Compare Compute with Hivenet vs. Google Compute Engine

Hivenet is an AI cloud platform based in Europe that boasts its own GPU infrastructure, strategically located in France, the UAE, and the US. It provides a comprehensive solution for AI teams, offering on-demand RTX 5090 GPUs starting at €0.75 per hour, as well as RTX Pro 6000 options for enterprise-level tasks. The platform includes vCPU instances, S3-compatible object storage, and an Inference API compatible with OpenAI. Pricing is transparent, charged per second from prepaid credits, with no additional costs for data egress and no obligation for long-term contracts. Users can easily set up services through the console in just a few minutes or fully automate processes using the public Compute API. Hivenet supports team organization with role-based access, making it suitable for businesses rather than just individual users. It serves as a practical alternative to major cloud providers, offering predictable pricing and catering to European companies that prefer to host their AI operations within the EU.

Utilize advanced open models such as Llama, Mistral, Qwen, and Gemma on powerful RTX 5090 GPUs, starting at just €0.75 per hour, charged per second. Each GPU is equipped with 32 GB of VRAM, making it possible to perform LoRA and QLoRA fine-tuning on various models ranging from 7B to 13B on a single GPU. For larger projects, you can scale up to multi-GPU setups with up to 8 GPUs. This per-second billing model allows you to conduct brief experiments or hyperparameter tuning without incurring unnecessary costs. Kick off your projects with PyTorch or Jupyter templates enhanced by Hugging Face tools, and easily save your environment as an image for quick re-launching in future sessions. Store your datasets and checkpoints in S3-compatible object storage without any egress fees. Once your model is trained, you can deploy it directly on the same platform using vLLM or an OpenAI-compatible Inference API endpoint.

There are two methods to execute inference using Hivenet. The first option is self-managed, where you can deploy RTX 5090 instances starting at €0.75 per hour with the vLLM template, allowing you to serve your own models and billing is calculated by the second. The second option is managed through the Inference API, which provides a dedicated endpoint compatible with OpenAI. This allows you to easily transition your existing code by simply updating the base URL, eliminating the need to handle the serving layer yourself. The RTX 5090 features 32 GB of GDDR7 and a memory bandwidth of 1.79 TB/s, making it an excellent choice for serving models ranging from 7 billion to 14 billion parameters, as well as larger quantized models. For even bigger models and high-utilization needs, the RTX Pro 6000 is available. There are no egress fees, meaning you won't incur charges for the responses sent to users. Hivenet offers regions in France, the UAE, and the US to ensure low latency and comply with EU data residency regulations.

Hivenet Compute offers a comprehensive GPU and CPU infrastructure managed entirely by Hivenet, featuring locations in France, the UAE, and the United States. Users can access on-demand RTX 5090 GPUs (32 GB GDDR7) starting at €0.75 per hour, as well as the RTX Pro 6000 for more intensive production tasks, along with virtual CPU instances ranging from 2 to 32 vCPUs. Billing is done per second using prepaid credits, with no requirements for long-term contracts or egress charges. Alongside the computing instances, Hivenet provides essential tools for AI teams, including S3-compatible object storage, an OpenAI-compatible Inference API for dedicated model endpoints, a public Compute API with an OpenAPI specification, and organizational features for teams with role-based access controls. For workloads that must remain in Europe, there is an option to run in the French region. Hivenet Compute is particularly well-suited for startups, research institutions, and product development teams seeking reliable production infrastructure without the prohibitive costs, quotas, or lock-in typically associated with larger hyperscale providers.

Utilize RTX 5090 GPUs (32 GB GDDR7, featuring 5th-generation Tensor Cores) starting at just €0.75 per hour. Scale up to 8 GPUs for each instance, enjoying per-second billing that ensures you're only charged for active time. Ideal for tasks in computer vision, smaller to medium language models, diffusion models, and experimental prototypes—perfect for when a cluster of H100s is excessive. For those requiring larger memory, the RTX Pro 6000 is also an option. Quick-start templates for PyTorch, Jupyter, and Docker enable you to begin training within minutes, and you can save your environment for easy relaunching. Store your datasets and checkpoints using S3-compatible object storage without incurring egress fees for transfers. Automate your workflows with the public Compute API. Our research collaborators and clients include INRIA, Proteineer, and the University of Arizona, with services available in regions such as France, the UAE, and the United States.

Rent RTX 5090 GPUs starting at just €0.75 per hour, with billing that occurs by the second, allowing you to stop payments immediately when you terminate the instance. Each RTX 5090 comes equipped with 32 GB of GDDR7 memory and offers a bandwidth of 1.79 TB/s, making it ideal for various tasks such as inference, fine-tuning, and image generation without incurring costs for unnecessary data-center cards. You can easily scale your setup from 1 to 8 GPUs per instance, or opt for the more powerful RTX Pro 6000 for handling larger models and consistent production demands. Utilize pre-configured templates for PyTorch, Jupyter, vLLM, ComfyUI, and Docker, save your customized images, and establish connections via SSH. Enjoy the advantages of no egress fees and no lengthy contracts. Available regions include France, the UAE, and the United States, allowing you to maintain data within the EU when necessary.

Google Compute Engine provides a powerful AI infrastructure designed specifically for intensive machine learning and artificial intelligence tasks. It allows users to utilize a mix of virtual machines, GPUs, and TPUs, optimizing the scaling of their AI models for quicker training and inference times. The platform is compatible with a wide range of frameworks and tools, enabling developers to enhance their AI operations on a global level. Additionally, new clients are given $300 in complimentary credits, allowing them to test and experience the capabilities of Google Compute Engine's AI infrastructure, facilitating the advancement of their AI projects without any initial expenses.

The auto scaling capability of Google Compute Engine dynamically modifies the number of virtual machine instances based on varying traffic or workload requirements. This functionality guarantees that applications operate efficiently without the need for manual adjustments and minimizes costs by reducing resources when demand decreases. Users have the flexibility to set scaling guidelines according to particular metrics, like CPU usage or request frequency, allowing for tailored resource distribution. New users are also offered $300 in free credits, giving them the opportunity to experiment with and optimize auto scaling for their specific needs.

Google Compute Engine provides users with the ability to utilize high-performance cloud GPUs that can be integrated with virtual machines, catering to demanding workloads. These Cloud GPUs are particularly well-suited for applications such as machine learning, video rendering, 3D modeling, and scientific simulations, delivering the necessary computational power for intensive tasks. Google offers a selection of GPU models, including NVIDIA Tesla K80s, P4s, T4s, and V100s, allowing users to select the best option for their individual performance requirements. New users are welcomed with $300 in free credits, which they can use to experiment with Cloud GPU capabilities across various GPU-accelerated applications, facilitating enhanced performance and quicker results.

Google Compute Engine delivers an extensive suite of cloud management solutions that empower users with oversight and administration of their cloud infrastructure. These solutions enable administrators to keep an eye on the status of virtual machines, set up resources, streamline deployment workflows, and monitor financial and usage data. Leveraging the integrated tools provided by Google Cloud, businesses can ensure efficient operations while managing expenses effectively. Additionally, new users can benefit from $300 in complimentary credits to experiment with and utilize cloud management capabilities, enhancing both the efficiency and affordability of their virtual setups.

Google Compute Engine is a powerful Infrastructure-as-a-Service (IaaS) solution that delivers users flexible compute resources via virtual machines. This service allows clients to easily allocate resources as required, only incurring charges for what they utilize, which helps them adjust their infrastructure to accommodate fluctuating workloads. By removing the necessity for physical hardware, it provides enhanced flexibility, security, and rapid provisioning to align with business needs. New users can take advantage of $300 in complimentary credits, giving them the opportunity to explore the capabilities of IaaS and experience the adaptability and scalability of Google Compute Engine's cloud platform.

Google Compute Engine's server virtualization feature enables users to operate several virtual machines on one physical server, thereby optimizing resource use and reducing hardware expenses. This innovative solution offers the flexibility to handle various workloads through environment isolation and support for multi-tenancy, streamlining application deployment, management, and scalability. Users can fully customize their virtualized servers, tailoring resources like CPU, memory, and storage to meet the unique needs of their applications. Additionally, new users receive $300 in complimentary credits to explore server virtualization, allowing them to dynamically scale their infrastructure while managing costs effectively.

Google Compute Engine offers virtual machines (VMs) that are both customizable and scalable, allowing users to adapt compute resources to their unique requirements. Supporting an extensive array of operating systems, including various distributions of Linux and Windows, these VMs provide the versatility needed for diverse applications. Users can effortlessly adjust CPU, memory, and storage specifications to match their workload demands, ensuring an optimal balance between performance and cost. Additionally, new users are eligible for $300 in free credits, which can be used to set up and test virtual machines on Google Compute Engine, giving them the opportunity to explore various configurations and enhance their infrastructure.

Google Compute Engine's virtualization technology facilitates the establishment of separate virtual spaces within a common physical framework. This innovative approach allows users to enhance resource efficiency and streamline the management of workloads by hosting several virtual machines (VMs) on a single server. With its advanced virtualization features, Google Compute Engine empowers users to adjust their resources dynamically according to immediate demands, ensuring both optimal performance and cost-effectiveness. New users are welcomed with $300 in complimentary credits, giving them the opportunity to discover the advantages of virtualization for their workloads and improve their cloud infrastructure.