provide a one-stop shop for Neocloud creation at scale

    Santa Clara, CA – 30th September 2025 – hosted·ai has signed a strategic partnership with Maerifa Solutions, a leading digital infrastructure company focused on the provision of technology design, deployment and supply chain management services. The partnership aims to facilitate the rapid creation and scaling of Neoclouds – cloud services built around GPU infrastructure for AI – by providing a one-stop shop for infrastructure advice, hardware, procurement and finance, and efficient, profitable GPU orchestration using hosted·ai software.

    Maerifa simplifies Neocloud creation through its relationships with AI cloud infrastructure OEMs such as NVIDIA, Supermicro and Lenovo, and supply chain and finance partners who can support hardware procurement and purchasing. With hosted·ai, Maerifa can now also provide turnkey software for Neocloud orchestration and monetization, with easy-to-use tools for GPU cloud service design, pricing, metering, billing and self-service.

    “The demand for GPU infrastructure is growing by leaps and bounds, however, there remains little focus developing multi-faceted Neoclouds with the ability to deliver the full catalogue of this infrastructure to end customers in a way that is economically viable long-term. Together with hosted.ai we have a solution that enables rapid scalability and will provide these companies with a way of focusing on what they are best at, attracting customers and providing innovative software solutions. We are already working on a number of projects together and invite others looking to grow their platforms to see how we can help,” said Rahul Kumar, Senior Executive Officer, Maerifa Solutions.

    “There is huge demand for AI training and inference infrastructure, but Neoclouds face quite a few challenges to deliver the scale that the market needs,” said Narendar Shankar, Chief Commercial Officer at hosted·ai. “Our partnership with Maerifa is exciting news for companies in this space, because they now have one expert partner for sourcing and delivering GPU infrastructure, and getting help with financing; and combined with hosted·ai, the software to manage, provision and bill for AI cloud services while making those services efficient and profitable.”

    hosted.ai was founded to make GPU cloud efficient, easy and profitable for service providers, by creating a turnkey GPUaaS platform designed specifically for companies in this market. hosted.ai was launched in 2024 by a team with deep experience of owning, running, and building solutions for AI and for service providers, at businesses including VMware, Nvidia, Expedia, XenSource, OnApp, Sunlight and UK2.

    Maerifa Solutions was conceived, incubated and launched by Aethlius Holdings to create an ecosystem of Tier-1 partners across Digital Infrastructure and related financing solutions delivered by its partners to address the funding gap of acquiring hard-to-access GPU server technology. Since its launch in Q3 2024 it has already partnered with leading players in the industry and is in discussions to deliver multi-million dollars’ worth of hardware and associated solutions to projects in Europe, Middle East, Africa and Southeast Asia.

    About hosted·ai
    hosted·ai provides software to make AI infrastructure hosting simple and profitable for service providers. The hosted·ai platform is a turnkey AI cloud / GPUaaS stack that gives service providers the tools they need to create, manage and monetize GPU cloud infrastructure. hosted·ai was founded in 2024, launched publicly in 2025 and has teams across the US, EMEA and Asia-Pacific. For more information, visit https://hosted.ai. 


    About Maerifa Solutions
    Maerifa Solutions is an ADGM-registered digital infrastructure company, in collaboration with its extensive ecosystem, brings expertise in technology design and deployment, supply chain management, data centers, and power solutions. This, combined with Maerifa Solutions’ deep financial acumen, enables it to deliver creative investment solutions that help clients realise the full potential of AI infrastructure. By offering innovative funding mechanisms and access to hardware and hosting capacity, Maerifa Solutions ensures the long-term scalability and capital efficiency of AI projects.