Alternatives to Nebius

Google Cloud is an online service that lets you create everything from simple websites to complex apps for businesses of any size. Customers who are new to the system will receive $300 in credits for testing, deploying, and running workloads. Customers can use up to 25+ products free of charge. Use Google's core data analytics and machine learning. All enterprises can use it. It is secure and fully featured. Use big data to build better products and find answers faster. You can grow from prototypes to production and even to planet-scale without worrying about reliability, capacity or performance. Virtual machines with proven performance/price advantages, to a fully-managed app development platform. High performance, scalable, resilient object storage and databases. Google's private fibre network offers the latest software-defined networking solutions. Fully managed data warehousing and data exploration, Hadoop/Spark and messaging.

Fully managed ML tools allow you to build, deploy and scale machine-learning (ML) models quickly, for any use case. Vertex AI Workbench is natively integrated with BigQuery Dataproc and Spark. You can use BigQuery to create and execute machine-learning models in BigQuery by using standard SQL queries and spreadsheets or you can export datasets directly from BigQuery into Vertex AI Workbench to run your models there. Vertex Data Labeling can be used to create highly accurate labels for data collection. Vertex AI Agent Builder empowers developers to design and deploy advanced generative AI applications for enterprise use. It supports both no-code and code-driven development, enabling users to create AI agents through natural language prompts or by integrating with frameworks like LangChain and LlamaIndex.

RunPod provides a cloud infrastructure that enables seamless deployment and scaling of AI workloads with GPU-powered pods. By offering access to a wide array of NVIDIA GPUs, such as the A100 and H100, RunPod supports training and deploying machine learning models with minimal latency and high performance. The platform emphasizes ease of use, allowing users to spin up pods in seconds and scale them dynamically to meet demand. With features like autoscaling, real-time analytics, and serverless scaling, RunPod is an ideal solution for startups, academic institutions, and enterprises seeking a flexible, powerful, and affordable platform for AI development and inference.

Our comprehensive suite of cloud services allows you to build your cloud server your way. Kamatera’s infrastructure is specialized in VPS hosting. With 24 data centers around the world, including 8 in the US, as well as in Europe, Asia and the Middle East, you can choose from. Our enterprise-grade cloud server can meet your requirements at any stage. We use cutting edge hardware, including Ice Lake Processors, NVMe SSDs, and other components, to deliver consistent performance and 99.95% uptime. With a robust service such as ours, you'll get a lot of great features like fantastic hardware, flexible cloud setup, Windows server hosting, fully managed hosting and data security. We also offer consultation, server migration and disaster recovery. We have a 24/7 live support team to assist you in all time zones. With our flexible and predictable pricing plans, you only pay for the services you use.

CoreWeave stands out as a cloud infrastructure service that focuses on GPU-centric computing solutions specifically designed for artificial intelligence applications. Their platform delivers scalable, high-performance GPU clusters that enhance both training and inference processes for AI models, catering to sectors such as machine learning, visual effects, and high-performance computing. In addition to robust GPU capabilities, CoreWeave offers adaptable storage, networking, and managed services that empower AI-focused enterprises, emphasizing reliability, cost-effectiveness, and top-tier security measures. This versatile platform is widely adopted by AI research facilities, labs, and commercial entities aiming to expedite their advancements in artificial intelligence technology. By providing an infrastructure that meets the specific demands of AI workloads, CoreWeave plays a crucial role in driving innovation across various industries.

NeevCloud offers cutting-edge GPU cloud services powered by NVIDIA GPUs such as the H200, GB200 NVL72 and others. These GPUs offer unmatched performance in AI, HPC and data-intensive workloads. Flexible pricing and energy-efficient graphics cards allow you to scale dynamically, reducing costs while increasing output. NeevCloud is ideal for AI model training and scientific research. It also ensures seamless integration, global accessibility, and media production. NeevCloud GPU Cloud Solutions offer unparalleled speed, scalability and sustainability.

Effortlessly launch cloud servers, bare metal solutions, and storage options globally! Our high-performance computing instances are ideal for both your web applications and development environments. Once you hit the deploy button, Vultr’s cloud orchestration takes charge and activates your instance in the selected data center. You can create a new instance featuring your chosen operating system or a pre-installed application in mere seconds. Additionally, you can scale the capabilities of your cloud servers as needed. For mission-critical systems, automatic backups are crucial; you can set up scheduled backups with just a few clicks through the customer portal. With our user-friendly control panel and API, you can focus more on coding and less on managing your infrastructure, ensuring a smoother and more efficient workflow. Enjoy the freedom and flexibility that comes with seamless cloud deployment and management!

It enables high-performance training on Amazon Elastic Compute Cloud (Amazon EC2) Trn1 instances, which are powered by AWS Trainium. For deploying models, the system offers efficient and low-latency inference capabilities on Amazon EC2 Inf1 instances that utilize AWS Inferentia and on Inf2 instances based on AWS Inferentia2. With the Neuron software development kit, users can seamlessly leverage popular machine learning frameworks like TensorFlow and PyTorch, allowing for the optimal training and deployment of machine learning models on EC2 instances without extensive code modifications or being locked into specific vendor solutions. The AWS Neuron SDK, designed for both Inferentia and Trainium accelerators, integrates smoothly with PyTorch and TensorFlow, ensuring users can maintain their existing workflows with minimal adjustments. Additionally, for distributed model training, the Neuron SDK is compatible with libraries such as Megatron-LM and PyTorch Fully Sharded Data Parallel (FSDP), enhancing its versatility and usability in various ML projects. This comprehensive support makes it easier for developers to manage their machine learning tasks efficiently.

Dataoorts GPU Cloud was built for AI. Dataoorts offers GC2 and a X-Series GPU instance to help you excel in your development tasks. Dataoorts GPU instances ensure that computational power is available to everyone, everywhere. Dataoorts can help you with your training, scaling and deployment tasks. Serverless computing allows you to create your own inference endpoint API cost you just $5 Per month.

The NVIDIA Triton™ inference server provides efficient and scalable AI solutions for production environments. This open-source software simplifies the process of AI inference, allowing teams to deploy trained models from various frameworks, such as TensorFlow, NVIDIA TensorRT®, PyTorch, ONNX, XGBoost, Python, and more, across any infrastructure that relies on GPUs or CPUs, whether in the cloud, data center, or at the edge. By enabling concurrent model execution on GPUs, Triton enhances throughput and resource utilization, while also supporting inferencing on both x86 and ARM architectures. It comes equipped with advanced features such as dynamic batching, model analysis, ensemble modeling, and audio streaming capabilities. Additionally, Triton is designed to integrate seamlessly with Kubernetes, facilitating orchestration and scaling, while providing Prometheus metrics for effective monitoring and supporting live updates to models. This software is compatible with all major public cloud machine learning platforms and managed Kubernetes services, making it an essential tool for standardizing model deployment in production settings. Ultimately, Triton empowers developers to achieve high-performance inference while simplifying the overall deployment process.

The Intel® Tiber™ AI Cloud serves as a robust platform tailored to efficiently scale artificial intelligence workloads through cutting-edge computing capabilities. Featuring specialized AI hardware, including the Intel Gaudi AI Processor and Max Series GPUs, it enhances the processes of model training, inference, and deployment. Aimed at enterprise-level applications, this cloud offering allows developers to create and refine models using well-known libraries such as PyTorch. Additionally, with a variety of deployment choices, secure private cloud options, and dedicated expert assistance, Intel Tiber™ guarantees smooth integration and rapid deployment while boosting model performance significantly. This comprehensive solution is ideal for organizations looking to harness the full potential of AI technologies.

Create your generative AI solutions in just a few minutes with GMI GPU Cloud. GMI Cloud goes beyond simple bare metal offerings by enabling you to train, fine-tune, and run cutting-edge models seamlessly. Our clusters come fully prepared with scalable GPU containers and widely-used ML frameworks, allowing for immediate access to the most advanced GPUs tailored for your AI tasks. Whether you seek flexible on-demand GPUs or dedicated private cloud setups, we have the perfect solution for you. Optimize your GPU utility with our ready-to-use Kubernetes software, which simplifies the process of allocating, deploying, and monitoring GPUs or nodes through sophisticated orchestration tools. You can customize and deploy models tailored to your data, enabling rapid development of AI applications. GMI Cloud empowers you to deploy any GPU workload swiftly and efficiently, allowing you to concentrate on executing ML models instead of handling infrastructure concerns. Launching pre-configured environments saves you valuable time by eliminating the need to build container images, install software, download models, and configure environment variables manually. Alternatively, you can utilize your own Docker image to cater to specific requirements, ensuring flexibility in your development process. With GMI Cloud, you'll find that the path to innovative AI applications is smoother and faster than ever before.

ModelArts, an all-encompassing AI development platform from Huawei Cloud, is crafted to optimize the complete AI workflow for both developers and data scientists. This platform encompasses a comprehensive toolchain that facilitates various phases of AI development, including data preprocessing, semi-automated data labeling, distributed training, automated model creation, and versatile deployment across cloud, edge, and on-premises systems. It is compatible with widely used open-source AI frameworks such as TensorFlow, PyTorch, and MindSpore, while also enabling the integration of customized algorithms to meet unique project requirements. The platform's end-to-end development pipeline fosters enhanced collaboration among DataOps, MLOps, and DevOps teams, resulting in improved development efficiency by as much as 50%. Furthermore, ModelArts offers budget-friendly AI computing resources with a range of specifications, supporting extensive distributed training and accelerating inference processes. This flexibility empowers organizations to adapt their AI solutions to meet evolving business challenges effectively.

Deploy GPU-accelerated instances that can be finely tuned to suit your AI requirements and financial plan. Secure access to thousands of GPUs within a cutting-edge AI data center, ideal for extensive training and inference operations. The trend in the AI landscape is clearly leaning towards GPU cloud solutions, allowing for the creation and deployment of innovative models while alleviating the challenges associated with infrastructure management and resource limitations. AI-focused cloud providers significantly surpass conventional hyperscalers in terms of availability, cost efficiency, and the ability to scale GPU usage for intricate AI tasks. Ori boasts a diverse array of GPU types, each designed to meet specific processing demands, which leads to a greater availability of high-performance GPUs compared to standard cloud services. This competitive edge enables Ori to deliver increasingly attractive pricing each year, whether for pay-as-you-go instances or dedicated servers. In comparison to the hourly or usage-based rates of traditional cloud providers, our GPU computing expenses are demonstrably lower for running extensive AI operations. Additionally, this cost-effectiveness makes Ori a compelling choice for businesses seeking to optimize their AI initiatives.

With Mystic, you have the flexibility to implement machine learning within your own Azure, AWS, or GCP account, or alternatively, utilize our shared GPU cluster for deployment. All Mystic functionalities are seamlessly integrated into your cloud environment. This solution provides a straightforward and efficient method for executing ML inference in a manner that is both cost-effective and scalable. Our GPU cluster accommodates hundreds of users at once, offering an economical option; however, performance may fluctuate based on the real-time availability of GPUs. Effective AI applications rely on robust models and solid infrastructure, and we take care of the infrastructure aspect for you. Mystic features a fully managed Kubernetes platform that operates within your cloud, along with an open-source Python library and API designed to streamline your entire AI workflow. You will benefit from a high-performance environment tailored for serving your AI models effectively. Additionally, Mystic intelligently adjusts GPU resources by scaling them up or down according to the volume of API requests your models generate. From your Mystic dashboard, command-line interface, and APIs, you can effortlessly monitor, edit, and manage your infrastructure, ensuring optimal performance at all times. This comprehensive approach empowers you to focus on developing innovative AI solutions while we handle the underlying complexities.

NVIDIA Picasso is an innovative cloud platform designed for the creation of visual applications utilizing generative AI technology. This service allows businesses, software developers, and service providers to execute inference on their models, train NVIDIA's Edify foundation models with their unique data, or utilize pre-trained models to create images, videos, and 3D content based on text prompts. Fully optimized for GPUs, Picasso enhances the efficiency of training, optimization, and inference processes on the NVIDIA DGX Cloud infrastructure. Organizations and developers are empowered to either train NVIDIA’s Edify models using their proprietary datasets or jumpstart their projects with models that have already been trained in collaboration with prestigious partners. The platform features an expert denoising network capable of producing photorealistic 4K images, while its temporal layers and innovative video denoiser ensure the generation of high-fidelity videos that maintain temporal consistency. Additionally, a cutting-edge optimization framework allows for the creation of 3D objects and meshes that exhibit high-quality geometry. This comprehensive cloud service supports the development and deployment of generative AI-based applications across image, video, and 3D formats, making it an invaluable tool for modern creators. Through its robust capabilities, NVIDIA Picasso sets a new standard in the realm of visual content generation.

Achieve prices that are 3-5 times more competitive than conventional cloud services. FluidStack combines underutilized GPUs from data centers globally to provide unmatched economic advantages in the industry. With just one platform and API, you can deploy over 50,000 high-performance servers in mere seconds. Gain access to extensive A100 and H100 clusters equipped with InfiniBand in just a few days. Utilize FluidStack to train, fine-tune, and launch large language models on thousands of cost-effective GPUs in a matter of minutes. By connecting multiple data centers, FluidStack effectively disrupts monopolistic GPU pricing in the cloud. Experience computing speeds that are five times faster while enhancing cloud efficiency. Instantly tap into more than 47,000 idle servers, all with tier 4 uptime and security, through a user-friendly interface. You can train larger models, set up Kubernetes clusters, render tasks more quickly, and stream content without delays. The setup process requires only one click, allowing for custom image and API deployment in seconds. Additionally, our engineers are available around the clock through Slack, email, or phone, acting as a seamless extension of your team to ensure you receive the support you need. This level of accessibility and assistance can significantly streamline your operations.

OVHcloud empowers technologists and businesses by granting them complete freedom to take control from the very beginning. As a worldwide technology enterprise, we cater to developers, entrepreneurs, and organizations by providing dedicated servers, software, and essential infrastructure components for efficient data management, security, and scaling. Our journey has consistently revolved around challenging conventional norms in order to make technology both accessible and affordable. In today's fast-paced digital landscape, we envision a future that embraces an open ecosystem and cloud environment, allowing everyone to prosper while giving customers the autonomy to decide how, when, and where to manage their data. Trusted by over 1.5 million clients across the globe, we take pride in manufacturing our own servers, managing 30 data centers, and operating an extensive fiber-optic network. Our commitment extends beyond products and services; we prioritize support, foster a vibrant ecosystem, and nurture a dedicated workforce, all while emphasizing our responsibility to society. Through these efforts, we remain devoted to empowering your data seamlessly.

Train advanced models in AI, machine learning, and deep learning effortlessly. With just a few clicks, you can scale your computing resources from a single machine to a complete fleet of virtual machines. Initiate or expand your deep learning endeavors using Lambda Cloud, which allows you to quickly get started, reduce computing expenses, and seamlessly scale up to hundreds of GPUs when needed. Each virtual machine is equipped with the latest version of Lambda Stack, featuring prominent deep learning frameworks and CUDA® drivers. In mere seconds, you can access a dedicated Jupyter Notebook development environment for every machine directly through the cloud dashboard. For immediate access, utilize the Web Terminal within the dashboard or connect via SSH using your provided SSH keys. By creating scalable compute infrastructure tailored specifically for deep learning researchers, Lambda is able to offer substantial cost savings. Experience the advantages of cloud computing's flexibility without incurring exorbitant on-demand fees, even as your workloads grow significantly. This means you can focus on your research and projects without being hindered by financial constraints.

Nscale is a specialized hyperscaler designed specifically for artificial intelligence, delivering high-performance computing that is fine-tuned for training, fine-tuning, and demanding workloads. Our vertically integrated approach in Europe spans from data centers to software solutions, ensuring unmatched performance, efficiency, and sustainability in all our offerings. Users can tap into thousands of customizable GPUs through our advanced AI cloud platform, enabling significant cost reductions and revenue growth while optimizing AI workload management. The platform is crafted to facilitate a smooth transition from development to production, whether employing Nscale's internal AI/ML tools or integrating your own. Users can also explore the Nscale Marketplace, which provides access to a wide array of AI/ML tools and resources that support effective and scalable model creation and deployment. Additionally, our serverless architecture allows for effortless and scalable AI inference, eliminating the hassle of infrastructure management. This system dynamically adjusts to demand, guaranteeing low latency and economical inference for leading generative AI models, ultimately enhancing user experience and operational efficiency. With Nscale, organizations can focus on innovation while we handle the complexities of AI infrastructure.

Amazon EC2 Inf1 instances are specifically engineered to provide efficient and high-performance machine learning inference at a lower cost. These instances can achieve throughput levels that are 2.3 times higher and costs per inference that are 70% lower than those of other Amazon EC2 offerings. Equipped with up to 16 AWS Inferentia chips—dedicated ML inference accelerators developed by AWS—Inf1 instances also include 2nd generation Intel Xeon Scalable processors, facilitating up to 100 Gbps networking bandwidth which is essential for large-scale machine learning applications. They are particularly well-suited for a range of applications, including search engines, recommendation systems, computer vision tasks, speech recognition, natural language processing, personalization features, and fraud detection mechanisms. Additionally, developers can utilize the AWS Neuron SDK to deploy their machine learning models on Inf1 instances, which supports integration with widely-used machine learning frameworks such as TensorFlow, PyTorch, and Apache MXNet, thus enabling a smooth transition with minimal alterations to existing code. This combination of advanced hardware and software capabilities positions Inf1 instances as a powerful choice for organizations looking to optimize their machine learning workloads.

Crusoe delivers a cloud infrastructure tailored for artificial intelligence tasks, equipped with cutting-edge GPU capabilities and top-tier data centers. This platform is engineered for AI-centric computing, showcasing high-density racks alongside innovative direct liquid-to-chip cooling to enhance overall performance. Crusoe’s infrastructure guarantees dependable and scalable AI solutions through features like automated node swapping and comprehensive monitoring, complemented by a dedicated customer success team that assists enterprises in rolling out production-level AI workloads. Furthermore, Crusoe emphasizes environmental sustainability by utilizing clean, renewable energy sources, which enables them to offer economical services at competitive pricing. With a commitment to excellence, Crusoe continuously evolves its offerings to meet the dynamic needs of the AI landscape.

Setting up your environment should be straightforward and hassle-free. We have taken genuine user feedback from our community into account to enhance the developer experience. Our billing structure is crafted from the ground up for cloud-native applications, ensuring you only pay for the resources you utilize, with no hidden costs. Maximize productivity with industry-leading launch times that enable quicker project initiation. Speed up your development cycles, foster innovation, and achieve results at a rapid pace. Experience lightning-fast, streamlined, managed Kubernetes solutions that allow you to host applications and adjust resources whenever required, featuring 90-second cluster launch times and a complimentary control plane. Benefit from enterprise-grade computing instances that leverage Kubernetes, complete with multi-region support, DDoS protection, bandwidth pooling, and a comprehensive suite of developer tools. Enjoy a fully managed, auto-scaling machine learning environment that doesn’t necessitate any Kubernetes or ML proficiency. Seamlessly configure and scale managed databases directly from your Civo dashboard or through our developer API, allowing you to adjust your resources as needed while only paying for what you consume. This approach not only simplifies your workflow but also empowers you to focus on what truly matters: innovation and growth.

CloudPe, a global provider of cloud solutions, offers scalable and secure cloud technology tailored to businesses of all sizes. CloudPe is a joint venture between Leapswitch Networks, Strad Solutions and combines industry expertise to deliver innovative solutions. Key Offerings: Virtual Machines: High performance VMs for various business requirements, including hosting websites and building applications. GPU Instances - NVIDIA GPUs for AI and machine learning. High-performance computing is also available. Kubernetes-as-a-Service: Simplified container orchestration for deploying and managing containerized applications efficiently. S3-Compatible storage: Highly scalable, cost-effective storage solution. Load balancers: Intelligent load-balancing to distribute traffic equally across resources and ensure fast and reliable performance. Why choose CloudPe? 1. Reliability 2. Cost Efficiency 3. Instant Deployment

Foundry represents a revolutionary type of public cloud, driven by an orchestration platform that simplifies access to AI computing akin to the ease of flipping a switch. Dive into the impactful features of our GPU cloud services that are engineered for optimal performance and unwavering reliability. Whether you are overseeing training processes, catering to client needs, or adhering to research timelines, our platform addresses diverse demands. Leading companies have dedicated years to developing infrastructure teams that create advanced cluster management and workload orchestration solutions to minimize the complexities of hardware management. Foundry democratizes this technology, allowing all users to take advantage of computational power without requiring a large-scale team. In the present GPU landscape, resources are often allocated on a first-come, first-served basis, and pricing can be inconsistent across different vendors, creating challenges during peak demand periods. However, Foundry utilizes a sophisticated mechanism design that guarantees superior price performance compared to any competitor in the market. Ultimately, our goal is to ensure that every user can harness the full potential of AI computing without the usual constraints associated with traditional setups.

Amazon EC2 Capacity Blocks for machine learning allow users to secure accelerated compute instances within Amazon EC2 UltraClusters specifically tailored for their ML tasks. This offering includes support for various instance types such as P5en, P5e, P5, and P4d, which utilize NVIDIA's H200, H100, and A100 Tensor Core GPUs, in addition to Trn2 and Trn1 instances powered by AWS Trainium. You have the option to reserve these instances for durations of up to six months, with cluster sizes that can range from a single instance to as many as 64 instances, accommodating a total of 512 GPUs or 1,024 Trainium chips to suit diverse machine learning requirements. Reservations can conveniently be made up to eight weeks ahead of time. By utilizing Amazon EC2 UltraClusters, Capacity Blocks provide a network that is both low-latency and high-throughput, which enhances the efficiency of distributed training processes. This arrangement guarantees reliable access to top-tier computing resources, enabling you to strategize your machine learning development effectively, conduct experiments, create prototypes, and also manage anticipated increases in demand for machine learning applications. Overall, this service is designed to streamline the machine learning workflow while ensuring scalability and performance.

NetApp AIPod presents a holistic AI infrastructure solution aimed at simplifying the deployment and oversight of artificial intelligence workloads. By incorporating NVIDIA-validated turnkey solutions like the NVIDIA DGX BasePOD™ alongside NetApp's cloud-integrated all-flash storage, AIPod brings together analytics, training, and inference into one unified and scalable system. This integration allows organizations to efficiently execute AI workflows, encompassing everything from model training to fine-tuning and inference, while also prioritizing data management and security. With a preconfigured infrastructure tailored for AI operations, NetApp AIPod minimizes complexity, speeds up the path to insights, and ensures smooth integration in hybrid cloud settings. Furthermore, its design empowers businesses to leverage AI capabilities more effectively, ultimately enhancing their competitive edge in the market.

The NVIDIA DGX Cloud provides an AI infrastructure as a service that simplifies the deployment of large-scale AI models and accelerates innovation. By offering a comprehensive suite of tools for machine learning, deep learning, and HPC, this platform enables organizations to run their AI workloads efficiently on the cloud. With seamless integration into major cloud services, it offers the scalability, performance, and flexibility necessary for tackling complex AI challenges, all while eliminating the need for managing on-premise hardware.

Wallaroo streamlines the final phase of your machine learning process, ensuring that ML is integrated into your production systems efficiently and rapidly to enhance financial performance. Built specifically for simplicity in deploying and managing machine learning applications, Wallaroo stands out from alternatives like Apache Spark and bulky containers. Users can achieve machine learning operations at costs reduced by up to 80% and can effortlessly scale to accommodate larger datasets, additional models, and more intricate algorithms. The platform is crafted to allow data scientists to swiftly implement their machine learning models with live data, whether in testing, staging, or production environments. Wallaroo is compatible with a wide array of machine learning training frameworks, providing flexibility in development. By utilizing Wallaroo, you can concentrate on refining and evolving your models while the platform efficiently handles deployment and inference, ensuring rapid performance and scalability. This way, your team can innovate without the burden of complex infrastructure management.

Our infrastructure is designed to fulfill all your computing needs, whether you require a single GPU or thousands, or just one vCPU to a vast array of tens of thousands of vCPUs; we have you fully covered. Our resources are always on standby to support your requirements, anytime you need them. With our platform, switching between GPU and CPU instances is incredibly simple. You can easily deploy, adjust, and scale your instances to fit your specific needs without any complications. Enjoy exceptional machine learning capabilities without overspending. We offer the most advanced technology at a much more affordable price. Our state-of-the-art GPUs are engineered to handle the demands of your workloads efficiently. Experience computational resources that are specifically designed to accommodate the complexities of your models. Utilize our infrastructure for large-scale inference and gain access to essential libraries through our OblivusAI OS. Furthermore, enhance your gaming experience by taking advantage of our powerful infrastructure, allowing you to play games in your preferred settings while optimizing performance. This flexibility ensures that you can adapt to changing requirements seamlessly.

Be it prompt engineering, fine-tuning, or extensive training, we are fully equipped to fulfill your business needs. Seamlessly incorporate your newly developed model into your application with the Together Inference API, which offers unparalleled speed and flexible scaling capabilities. Together AI is designed to adapt to your evolving requirements as your business expands. You can explore the training processes of various models and the datasets used to enhance their accuracy while reducing potential risks. It's important to note that the ownership of the fine-tuned model lies with you, not your cloud service provider, allowing for easy transitions if you decide to switch providers for any reason, such as cost adjustments. Furthermore, you can ensure complete data privacy by opting to store your data either locally or within our secure cloud environment. The flexibility and control we offer empower you to make decisions that best suit your business.

Intel® Tiber™ AI Studio serves as an all-encompassing machine learning operating system designed to streamline and unify the development of artificial intelligence. This robust platform accommodates a diverse array of AI workloads and features a hybrid multi-cloud infrastructure that enhances the speed of ML pipeline creation, model training, and deployment processes. By incorporating native Kubernetes orchestration and a meta-scheduler, Tiber™ AI Studio delivers unparalleled flexibility for managing both on-premises and cloud resources. Furthermore, its scalable MLOps framework empowers data scientists to seamlessly experiment, collaborate, and automate their machine learning workflows, all while promoting efficient and cost-effective resource utilization. This innovative approach not only boosts productivity but also fosters a collaborative environment for teams working on AI projects.

Our Linux virtual machines simplify cloud infrastructure and provide a robust set of tools that make it easy to develop, deploy, scale, and scale modern applications faster and more efficiently. Linode believes virtual computing is essential to enable innovation in the cloud. It must be accessible, affordable, and easy. Our infrastructure-as-a-service platform is deployed across 11 global markets from our data centers around the world and is supported by our Next Generation Network, advanced APIs, comprehensive services, and vast library of educational resources. Linode products, services and people allow developers and businesses to create, deploy, scale, and scale applications in the cloud more efficiently and cost-effectively.

Amazon SageMaker Model Training streamlines the process of training and fine-tuning machine learning (ML) models at scale, significantly cutting down both time and expenses while eliminating the need for infrastructure management. Users can leverage some of the most advanced ML computing resources on the market, with SageMaker offering the capability to automatically adjust infrastructure from a single GPU to thousands, ensuring optimal performance. With a pay-as-you-go model, it becomes easier to keep training costs under control. To enhance the speed of deep learning model training, SageMaker’s distributed training libraries can efficiently distribute large models and datasets across multiple AWS GPU instances, and users also have the option to implement third-party solutions like DeepSpeed, Horovod, or Megatron. The platform allows for effective management of system resources by providing a diverse selection of GPUs and CPUs, including the P4d.24xl instances, recognized as the fastest training instances available in the cloud. Users can easily specify data locations, choose the appropriate SageMaker instance types, and initiate their training processes with just one click, simplifying the overall experience. Overall, SageMaker provides an accessible and efficient way to harness the power of machine learning without the usual complexities of infrastructure management.

Enhance the efficiency of your deep learning projects and reduce the time it takes to realize value through AI model training and inference. As technology continues to improve in areas like computation, algorithms, and data accessibility, more businesses are embracing deep learning to derive and expand insights in fields such as speech recognition, natural language processing, and image classification. This powerful technology is capable of analyzing text, images, audio, and video on a large scale, allowing for the generation of patterns used in recommendation systems, sentiment analysis, financial risk assessments, and anomaly detection. The significant computational resources needed to handle neural networks stem from their complexity, including multiple layers and substantial training data requirements. Additionally, organizations face challenges in demonstrating the effectiveness of deep learning initiatives that are executed in isolation, which can hinder broader adoption and integration. The shift towards more collaborative approaches may help mitigate these issues and enhance the overall impact of deep learning strategies within companies.

SambaNova is the leading purpose-built AI system for generative and agentic AI implementations, from chips to models, that gives enterprises full control over their model and private data. We take the best models, optimize them for fast tokens and higher batch sizes, the largest inputs and enable customizations to deliver value with simplicity. The full suite includes the SambaNova DataScale system, the SambaStudio software, and the innovative SambaNova Composition of Experts (CoE) model architecture. These components combine into a powerful platform that delivers unparalleled performance, ease of use, accuracy, data privacy, and the ability to power every use case across the world's largest organizations. At the heart of SambaNova innovation is the fourth generation SN40L Reconfigurable Dataflow Unit (RDU). Purpose built for AI workloads, the SN40L RDU takes advantage of a dataflow architecture and a three-tiered memory design. The dataflow architecture eliminates the challenges that GPUs have with high performance inference. The three tiers of memory enable the platform to run hundreds of models on a single node and to switch between them in microseconds. We give our customers the optionality to experience through the cloud or on-premise.

The latest generation of NVIDIA GPU-based instances offered by Amazon EC2, known as G5 instances, are designed for a variety of graphics-heavy and machine-learning applications. These instances provide up to three times the performance for graphics-intensive tasks and machine learning inference, along with an impressive 3.3 times increase in training performance when compared to the previous G4dn instances. Ideal for applications that require high-quality graphics in real-time, G5 instances are suitable for remote workstations, video rendering, and gaming. Furthermore, they offer a powerful and cost-effective infrastructure for machine learning users, enabling the training and deployment of larger and more complex models in areas such as natural language processing, computer vision, and recommendation systems. Notably, G5 instances boast graphics performance that is three times higher and a 40% improvement in price performance over their G4dn counterparts. Additionally, they feature the highest number of ray tracing cores among all GPU-based EC2 instances, enhancing their capability to handle advanced graphic rendering tasks. This makes G5 instances a compelling choice for developers and businesses looking to leverage cutting-edge technology for their projects.

Businesses now have numerous options to efficiently train their deep learning and machine learning models without breaking the bank. AI accelerators cater to various scenarios, providing solutions that range from economical inference to robust training capabilities. Getting started is straightforward, thanks to an array of services designed for both development and deployment purposes. Custom-built ASICs known as Tensor Processing Units (TPUs) are specifically designed to train and run deep neural networks with enhanced efficiency. With these tools, organizations can develop and implement more powerful and precise models at a lower cost, achieving faster speeds and greater scalability. A diverse selection of NVIDIA GPUs is available to facilitate cost-effective inference or to enhance training capabilities, whether by scaling up or by expanding out. Furthermore, by utilizing RAPIDS and Spark alongside GPUs, users can execute deep learning tasks with remarkable efficiency. Google Cloud allows users to run GPU workloads while benefiting from top-tier storage, networking, and data analytics technologies that improve overall performance. Additionally, when initiating a VM instance on Compute Engine, users can leverage CPU platforms, which offer a variety of Intel and AMD processors to suit different computational needs. This comprehensive approach empowers businesses to harness the full potential of AI while managing costs effectively.

Accelerate the building, training, and deployment of models at scale through a fully managed infrastructure that provides essential tools and streamlined workflows. Launch personalized AI and LLMs on any infrastructure in mere seconds, effortlessly scaling inference as required. Tackle your most intensive tasks with batch job scheduling, ensuring you only pay for what you use on a per-second basis. Reduce costs effectively by utilizing GPU resources, spot instances, and a built-in automatic failover mechanism. Simplify complex infrastructure configurations by deploying with just a single command using YAML. Adjust to demand by automatically increasing worker capacity during peak traffic periods and reducing it to zero when not in use. Release advanced models via persistent endpoints within a serverless architecture, maximizing resource efficiency. Keep a close eye on system performance and inference metrics in real-time, tracking aspects like worker numbers, GPU usage, latency, and throughput. Additionally, carry out A/B testing with ease by distributing traffic across various models for thorough evaluation, ensuring your deployments are continually optimized for performance.

AI Infrastructure Virtualization Software. Enhance oversight and management of AI tasks to optimize GPU usage. Run:AI has pioneered the first virtualization layer specifically designed for deep learning training models. By decoupling workloads from the underlying hardware, Run:AI establishes a collective resource pool that can be allocated as needed, ensuring that valuable GPU resources are fully utilized. This approach allows for effective management of costly GPU allocations. With Run:AI’s scheduling system, IT departments can direct, prioritize, and synchronize computational resources for data science projects with overarching business objectives. Advanced tools for monitoring, job queuing, and the automatic preemption of tasks according to priority levels provide IT with comprehensive control over GPU resource utilization. Furthermore, by forming a versatile ‘virtual resource pool,’ IT executives can gain insights into their entire infrastructure’s capacity and usage, whether hosted on-site or in the cloud, thus facilitating more informed decision-making. This comprehensive visibility ultimately drives efficiency and enhances resource management.

Hyperbolic is an accessible AI cloud platform focused on making artificial intelligence available to all by offering cost-effective and scalable GPU resources along with AI services. By harnessing worldwide computing capabilities, Hyperbolic empowers businesses, researchers, data centers, and individuals to utilize and monetize GPU resources at significantly lower prices compared to conventional cloud service providers. Their goal is to cultivate a cooperative AI environment that promotes innovation free from the burdens of exorbitant computational costs. This approach not only enhances accessibility but also encourages a diverse range of participants to contribute to the advancement of AI technologies.

Fal represents a serverless Python environment enabling effortless cloud scaling of your code without the need for infrastructure management. It allows developers to create real-time AI applications with incredibly fast inference times, typically around 120 milliseconds. Explore a variety of pre-built models that offer straightforward API endpoints, making it easy to launch your own AI-driven applications. You can also deploy custom model endpoints, allowing for precise control over factors such as idle timeout, maximum concurrency, and automatic scaling. Utilize widely-used models like Stable Diffusion and Background Removal through accessible APIs, all kept warm at no cost to you—meaning you won’t have to worry about the expense of cold starts. Engage in conversations about our product and contribute to the evolution of AI technology. The platform can automatically expand to utilize hundreds of GPUs and retract back to zero when not in use, ensuring you only pay for compute resources when your code is actively running. To get started with fal, simply import it into any Python project and wrap your existing functions with its convenient decorator, streamlining the development process for AI applications. This flexibility makes fal an excellent choice for both novice and experienced developers looking to harness the power of AI.

Qubrid AI stands out as a pioneering company in the realm of Artificial Intelligence (AI), dedicated to tackling intricate challenges across various sectors. Their comprehensive software suite features AI Hub, a centralized destination for AI models, along with AI Compute GPU Cloud and On-Prem Appliances, and the AI Data Connector. Users can develop both their own custom models and utilize industry-leading inference models, all facilitated through an intuitive and efficient interface. The platform allows for easy testing and refinement of models, followed by a smooth deployment process that enables users to harness the full potential of AI in their initiatives. With AI Hub, users can commence their AI journey, transitioning seamlessly from idea to execution on a robust platform. The cutting-edge AI Compute system maximizes efficiency by leveraging the capabilities of GPU Cloud and On-Prem Server Appliances, making it easier to innovate and execute next-generation AI solutions. The dedicated Qubrid team consists of AI developers, researchers, and partnered experts, all committed to continually enhancing this distinctive platform to propel advancements in scientific research and applications. Together, they aim to redefine the future of AI technology across multiple domains.

Your software can see objects in video and images. A few dozen images can be used to train a computer vision model. This takes less than 24 hours. We support innovators just like you in applying computer vision. Upload files via API or manually, including images, annotations, videos, and audio. There are many annotation formats that we support and it is easy to add training data as you gather it. Roboflow Annotate was designed to make labeling quick and easy. Your team can quickly annotate hundreds upon images in a matter of minutes. You can assess the quality of your data and prepare them for training. Use transformation tools to create new training data. See what configurations result in better model performance. All your experiments can be managed from one central location. You can quickly annotate images right from your browser. Your model can be deployed to the cloud, the edge or the browser. Predict where you need them, in half the time.

AWS Deep Learning AMIs (DLAMI) offer machine learning professionals and researchers a well-organized and secure collection of frameworks, dependencies, and tools designed to enhance deep learning capabilities in the cloud environment. These Amazon Machine Images (AMIs), tailored for both Amazon Linux and Ubuntu, come pre-installed with a variety of popular frameworks such as TensorFlow, PyTorch, Apache MXNet, Chainer, Microsoft Cognitive Toolkit (CNTK), Gluon, Horovod, and Keras, which facilitate seamless deployment and scaling of these tools. You can efficiently build sophisticated machine learning models aimed at advancing autonomous vehicle (AV) technologies, utilizing millions of virtual tests to validate these models safely. Furthermore, the solution streamlines the process of setting up and configuring AWS instances, thereby accelerating experimentation and assessment through the use of the latest frameworks and libraries, including Hugging Face Transformers. By leveraging advanced analytics, machine learning, and deep learning features, users can uncover trends and make informed predictions from diverse and raw health data, ultimately leading to improved decision-making in healthcare applications. This comprehensive approach enables practitioners to harness the full potential of deep learning while ensuring they remain at the forefront of innovation in the field.

The Cloud that truly delivers. Scaleway offers a robust foundation for achieving digital success, ranging from a high-performance cloud ecosystem to expansive green datacenters. Tailored for developers and expanding businesses alike, our cloud platform equips you with everything necessary to create, deploy, and scale your infrastructure seamlessly. We provide a variety of services including Compute, GPU, Bare Metal, and Containers, as well as Evolutive & Managed Storage solutions. Our offerings extend to Networking and IoT, featuring the most extensive selection of dedicated servers for even the most challenging projects. In addition to high-end dedicated servers, we also offer Web Hosting and Domain Name Services. Leverage our advanced expertise to securely host your hardware within our resilient and high-performance data centers, with options for Private Suites & Cages, as well as Rack, 1/2, and 1/4 Rack setups. Scaleway operates six state-of-the-art data centers across Europe, delivering cloud solutions to clients in over 160 countries worldwide. Our dedicated Excellence team is available 24/7 throughout the year, ensuring that we are always ready to assist our customers in utilizing, fine-tuning, and optimizing their platforms with the guidance of knowledgeable experts, fostering an environment of continuous improvement and innovation.

In the past, tasks such as deep learning, 3D modeling, simulations, distributed analytics, and molecular modeling could take several days or even weeks to complete. Thanks to GPUonCLOUD’s specialized GPU servers, these processes can now be accomplished in just a few hours. You can choose from a range of pre-configured systems or ready-to-use instances equipped with GPUs that support popular deep learning frameworks like TensorFlow, PyTorch, MXNet, and TensorRT, along with libraries such as the real-time computer vision library OpenCV, all of which enhance your AI/ML model-building journey. Among the diverse selection of GPUs available, certain servers are particularly well-suited for graphics-intensive tasks and multiplayer accelerated gaming experiences. Furthermore, instant jumpstart frameworks significantly boost the speed and flexibility of the AI/ML environment while ensuring effective and efficient management of the entire lifecycle. This advancement not only streamlines workflows but also empowers users to innovate at an unprecedented pace.

Introducing a pioneering compute protocol that combines integrated hardware and software for the training and fine-tuning of AI models. Experience a reduction in training expenses by as much as 80%. You can deploy your models in mere seconds, utilizing either open-source templates or your own customized models. Effortlessly debug your containers while having access to vital resources such as GPU, CPU, Memory, and other performance metrics. Real-time log monitoring allows for immediate insights into your processes. Maintain complete accountability by tracing all models and training datasets with cryptographically verified proofs. Command the entire training workflow effortlessly with just a few straightforward commands. Additionally, you can earn block rewards by contributing your computer to the network, while also tracking essential metrics like connectivity and uptime to ensure optimal performance. The innovative design of this system not only enhances efficiency but also promotes a collaborative environment for AI development.

KServe is a robust model inference platform on Kubernetes that emphasizes high scalability and adherence to standards, making it ideal for trusted AI applications. This platform is tailored for scenarios requiring significant scalability and delivers a consistent and efficient inference protocol compatible with various machine learning frameworks. It supports contemporary serverless inference workloads, equipped with autoscaling features that can even scale to zero when utilizing GPU resources. Through the innovative ModelMesh architecture, KServe ensures exceptional scalability, optimized density packing, and smart routing capabilities. Moreover, it offers straightforward and modular deployment options for machine learning in production, encompassing prediction, pre/post-processing, monitoring, and explainability. Advanced deployment strategies, including canary rollouts, experimentation, ensembles, and transformers, can also be implemented. ModelMesh plays a crucial role by dynamically managing the loading and unloading of AI models in memory, achieving a balance between user responsiveness and the computational demands placed on resources. This flexibility allows organizations to adapt their ML serving strategies to meet changing needs efficiently.

Nebula provides a streamlined solution for deploying and scaling AI projects quickly, efficiently, and at a lower cost on highly reliable, on-demand GPU infrastructure. With Nebula’s cloud, powered by cutting-edge Nvidia GPUs, you can securely train and infer your models while managing your containerized workloads through an intuitive orchestration layer. The platform offers MLOps and low-code/no-code tools that empower business teams to create and implement AI use cases effortlessly, enabling the fast deployment of AI-driven applications with minimal coding required. You have the flexibility to choose between the Nebula containerized AI cloud, your own on-premises setup, or any preferred cloud environment. With Nebula Unify, organizations can develop and scale AI-enhanced business applications in just weeks, rather than the traditional months, making AI adoption more accessible than ever. This makes Nebula an ideal choice for businesses looking to innovate and stay ahead in a competitive marketplace.