Top 50 Ai Compute Platform - Series A

Lepton AI Cloud provides a scalable platform for AI inference and training, utilizing high-performance GPU infrastructure and a fast LLM engine to achieve up to 600 tokens per second. The platform enables enterprises to efficiently deploy and manage AI models, processing over 20 billion tokens and generating 1 million images daily with 99.9% uptime.

FlexAI provides a universal AI compute platform that enables developers to run AI workloads across diverse hardware architectures without code modifications. This approach maximizes resource utilization and energy efficiency, reducing operational complexity and minimizing failures in AI product development.

Provides a compute engine optimized for running multi-step AI workloads by analyzing and tuning workflows as directed acyclic graphs. Enables developers to build compound AI systems using modular components like models, vector databases, and code interpreters, improving performance through automatic workload optimization and maximum parallelism.

TensorWave provides a cloud platform optimized for AI workloads, utilizing AMD's Instinct MI300X accelerators for enhanced training, fine-tuning, and inference capabilities. The platform offers immediate availability, lower total cost of ownership, and seamless integration with popular frameworks like PyTorch and TensorFlow, addressing the need for efficient and scalable AI compute solutions.

The startup provides AI supercomputers that configure and host GPU servers optimized for deep learning and high-performance computing (HPC) applications. This offering enables organizations to scale their computational resources efficiently while minimizing the infrastructure costs associated with AI workloads.

Provides a platform for decentralized AI model training by aggregating global compute resources and enabling multi-node GPU deployments across cloud providers. This reduces costs and increases accessibility for developing large-scale models, while allowing contributors to co-own and improve open-source AI innovations.

Lyceum simplifies AI model training by automating GPU infrastructure selection and deployment. The platform offers one-click GPU deployment, intelligent hardware matching, and predictive runtime analysis to optimize job scheduling for speed and cost efficiency. This allows AI developers and data scientists to focus on model development without managing complex infrastructure.

Io.net Cloud offers a decentralized computing network that provides machine learning engineers with instant, permissionless access to global GPU resources for their workloads. This platform enables efficient deployment of pre-configured clusters, significantly reducing costs and deployment time for AI startups.

Thyris provides a cloud-based AI runtime that utilizes Kubernetes and CPU/RAM resources, eliminating the need for GPUs in AI applications. This technology enables organizations to deploy AI solutions more efficiently and cost-effectively, broadening access to advanced AI capabilities without the high infrastructure costs.

Berkeley Compute is a decentralized GPU platform that leverages blockchain technology to create a distributed network for artificial intelligence processing. This platform enables users to monetize their idle GPU resources, addressing the high costs and accessibility issues associated with traditional cloud computing services.

Hyperbolic provides an open-access AI cloud that aggregates global GPU resources, enabling users to run AI inference and access compute power at significantly reduced costs. The platform addresses the high expenses associated with traditional cloud services by offering flexible, pay-as-you-go GPU access and opportunities for individuals and data centers to monetize idle machines.

Kluisz.ai provides an AI-powered cloud platform for deploying and managing enterprise workloads across on-premises, edge, and hybrid environments. Its API-first architecture and zero-trust security principles enable seamless application composability and unified management for distributed computing resources.

XFA AI provides cloud-based GPU rentals through a user-friendly interface, enabling businesses to access high-performance computing resources without the need for significant upfront investment. This service reduces operational costs associated with GPU usage, making advanced computing more accessible for data-intensive applications.

The startup provides scalable training clusters for startups, graduate students, and research labs, offering access to supercomputers and pre-training clusters on a short-term basis. This model enables clients to efficiently scale their machine learning models without the burden of long-term contracts, significantly reducing costs associated with large training runs.

Starcloud builds hyperscale data centers in orbit to overcome the limitations of terrestrial infrastructure for AI training. These orbital facilities offer continuous solar energy and radiative cooling, enabling the deployment of massive computational resources unhindered by Earth-based constraints.

Union.ai is a Kubernetes-native workflow orchestration platform that streamlines the development, management, and deployment of AI models at scale. It addresses challenges such as disconnected development teams, high cloud costs, and slow time-to-market by providing a unified interface for automating workflows and optimizing resource usage.

Cake provides a modular platform that integrates open-source AI components, compute management, and security features to streamline the development and deployment of AI projects. By offering pre-built integrations and auto-scaling capabilities, Cake reduces the time and resources required for businesses to implement production-ready AI solutions.

Aethirs provides a decentralized cloud infrastructure that delivers on-demand access to enterprise-grade GPUs for AI model training and real-time gaming applications. This solution addresses the need for scalable, low-latency compute resources while ensuring high performance and security across a global network.

Genesis Cloud provides a GPU cloud platform built on NVIDIA's reference architecture, delivering up to 35 times more performance for AI and machine learning workloads at 80% lower costs compared to traditional cloud providers. The platform ensures high security and compliance with EU regulations, enabling enterprises to efficiently manage and scale their AI applications.

The startup offers an artificial intelligence infrastructure platform that enhances the transparency of deep learning models by making their decision-making processes explainable. This platform provides insights into AI operations and optimizes cost management, eliminating the need for custom integration or extensive log analysis.

MAKO provides automated GPU kernel selection and tuning technology that enables the deployment of AI models with up to 70% lower computing costs across any hardware infrastructure. This solution eliminates the need for manual optimization and vendor lock-in, allowing businesses to efficiently scale their AI operations in any cloud or on-premises environment.

The startup offers a federated learning and edge-computing platform that enables AI data-science developers to collaborate while maintaining data privacy and security. By utilizing distributed computing technologies, the platform allows models to continuously improve through local data insights without the need for data transfers, enhancing the accuracy of AI applications across diverse patient populations.

RunPod is a cloud platform that provides globally distributed GPU resources for deploying and scaling machine learning applications, enabling developers to run AI workloads without managing infrastructure. The platform reduces cold-start times to under 250 milliseconds and offers flexible pricing, allowing users to efficiently handle fluctuating demand while minimizing operational costs.

Taalas provides a platform that transforms any AI model into custom silicon, creating Hardcore Models that are hardwired for optimal performance. This approach significantly enhances computational efficiency, achieving up to 1000 times the performance of traditional software implementations.

EnCharge AI develops a scalable analog in-memory computing platform that enhances AI performance by achieving 20 times higher efficiency and 10 times lower total cost of ownership compared to traditional GPU solutions. This technology enables on-device processing, significantly reducing CO2 emissions and ensuring data privacy while making advanced AI accessible beyond cloud infrastructure.

The startup provides a hybrid cloud platform that combines GPU cloud services with AI controller software for on-premises deployments, enabling enterprises to efficiently manage AI workloads. This solution allows businesses to optimize their data center resources while seamlessly scaling to the public cloud as needed.

The startup offers an artificial intelligence orchestration platform that simplifies model management, intelligent caching, and cost tracking for businesses. This platform enhances operational efficiency by optimizing performance and reducing costs, enabling seamless scalability.

The startup provides a decentralized infrastructure for AI that enables users to access and purchase computing services, data storage, and Model as a Service (MaaS) at scale. This platform addresses the high costs and inefficiencies associated with deploying sophisticated AI solutions, allowing users to optimize performance and resource utilization.

Rain AI manufactures energy-efficient hardware specifically designed for artificial intelligence applications, utilizing advanced power management techniques to minimize energy consumption. This technology addresses the high energy demands of AI processing, enabling organizations to reduce operational costs and environmental impact.

Cudos provides a decentralized cloud computing network that allows users to deploy virtual machines and write smart contracts using a globally distributed resource pool. This platform addresses the need for cost-effective, secure, and scalable infrastructure for decentralized applications by enabling permissionless access through digital wallets.

Multiverse Computing develops quantum software solutions that enhance AI and optimization processes, specifically targeting the high computational costs associated with training large language models and optimizing renewable energy systems. Their platform, Singularity, provides tailored algorithms that improve efficiency, reduce costs, and support sustainable practices across various industries.

The startup offers a composable AI cloud platform that enables eCommerce teams to create, configure, and deploy tailored AI solutions without requiring prior AI expertise. This platform addresses the challenge of outdated technology in eCommerce by providing a user-friendly toolkit that allows brands to rapidly implement AI to enhance conversion rates and optimize margins.

TensorOpera provides a scalable AI platform that enables developers and enterprises to build and commercialize generative AI applications, utilizing features such as model deployment, serverless GPU cloud processing, and AI agent APIs. Additionally, TensorOpera's FedML platform facilitates secure federated learning across edge devices, allowing organizations to perform decentralized machine learning without compromising data privacy.

Apolo provides a white-label AI platform that integrates with existing data center infrastructure, enabling colocation and hybrid cloud providers to offer GPU-as-a-Service without the need for costly data migrations. This solution enhances operational efficiency by supporting AI development tools and seamlessly connecting with back-office applications like billing and ERP systems.

Outerbounds provides a human-centric infrastructure for machine learning and data science, utilizing the open-source Metaflow framework originally developed at Netflix. The platform enables data scientists and ML engineers to efficiently develop, scale, and deploy production-ready AI systems while minimizing infrastructure management burdens.

Outerbounds provides a machine learning infrastructure platform that integrates Slurm and Kubernetes to facilitate scalable compute resources for large-scale workflows. This technology enables data scientists and ML engineers to develop, deploy, and manage AI models efficiently while minimizing infrastructure overhead and operational complexity.

TensorOpera offers a unified platform for developing and commercializing generative AI applications. It provides enterprise-grade infrastructure for scalable model training and deployment, along with agent APIs, to streamline the AI lifecycle and accelerate time-to-market.

The startup develops intelligent information processing products that optimize data center operations by enhancing performance and power efficiency while minimizing capital expenditures on computing and storage hardware. Their technology specifically addresses the high costs and energy consumption associated with big data analytics, deep learning, and large-scale computing environments.

SynthBee is developing a computing intelligence platform that utilizes advanced algorithms to enhance data processing and decision-making capabilities. This technology enables organizations to increase innovation efficiency while ensuring data security and scalability in their operations.

The startup operates a data intelligence platform that utilizes machine learning for automated performance and fault modeling in microservices and serverless computing architectures. This technology enables developers to minimize manual errors and enhance software delivery efficiency.

NexGen Cloud provides sustainable Infrastructure as a Service (IaaS) with a focus on high-performance computing (HPC) and GPU infrastructure, utilizing its Hyperstack platform for on-demand GPU as a Service (GPUaaS). The company enables businesses to efficiently integrate AI capabilities into their operations while ensuring data privacy and compliance through its European and North American data centers.

Neurophos develops a photonic computing architecture that utilizes ultra-dense optical modulators to achieve 160,000 TOPS at 300 TOPS per watt, significantly outperforming traditional GPUs. This technology addresses the escalating demand for AI compute power by providing a solution that replaces 100 GPUs with a single processor while consuming only 1% of the energy.

Deeplite provides AI optimization software that enhances the performance of deep neural networks by reducing their size and energy consumption. This technology enables faster inference times, making AI applications more efficient and cost-effective for various industries.

Provides a decentralized platform that aggregates idle GPU resources from data centers and independent providers worldwide, creating a scalable and cost-effective infrastructure for on-demand high-performance computing. This system addresses the shortage of AI-grade GPUs by enabling seamless access to thousands of GPUs, including H100s and A6000s, for applications like AI training, rendering, and scientific computation.

Oumi offers an open-source platform for the end-to-end development and deployment of AI models, supporting pre-training, fine-tuning, and evaluation of text and multimodal architectures. It enables flexible deployment across diverse environments and helps users avoid vendor lock-in while accelerating AI research and development.

Hydra Host provides dedicated bare metal servers with full root access and optimized GPU configurations for AI and high-performance computing workloads, ensuring maximum processing capabilities without the overhead of shared resources. The platform addresses the need for enhanced privacy and security in multi-cloud environments by offering customizable solutions that eliminate vulnerabilities associated with multi-tenant setups.

The startup offers an artificial intelligence platform that integrates with observability tools to collect metrics, logs, and traces from cloud applications and infrastructure. This technology enables IT developers to eliminate human troubleshooting, ensuring uninterrupted application delivery.

The startup offers a cloud-based rendering platform that provides fast and scalable 3D rendering by efficiently matching computing workloads with available cloud resources. This technology enables architects, animators, and designers to lower costs and enhance productivity while meeting tight project deadlines across various 3D software and engines.

The startup offers an artificial intelligence-powered risk resolution platform that simplifies cloud risk remediation through automated code-based mitigation and remediation of vulnerabilities and misconfigurations. This enables security and development operations teams to efficiently address open security issues, significantly reducing the time required for remediation.

Eta Compute develops a no-code MLOps toolchain that optimizes machine learning models for low-power edge devices, enhancing their efficiency and accuracy. This technology enables enterprises to effectively monitor resources while minimizing energy consumption and inference time.