Vultr

Vultr, the Independent Cloud, provides developers and businesses around the world with easy-to-use, high-performance cloud infrastructure at a fraction of the cost of the Big Tech clouds. Featuring virtualized Cloud Compute, Bare Metal, and managed Kubernetes, Vultr has enabled over a million customers to run applications on an elastically scalable cloud that delivers industry-leading price-performance. Many customers prefer Vultr because it offers more than 20 data center locations, across 5 continents, allow them to minimize latency by placing computing power close to end users. Vultr is profitable, privately owned, and part of The Constant Company, LLC. Vultr Holdings was founded in 2014 by David Aninowsky with offices in Central New Jersey and South Florida.    We're hiring!  Join our team today at vultr.com/company/careers/

Vultr is a cloud computing platform founded in 2014 that provides Infrastructure-as-a-Service (IaaS) solutions. The company offers a range of cloud services including virtual private servers (VPS), bare metal servers, dedicated cloud instances, block storage, and managed databases. Vultr operates 32 data center locations across six continents, allowing customers to deploy cloud resources globally. The platform utilizes KVM virtualization technology and provides high-performance SSD storage and Intel/AMD CPUs. Vultr's services are designed to be flexible and scalable, with options ranging from small VPS instances starting at USD 2.50 per month to large dedicated cloud servers. The company focuses on providing developer-friendly tools and APIs to simplify cloud deployment and management.

The toolkit to build machine learning applications at scale

Machine Learning Infrastructure

Vultr Offers AMD Instinct MI300X for AI Cloud Platform

To integrate the AMD Instinct™ MI300X accelerator and ROCm™ software into its cloud infrastructure.

Platforms and frameworks that help developers build and train ML models.

Vultr is a cloud computing platform founded in 2014 that provides Infrastructure-as-a-Service (IaaS) solutions. The company offers a range of cloud services including virtual private servers (VPS), bare metal servers, dedicated cloud instances, block storage, and managed databases. Vultr operates 32 data center locations across six continents, allowing customers to deploy cloud resources globally. The platform utilizes KVM virtualization technology and provides high-performance SSD storage and Intel/AMD CPUs. Vultr's services are designed to be flexible and scalable, with options ranging from small VPS instances starting at USD 2.50 per month to large dedicated cloud servers. The company focuses on providing developer-friendly tools and APIs to simplify cloud deployment and management.In March 2024, Vultr launched Cloud Inference, a serverless platform for deploying AI models globally. This service allows customers to run AI inference workloads across Vultr's network of data centers, leveraging NVIDIA GPUs. Vultr has also introduced sovereign and private cloud offerings to address data sovereignty concerns, partnering with telecom providers like Singtel to deploy locally managed cloud environments. In February 2024, the company launched Vultr CDN, a content delivery network service optimized for content acceleration and API caching.Key customers and partnershipsVultr has served over 1.5 million customers across 185 countries. The company caters to a range of users from individual developers to large enterprises. Vultr has formed strategic partnerships to enhance its offerings, including a collaboration with Run:ai to provide advanced GPU orchestration for AI workloads. The company also partnered with Singtel in March 2024 to expand its GPU capacity in Singapore and Southeast Asia, leveraging Singtel's NVIDIA H100 Tensor Core GPU infrastructure. Additionally, Vultr works with Dell Technologies to provide access to Dell PowerEdge servers with NVIDIA GPUs. In the AI space, Music.AI selected Vultr to leverage NVIDIA H100 GPUs for its audio processing platform, which serves over 45 million users worldwide.

Datagen provides a self-service synthetic data platform that generates visual synthetic data of humans for computer vision applications. The platform allows users to write their own code to generate labeled synthetic data for faces, full body, and humans in motion in the form of images and videos to test and train AI models while retaining complete control over the data. The data covers a range of computer vision tasks including face landmark detection, face and hair segmentation, facial recognition and identification, full body pose estimation, full body segmentation, and behavior detection.Key customers and partnershipsThe company serves three of the top five global technology companies and Fortune 500 companies across the automotive, smart fitness, AR/VR/metaverse, smart office, and home security industries. Its notable customers include Google, Toyota, NVIDIA, IBM, and Intel.Funding and financialsIn March 2022, the company raised USD 50 million in a Series B funding round led by Scale Venture Partners. The funds were utilized to bolster its position in the market.Datagen reported to have recorded 8x growth in revenue in 2022.

Datagen

Weights &amp; Biases offers a platform that allows users to build models with experiment tracking, data set and model versioning, data visualization, hyperparameter optimization, and model management. The platform enables users to automatically retrain and re-evaluate models and centrally manage all model versions through every lifecycle stage—from development to staging to production. It also helps to visualize comparisons between model versions to evaluate performance and identify regressions before model deployment. It also offers toolkits such as <a href="https://sp-edge.com/updates/19172">W&amp;B Weave and W&amp;B Production Monitoring </a>to build and monitor data- and ML-powered applications, respectively, through interactive visualizations.The company also offers a <a href="https://sp-edge.com/updates/17880">suite of tools (W&amp;B Prompts) </a>to support large language model operations (LLMOps). W&amp;B Prompts includes tools and features for experiment tracking, debugging, and collaboration, as well as a one-click method to evaluate and score prompts through the OpenAI Eval’s framework.The company provides the platform through a free tier and under a subscription model for up to USD 50 per user per month.Key customers and partnershipsWeights &amp; Biases served more than 700,000 ML practitioners as of August 2023. Its notable include NVIDIA, Toyota, BMW Group, Samsung, Fujifilm, Truebill, and Clear Capital.The company has <a href="https://sp-edge.com/updates/18663">partnered with Lambda </a>, a GPU cloud computing provider, to offer its MLOps platform integrated with the infrastructure services of the latter, catering to enterprises dealing with AI and deep-learning workloads.Funding and financialsIn August 2023, the company raised <a href="https://sp-edge.com/updates/20874">USD 50 million in a strategic investment </a>round at a valuation of USD 1.25 billion, led by Daniel Gross and Nat Friedman. The round also saw participation from Sapphire Ventures, Coatue, Insight Partners, and other investors. The company aimed to utilize the fresh funds to further develop its ML tools, including the launch of W&amp;B Prompts.

Weights & Biases

BentoML offers an open-source platform called Bento Cloud, for the deployment and scaling of AI applications. It is a fully managed platform that handles the complexities of AI infrastructure, providing serverless autoscaling as required during the development and production stages. The platform helps to ship models to production with advanced deployment options including online and shadow A/B testing, while also providing visibility through model performance monitoring. The platform integrates with an organization’s existing data stack and enables users to continuously deploy and improve models in production. The company offers its platform through a combination of subscription and usage-based model.In addition, BentoML also provides the following open-source platforms: 1) Yatai, for model deployment on Kubernetes and 2) bentoctl, for model deployment on any cloud platform.Key customers and partnershipsAs of January 2024, BentoML reported that over 1,000 organizations, comprising more than 3,000 members, use its platform. Its notable customers include Koo, Open Layer, Line, and Naver.The company entered into a strategic partnership with TomTom, a developer of location technology, to help integrate with various models as well as in LLM deployments.Funding and financialsThe company raised <a href="https://sp-edge.com/updates/19684">USD 9 million in seed funding </a>in June 2023 led by DCM Ventures, with participation from Bow Capital. It aimed to channel the newly acquired funds to enhance its platform and offer developers a range of tools for building, deploying, and scaling AI applications efficiently.

BentoML

Iguazio provides a serverless platform based on its open-source Nuclio framework that helps users to develop, deploy, and manage real-time AI applications without requiring them to manually manage underlying infrastructures such as servers and storage. The platform creates real-time intelligence for applications located at the edge, public cloud, and on-premise data centers and reduces bandwidth constraints by consolidating data into a repository and making it available for applications as required. The platform can ingest and transform data from any source, which is then used to train, evaluate, deploy, and monitor models in production.The company leverages Big Data and ML software such as Apache Spark, TensorFlow, and Kubernetes to manage software containers that enable applications to run across different computing environments.Key customers and partnershipsIguazio serves customers in the financial services, telecommunications, smart mobility, manufacturing, retail, AdTech, gaming, healthcare, energy, and utilities sectors. Notable customers include Samsung, Microsoft, NVIDIA, AWS, Google, and Intel.Funding and financialsIn January 2020, the company raised USD 24 million in a funding round led by INCapital Ventures. The funds were utilized to accelerate its growth, expand its reach into new global markets, grow sales, and facilitate new sales channels that include companies such as Dell Technologies

Iguazio

Cogito Tech provides image and video annotation to generate computer vision training data sets for ML &amp; deep learning (DL). The video annotation solution leverages AI-powered tools to convert video into a sequence of images and generates training data sets for ML-based AI models, including self-driving cars and drones. The AI model understands and derives insights from visual data to detect, identify, classify, and track objects in an image or video. Cogito also provides virtual assistants and chatbots to help users in the process of training ML algorithms. Cogito Tech also provides high-quality, ethically sourced datasets that are compliant and reliable to train ML models.In addition, the company provides services to fine-tune LLMs and GenAI applications through reinforcement learning techniques as well as stress test models through red teaming.Key customers and partnershipsAs of December 2022, the company served more than 150 customers across 25 countries in the retail, medical, automotive, agriculture, robotics, smart cities, security, and manufacturing industries. Notable customers includes Verizon Media, The University of Sydney, National Geographic, Tumblr, and Vimeo.

Cogito Tech

Platforms that deploy and host the trained and tested models in production and integrate them into applications through APIs.

Platforms that generate artificial data that mimics the characteristics of real data, which is then used to train ML models at scale.

Platforms that help monitor and manage ML models in training and deployment to track performance, conduct variance analysis, and optimize performance.

Makers of hardware such as CPUs, GPUs, and other application-specific hardware required to train and run ML workloads.

Integrated solutions that handle the end-to-end processes of developing, training, deploying, and monitoring ML models.

Data exchange providers and platforms that offer data labeling services via manual and/or software methods, which are used as input to train ML models.

Includes companies that provide infrastructure to build AI/ML applications

Provides tools to build, monitor, and deploy GenAI applications

Industry is focused on offering platforms for other businesses to build AI-/ML-powered apps

Empowering Tomorrow's Innovations with Generative AI Infrastructure

Generative AI Infrastructure

PROMINENT_CATEGORY

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 Cloud computing platform Vultr has partnered with AI optimization company Run:ai to enhance NVIDIA GPU orchestration for AI and machine learning workloads. This is part of Vultr's Cloud Alliance program and aims to tackle AI development issues such as scaling training jobs, fragmented tools, and long queue times.
 Vultr is combining its scalable infrastructure with Run.ai's workload orchestration platform to offer efficient and cost-effective AI deployments. This will enable simpler creation and management of AI workloads.
 Notably, this partnership comes after NVIDIA's recent acquisition of Run:ai for approximately USD 700 million, which strengthened NVIDIA's position in AI and machine learning, particularly in the area of GPU utilization.
 
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Vultr

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