Alternatives to SwarmOne
Compare SwarmOne alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to SwarmOne in 2026. Compare features, ratings, user reviews, pricing, and more from SwarmOne competitors and alternatives in order to make an informed decision for your business.
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1
Vertex AI
Google
Build, deploy, and scale machine learning (ML) models faster, with fully managed ML tools for any use case. Through Vertex AI Workbench, Vertex AI is natively integrated with BigQuery, Dataproc, and Spark. You can use BigQuery ML to create and execute machine learning models in BigQuery using standard SQL queries on existing business intelligence tools and spreadsheets, or you can export datasets from BigQuery directly into Vertex AI Workbench and run your models from there. Use Vertex Data Labeling to generate highly accurate labels for your data collection. Vertex AI Agent Builder enables developers to create and deploy enterprise-grade generative AI applications. It offers both no-code and code-first approaches, allowing users to build AI agents using natural language instructions or by leveraging frameworks like LangChain and LlamaIndex. -
2
RunPod
RunPod
RunPod offers a cloud-based platform designed for running AI workloads, focusing on providing scalable, on-demand GPU resources to accelerate machine learning (ML) model training and inference. With its diverse selection of powerful GPUs like the NVIDIA A100, RTX 3090, and H100, RunPod supports a wide range of AI applications, from deep learning to data processing. The platform is designed to minimize startup time, providing near-instant access to GPU pods, and ensures scalability with autoscaling capabilities for real-time AI model deployment. RunPod also offers serverless functionality, job queuing, and real-time analytics, making it an ideal solution for businesses needing flexible, cost-effective GPU resources without the hassle of managing infrastructure. -
3
BentoML
BentoML
Serve your ML model in any cloud in minutes. Unified model packaging format enabling both online and offline serving on any platform. 100x the throughput of your regular flask-based model server, thanks to our advanced micro-batching mechanism. Deliver high-quality prediction services that speak the DevOps language and integrate perfectly with common infrastructure tools. Unified format for deployment. High-performance model serving. DevOps best practices baked in. The service uses the BERT model trained with the TensorFlow framework to predict movie reviews' sentiment. DevOps-free BentoML workflow, from prediction service registry, deployment automation, to endpoint monitoring, all configured automatically for your team. A solid foundation for running serious ML workloads in production. Keep all your team's models, deployments, and changes highly visible and control access via SSO, RBAC, client authentication, and auditing logs.Starting Price: Free -
4
CoreWeave
CoreWeave
CoreWeave is a cloud infrastructure provider specializing in GPU-based compute solutions tailored for AI workloads. The platform offers scalable, high-performance GPU clusters that optimize the training and inference of AI models, making it ideal for industries like machine learning, visual effects (VFX), and high-performance computing (HPC). CoreWeave provides flexible storage, networking, and managed services to support AI-driven businesses, with a focus on reliability, cost efficiency, and enterprise-grade security. The platform is used by AI labs, research organizations, and businesses to accelerate their AI innovations. -
5
Amazon SageMaker
Amazon
Amazon SageMaker is an advanced machine learning service that provides an integrated environment for building, training, and deploying machine learning (ML) models. It combines tools for model development, data processing, and AI capabilities in a unified studio, enabling users to collaborate and work faster. SageMaker supports various data sources, such as Amazon S3 data lakes and Amazon Redshift data warehouses, while ensuring enterprise security and governance through its built-in features. The service also offers tools for generative AI applications, making it easier for users to customize and scale AI use cases. SageMaker’s architecture simplifies the AI lifecycle, from data discovery to model deployment, providing a seamless experience for developers. -
6
TensorFlow
TensorFlow
An end-to-end open source machine learning platform. TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications. Build and train ML models easily using intuitive high-level APIs like Keras with eager execution, which makes for immediate model iteration and easy debugging. Easily train and deploy models in the cloud, on-prem, in the browser, or on-device no matter what language you use. A simple and flexible architecture to take new ideas from concept to code, to state-of-the-art models, and to publication faster. Build, deploy, and experiment easily with TensorFlow.Starting Price: Free -
7
Huawei Cloud ModelArts
Huawei Cloud
ModelArts is a comprehensive AI development platform provided by Huawei Cloud, designed to streamline the entire AI workflow for developers and data scientists. It offers a full-lifecycle toolchain that includes data preprocessing, semi-automated data labeling, distributed training, automated model building, and flexible deployment options across cloud, edge, and on-premises environments. It supports popular open source AI frameworks such as TensorFlow, PyTorch, and MindSpore, and allows for the integration of custom algorithms tailored to specific needs. ModelArts features an end-to-end development pipeline that enhances collaboration across DataOps, MLOps, and DevOps, boosting development efficiency by up to 50%. It provides cost-effective AI computing resources with diverse specifications, enabling large-scale distributed training and inference acceleration. -
8
Intel Tiber AI Cloud
Intel
Intel® Tiber™ AI Cloud is a powerful platform designed to scale AI workloads with advanced computing resources. It offers specialized AI processors, such as the Intel Gaudi AI Processor and Max Series GPUs, to accelerate model training, inference, and deployment. Optimized for enterprise-level AI use cases, this cloud solution enables developers to build and fine-tune models with support for popular libraries like PyTorch. With flexible deployment options, secure private cloud solutions, and expert support, Intel Tiber™ ensures seamless integration, fast deployment, and enhanced model performance.Starting Price: Free -
9
Swarm
Docker
Current versions of Docker include swarm mode for natively managing a cluster of Docker Engines called a swarm. Use the Docker CLI to create a swarm, deploy application services to a swarm, and manage swarm behavior. Cluster management integrated with Docker Engine: Use the Docker Engine CLI to create a swarm of Docker Engines where you can deploy application services. You don’t need additional orchestration software to create or manage a swarm. Decentralized design: Instead of handling differentiation between node roles at deployment time, the Docker Engine handles any specialization at runtime. You can deploy both kinds of nodes, managers and workers, using the Docker Engine. This means you can build an entire swarm from a single disk image. Declarative service model: Docker Engine uses a declarative approach to let you define the desired state of the various services in your application stack. -
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Build your deep learning project quickly on Google Cloud: Quickly prototype with a portable and consistent environment for developing, testing, and deploying your AI applications with Deep Learning Containers. These Docker images use popular frameworks and are performance optimized, compatibility tested, and ready to deploy. Deep Learning Containers provide a consistent environment across Google Cloud services, making it easy to scale in the cloud or shift from on-premises. You have the flexibility to deploy on Google Kubernetes Engine (GKE), AI Platform, Cloud Run, Compute Engine, Kubernetes, and Docker Swarm.
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11
Azure Machine Learning
Microsoft
Accelerate the end-to-end machine learning lifecycle with Azure Machine Learning Studio. Empower developers and data scientists with a wide range of productive experiences for building, training, and deploying machine learning models faster. Accelerate time to market and foster team collaboration with industry-leading MLOps—DevOps for machine learning. Innovate on a secure, trusted platform, designed for responsible ML. Productivity for all skill levels, with code-first and drag-and-drop designer, and automated machine learning. Robust MLOps capabilities that integrate with existing DevOps processes and help manage the complete ML lifecycle. Responsible ML capabilities – understand models with interpretability and fairness, protect data with differential privacy and confidential computing, and control the ML lifecycle with audit trials and datasheets. Best-in-class support for open-source frameworks and languages including MLflow, Kubeflow, ONNX, PyTorch, TensorFlow, Python, and R. -
12
SambaNova
SambaNova Systems
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. We give our customers the optionality to experience through the cloud or on-premise. -
13
Swarm
Swarm Foundation
Swarm is a decentralized data storage and distribution technology. Ready to power the next generation of censorship-resistant, unstoppable, serverless dapps. Swarm continues where the blockchain ends, making the world computer real. Swarm is open source code, limited only by the people who use and maintain it - Join a community building the future of the web. Redundant storage with local replication ensures data availability even in the face of node dropouts or data loss. Swarm is decentralized and distributed, and so it’s also always up, making it stable and reliable. -
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NetApp AIPod
NetApp
NetApp AIPod is a comprehensive AI infrastructure solution designed to streamline the deployment and management of artificial intelligence workloads. By integrating NVIDIA-validated turnkey solutions, such as NVIDIA DGX BasePOD™ and NetApp's cloud-connected all-flash storage, AIPod consolidates analytics, training, and inference capabilities into a single, scalable system. This convergence enables organizations to rapidly implement AI workflows, from model training to fine-tuning and inference, while ensuring robust data management and security. With preconfigured infrastructure optimized for AI tasks, NetApp AIPod reduces complexity, accelerates time to insights, and supports seamless integration into hybrid cloud environments. -
15
Baseten
Baseten
Baseten is a high-performance platform designed for mission-critical AI inference workloads. It supports serving open-source, custom, and fine-tuned AI models on infrastructure built specifically for production scale. Users can deploy models on Baseten’s cloud, their own cloud, or in a hybrid setup, ensuring flexibility and scalability. The platform offers inference-optimized infrastructure that enables fast training and seamless developer workflows. Baseten also provides specialized performance optimizations tailored for generative AI applications such as image generation, transcription, text-to-speech, and large language models. With 99.99% uptime, low latency, and support from forward deployed engineers, Baseten aims to help teams bring AI products to market quickly and reliably.Starting Price: Free -
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SwarmZero
SwarmZero
SwarmZero is a decentralized platform designed to empower AI researchers, machine learning engineers, and agent builders by providing tools to rapidly build, deploy, and monetize AI agents. It offers an intuitive agent builder, enabling users to create agents without extensive coding knowledge, and supports integration with multiple machine learning models, APIs, and knowledge files to enhance agent capabilities. SwarmZero's Agent Hub serves as a digital marketplace where developers can publish their AI agents, allowing customers to browse and select solutions tailored to their needs. Additionally, it introduces the concept of "Swarms," which are groups of agents that collaborate to handle complex workflows, thereby enhancing efficiency and productivity. By promoting a transparent and community-driven ecosystem, SwarmZero aims to democratize AI development and monetization, making it accessible to a broader audience. Starting Price: $15 per month -
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Nebius
Nebius
Training-ready platform with NVIDIA® H100 Tensor Core GPUs. Competitive pricing. Dedicated support. Built for large-scale ML workloads: Get the most out of multihost training on thousands of H100 GPUs of full mesh connection with latest InfiniBand network up to 3.2Tb/s per host. Best value for money: Save at least 50% on your GPU compute compared to major public cloud providers*. Save even more with reserves and volumes of GPUs. Onboarding assistance: We guarantee a dedicated engineer support to ensure seamless platform adoption. Get your infrastructure optimized and k8s deployed. Fully managed Kubernetes: Simplify the deployment, scaling and management of ML frameworks on Kubernetes and use Managed Kubernetes for multi-node GPU training. Marketplace with ML frameworks: Explore our Marketplace with its ML-focused libraries, applications, frameworks and tools to streamline your model training. Easy to use. We provide all our new users with a 1-month trial period.Starting Price: $2.66/hour -
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QpiAI
QpiAI
QpiAI Pro is a no-code AutoML and MLOps platform designed to empower AI development with generative AI tools for automated data annotation, foundation model tuning, and scalable deployment. It offers flexible deployment solutions tailored to meet unique enterprise needs, including cloud VPC deployment within enterprise VPC on the public cloud, managed service on public cloud with integrated QpiAI serverless billing infrastructure, and enterprise data center deployment for complete control over security and compliance. These options enhance operational efficiency and provide end-to-end access to platform functionalities. QpiAI Pro is part of QpiAI's suite of products that integrate AI and quantum technologies in enterprise solutions, aiming to solve complex scientific and business problems across various industries. -
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SWARM
SWARM
SWARM Engineering is an AI-powered SaaS platform built to help organizations tackle complex operational challenges, such as supply-chain disruption, workforce planning, and production logistics, through a methodology combined with Agentic AI. The workflow begins when a business user defines a specific operational problem via their Challenge Modeler; SWARM then uses its Solution Engine, an open library of multi-agent systems, optimization algorithms, and machine-learning models, to ingest data (from ERPs, spreadsheets, or IoT feeds), run simulations, and deploy a tailored solution through their Ops Dashboard. The system is designed for enterprise-scale deployment on Microsoft Azure, supports no-code configuration so business users can interact without needing data-science skills, and promises rapid time-to-impact (e.g., planning cycles reduced by up to 400%) and strong ROI in industries such as ag-food, manufacturing, and distribution. -
20
WindESCo
WindESCo
WindESCo offers advanced solutions to enhance wind turbine performance and reliability through two primary products, Pulse and Swarm. Pulse is an AI and machine learning-powered platform that provides comprehensive performance analytics and asset health monitoring across 12 turbine subsystems. It integrates multiple data sources, including SCADA, events, failure history, maintenance records, vibration, and weather data, into a synthesized data fabric, enabling users to identify actionable factors affecting turbine performance. The platform also features case management tools to streamline operations and maintenance processes, track resolution progress, and maintain all relevant information in one place. Swarm is an autonomous collective control technology that enables turbines to communicate and learn from each other to optimize wind plant output. -
21
Intel Open Edge Platform
Intel
The Intel Open Edge Platform simplifies the development, deployment, and scaling of AI and edge computing solutions on standard hardware with cloud-like efficiency. It provides a curated set of components and workflows that accelerate AI model creation, optimization, and application development. From vision models to generative AI and large language models (LLM), the platform offers tools to streamline model training and inference. By integrating Intel’s OpenVINO toolkit, it ensures enhanced performance on Intel CPUs, GPUs, and VPUs, allowing organizations to bring AI applications to the edge with ease. -
22
MLflow
MLflow
MLflow is an open source platform to manage the ML lifecycle, including experimentation, reproducibility, deployment, and a central model registry. MLflow currently offers four components. Record and query experiments: code, data, config, and results. Package data science code in a format to reproduce runs on any platform. Deploy machine learning models in diverse serving environments. Store, annotate, discover, and manage models in a central repository. The MLflow Tracking component is an API and UI for logging parameters, code versions, metrics, and output files when running your machine learning code and for later visualizing the results. MLflow Tracking lets you log and query experiments using Python, REST, R API, and Java API APIs. An MLflow Project is a format for packaging data science code in a reusable and reproducible way, based primarily on conventions. In addition, the Projects component includes an API and command-line tools for running projects. -
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Swarm
Swarm
Self-custody gives you full control of assets. Trading is decentralized and regulatory compliant. The future of finance is here. Connect your wallet and experience the gold standard for blockchain-based trading. The gold standard for blockchain-based finance. Trade real world assets (RWAs) on chain today, 100% asset-backed, and regulatory compliant. Web3 self-custody and protocol transparency. We never take custody of your assets. Our battle-proven infrastructure offers full transparency for robust trading. With Swarm, any asset can be tokenized and traded in a regulated environment, including real estate, carbon credits, private holdings, stocks, and bonds. Embed a custom marketplace into your ecosystem using the Swarm platform. Swarm is the first organization in the world to offer tokenized US Treasury bills and public stocks that are tradable on a regulated and decentralized platform. Our platform opens up new opportunities for retail investors and institutional market participants. -
24
NeevCloud
NeevCloud
NeevCloud delivers cutting-edge GPU cloud solutions powered by NVIDIA GPUs like the H200, H100, GB200 NVL72, and many more offering unmatched performance for AI, HPC, and data-intensive workloads. Scale dynamically with flexible pricing and energy-efficient GPUs that reduce costs while maximizing output. Ideal for AI model training, scientific research, media production, and real-time analytics, NeevCloud ensures seamless integration and global accessibility. Experience unparalleled speed, scalability, and sustainability with NeevCloud GPU cloud solutions.Starting Price: $1.69/GPU/hour -
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Amazon SageMaker Unified Studio is a comprehensive, AI and data development environment designed to streamline workflows and simplify the process of building and deploying machine learning models. Built on Amazon DataZone, it integrates various AWS analytics and AI/ML services, such as Amazon EMR, AWS Glue, and Amazon Bedrock, into a single platform. Users can discover, access, and process data from various sources like Amazon S3 and Redshift, and develop generative AI applications. With tools for model development, governance, MLOps, and AI customization, SageMaker Unified Studio provides an efficient, secure, and collaborative environment for data teams.
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26
01.AI
01.AI
The 01.AI Super Employee platform transforms enterprise operations with AI agents capable of deep reasoning, task planning, and end-to-end execution. Through its centralized Solution Console, organizations can manage knowledge bases, train custom models, and deploy business-ready AI solutions with ease. Built for enterprise security, it supports on-premise deployment, secure sandboxing, and MCP connectivity for controlled access to legacy systems and external tools. 01.AI offers a comprehensive suite of industry-specific agents—from sales and insurance to supply chain, finance, and government—each designed to automate workflows across browsers, terminals, cloud phones, and interpreters. With native support for leading LLMs like DeepSeek, Qwen, and Yi, businesses gain a flexible and future-ready AI stack. The platform accelerates AI adoption by enabling rapid deployment, continuous evolution, and seamless integration across enterprise environments. -
27
IBM watsonx.ai
IBM
Now available—a next generation enterprise studio for AI builders to train, validate, tune and deploy AI models IBM® watsonx.ai™ AI studio is part of the IBM watsonx™ AI and data platform, bringing together new generative AI (gen AI) capabilities powered by foundation models and traditional machine learning (ML) into a powerful studio spanning the AI lifecycle. Tune and guide models with your enterprise data to meet your needs with easy-to-use tools for building and refining performant prompts. With watsonx.ai, you can build AI applications in a fraction of the time and with a fraction of the data. Watsonx.ai offers: End-to-end AI governance: Enterprises can scale and accelerate the impact of AI with trusted data across the business, using data wherever it resides. Hybrid, multi-cloud deployments: IBM provides the flexibility to integrate and deploy your AI workloads into your hybrid-cloud stack of choice. -
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The Swarm
The Swarm
The Swarm is a Go-To-Network (GTN) platform designed to help companies and investors unlock the full potential of their extended networks to accelerate sales, recruiting, and fundraising. By mapping and combining the networks of team members, advisors, investors, and partners, The Swarm reveals warm relationships and provides actionable insights into connection strengths. Users can import connections from LinkedIn, Google, and email/calendar contacts, and the platform's AI automatically identifies former colleagues and education overlaps to expand the network. Features include relationship scoring, powerful search filters, intro requests, and integration with CRMs like HubSpot, Salesforce, and Affinity. The Swarm also offers a Chrome extension for seamless LinkedIn integration and supports privacy controls and role-based permissions.Starting Price: $99 per month -
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Pipeshift
Pipeshift
Pipeshift is a modular orchestration platform designed to facilitate the building, deployment, and scaling of open source AI components, including embeddings, vector databases, large language models, vision models, and audio models, across any cloud environment or on-premises infrastructure. The platform offers end-to-end orchestration, ensuring seamless integration and management of AI workloads, and is 100% cloud-agnostic, providing flexibility in deployment. With enterprise-grade security, Pipeshift addresses the needs of DevOps and MLOps teams aiming to establish production pipelines in-house, moving beyond experimental API providers that may lack privacy considerations. Key features include an enterprise MLOps console for managing various AI workloads such as fine-tuning, distillation, and deployment; multi-cloud orchestration with built-in auto-scalers, load balancers, and schedulers for AI models; and Kubernetes cluster management. -
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NVIDIA Triton™ inference server delivers fast and scalable AI in production. Open-source inference serving software, Triton inference server streamlines AI inference by enabling teams deploy trained AI models from any framework (TensorFlow, NVIDIA TensorRT®, PyTorch, ONNX, XGBoost, Python, custom and more on any GPU- or CPU-based infrastructure (cloud, data center, or edge). Triton runs models concurrently on GPUs to maximize throughput and utilization, supports x86 and ARM CPU-based inferencing, and offers features like dynamic batching, model analyzer, model ensemble, and audio streaming. Triton helps developers deliver high-performance inference aTriton integrates with Kubernetes for orchestration and scaling, exports Prometheus metrics for monitoring, supports live model updates, and can be used in all major public cloud machine learning (ML) and managed Kubernetes platforms. Triton helps standardize model deployment in production.Starting Price: Free
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Perception Platform
Intuition Machines
The Perception Platform by Intuition Machines automates the entire lifecycle of machine learning models—from training to deployment and continuous improvement. Featuring advanced active learning, the platform enables models to evolve by learning from new data and human interaction, enhancing accuracy while reducing manual oversight. Robust APIs facilitate seamless integration with existing systems, making it scalable and easy to adopt across diverse AI/ML applications. -
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Swarm
OpenAI
Swarm is an experimental, educational framework developed by OpenAI to explore ergonomic, lightweight multi-agent orchestration. It is designed to be scalable and highly customizable, making it suitable for scenarios involving a large number of independent capabilities and instructions that are challenging to encode into a single prompt. Swarm operates entirely on the client side and, like the Chat Completions API it utilizes, does not store state between calls. This stateless nature allows for the construction of scalable, real-world solutions without a steep learning curve. Swarm agents are distinct from assistants in the assistants API; they are named similarly for convenience but are otherwise completely unrelated. It includes examples demonstrating fundamentals such as setup, function calling, handoffs, and context variables, as well as more complex scenarios like a multi-agent setup for handling different customer service requests in an airline context.Starting Price: Free -
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Predibase
Predibase
Declarative machine learning systems provide the best of flexibility and simplicity to enable the fastest-way to operationalize state-of-the-art models. Users focus on specifying the “what”, and the system figures out the “how”. Start with smart defaults, but iterate on parameters as much as you’d like down to the level of code. Our team pioneered declarative machine learning systems in industry, with Ludwig at Uber and Overton at Apple. Choose from our menu of prebuilt data connectors that support your databases, data warehouses, lakehouses, and object storage. Train state-of-the-art deep learning models without the pain of managing infrastructure. Automated Machine Learning that strikes the balance of flexibility and control, all in a declarative fashion. With a declarative approach, finally train and deploy models as quickly as you want. -
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Paradigm
Paradigm
Leverage swarms of agents to gather, structure, and take action on data with human-level precision. -
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Orq.ai
Orq.ai
Orq.ai is the #1 platform for software teams to operate agentic AI systems at scale. Optimize prompts, deploy use cases, and monitor performance, no blind spots, no vibe checks. Experiment with prompts and LLM configurations before moving to production. Evaluate agentic AI systems in offline environments. Roll out GenAI features to specific user groups with guardrails, data privacy safeguards, and advanced RAG pipelines. Visualize all events triggered by agents for fast debugging. Get granular control on cost, latency, and performance. Connect to your favorite AI models, or bring your own. Speed up your workflow with out-of-the-box components built for agentic AI systems. Manage core stages of the LLM app lifecycle in one central platform. Self-hosted or hybrid deployment with SOC 2 and GDPR compliance for enterprise security. -
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AWS Neuron
Amazon Web Services
It supports high-performance training on AWS Trainium-based Amazon Elastic Compute Cloud (Amazon EC2) Trn1 instances. For model deployment, it supports high-performance and low-latency inference on AWS Inferentia-based Amazon EC2 Inf1 instances and AWS Inferentia2-based Amazon EC2 Inf2 instances. With Neuron, you can use popular frameworks, such as TensorFlow and PyTorch, and optimally train and deploy machine learning (ML) models on Amazon EC2 Trn1, Inf1, and Inf2 instances with minimal code changes and without tie-in to vendor-specific solutions. AWS Neuron SDK, which supports Inferentia and Trainium accelerators, is natively integrated with PyTorch and TensorFlow. This integration ensures that you can continue using your existing workflows in these popular frameworks and get started with only a few lines of code changes. For distributed model training, the Neuron SDK supports libraries, such as Megatron-LM and PyTorch Fully Sharded Data Parallel (FSDP). -
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Storidge
Storidge
Storidge was built on the idea that operating storage for enterprise applications should be really simple. We take a fundamentally different approach to Kubernetes storage and Docker volumes. By automating storage operations for orchestration systems, such as Kubernetes and Docker Swarm, it saves you time and money by eliminating the need for expensive expertise to setup, and operate storage infrastructure. This enables developers to focus their best energies on writing applications and creating value, and operators on delivering the value faster to market. Add persistent storage to your single node test cluster in seconds. Deploy storage infrastructure as code, and minimize operator decisions while maximizing operational workflow. Automated updates, provisioning, recovery, and high availability. Keep your critical databases and apps running with auto failover and automatic data recovery. -
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AWS Deep Learning AMIs
Amazon
AWS Deep Learning AMIs (DLAMI) provides ML practitioners and researchers with a curated and secure set of frameworks, dependencies, and tools to accelerate deep learning in the cloud. Built for Amazon Linux and Ubuntu, Amazon Machine Images (AMIs) come preconfigured with TensorFlow, PyTorch, Apache MXNet, Chainer, Microsoft Cognitive Toolkit (CNTK), Gluon, Horovod, and Keras, allowing you to quickly deploy and run these frameworks and tools at scale. Develop advanced ML models at scale to develop autonomous vehicle (AV) technology safely by validating models with millions of supported virtual tests. Accelerate the installation and configuration of AWS instances, and speed up experimentation and evaluation with up-to-date frameworks and libraries, including Hugging Face Transformers. Use advanced analytics, ML, and deep learning capabilities to identify trends and make predictions from raw, disparate health data. -
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HoldMyTicket
HoldMyTicket
HoldMyTicket is an innovative ticketing solution. Built for the event industry of today, HoldMyTicket offers custom solutions for our clients. Customized ticketing solutions for any event. Whether you are selling tickets to a small conference, sports arena, or large-scale event, HoldMyTicket has you covered! With HoldMyTicket’s Spark event management and ticketing solutions, we have made it simple for our users to coordinate every step of their event and sell tickets online in minutes! Integrate social media and marketing tools, reports and analytics, and gain access to the best online ticket service! HoldMyTicket’s Swarm Box Office app was designed with our client's needs first and gives you the power of a full-service box office at your fingertips! No wifi? No Problem! Swarm Box Office is the first in our industry to offer offline ticket scanning! Designed with the cloud in mind, Swarm Box Office supports iOS, Android, Windows, Mac, and all web browsers.Starting Price: $0.01 -
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Amazon SageMaker Model Training reduces the time and cost to train and tune machine learning (ML) models at scale without the need to manage infrastructure. You can take advantage of the highest-performing ML compute infrastructure currently available, and SageMaker can automatically scale infrastructure up or down, from one to thousands of GPUs. Since you pay only for what you use, you can manage your training costs more effectively. To train deep learning models faster, SageMaker distributed training libraries can automatically split large models and training datasets across AWS GPU instances, or you can use third-party libraries, such as DeepSpeed, Horovod, or Megatron. Efficiently manage system resources with a wide choice of GPUs and CPUs including P4d.24xl instances, which are the fastest training instances currently available in the cloud. Specify the location of data, indicate the type of SageMaker instances, and get started with a single click.
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41
sipXcom
sipXcom
sipXcom was established in January of 2015 from a fork in the sipXecs project by the development team at eZuce, Inc. sipXecs and SIPfoundry in particular had become a user community and was not seeing any growth in the developer community. Due to a restrictive contributor agreement, sipXcom is without any restrictive contribution rights. sipXcom source code licensing is based on the copyleft-friendly AGLP v3 (Affero General Public License). SWARM is the code name for the next generation of sipXcom/sipXecs projects. Under development with anticipated production readiness scheduled for early 2017, SWARM will be a microservices-based architecture that will improve upon sipX scalability, reliability and configurability. The software is designed to support any compute platform whether you want to use dedicated, virtual, or cloud-based servers. You can also create hybrid implementations with a combination of premise-based and data center or cloud instances. -
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Aritic Swarm
Aritic
Go beyond traditional messaging with Aritic Swarm. Engage in interactive messaging with text formatting, emojis, sharing, and internal team collaboration. Seamlessly collaborate with your entire team as well as other teams to get work completed faster and drive business growth. Share media, videos, and files with anyone and everyone instantly by simply uploading it from your computer. Do more than one-on-one messaging. Create groups, make video calls, format texts like bold, italics, and more. Turn discussions into real actions. Push your team a step ahead towards smart collaboration by creating and assigning tasks within Aritic Swarm rooms. Like marking important messages in your inbox? But why wait for an email. Mark and save your valuable discussions to tag later and keep pick up from where you left; or just use it as a reference. Aritic Swarm Meetings are compatible on mobiles and desktops alike. -
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DataCore Swarm
DataCore Software
Are you struggling with protecting and providing access to rapidly scaling data sets or enabling distributed content-based use cases? Using tape is cost-effective but data is not instantly accessible; and tape is difficult to manage. The public cloud often presents the challenge of compounding, unpredictable recurring costs, and the inability to meet local performance and privacy requirements. DataCore Swarm provides an on-premises object storage solution that radically simplifies the ability to manage, store, and protect data while allowing S3/HTTP access to any application, device, or end-user. Swarm transforms your data archive into a flexible and immediately accessible content library that enables remote workflows, on-demand access, and massive scalability. -
44
Klu
Klu
Klu.ai is a Generative AI platform that simplifies the process of designing, deploying, and optimizing AI applications. Klu integrates with your preferred Large Language Models, incorporating data from varied sources, giving your applications unique context. Klu accelerates building applications using language models like Anthropic Claude, Azure OpenAI, GPT-4, and over 15 other models, allowing rapid prompt/model experimentation, data gathering and user feedback, and model fine-tuning while cost-effectively optimizing performance. Ship prompt generations, chat experiences, workflows, and autonomous workers in minutes. Klu provides SDKs and an API-first approach for all capabilities to enable developer productivity. Klu automatically provides abstractions for common LLM/GenAI use cases, including: LLM connectors, vector storage and retrieval, prompt templates, observability, and evaluation/testing tooling.Starting Price: $97 -
45
CentML
CentML
CentML accelerates Machine Learning workloads by optimizing models to utilize hardware accelerators, like GPUs or TPUs, more efficiently and without affecting model accuracy. Our technology boosts training and inference speed, lowers compute costs, increases your AI-powered product margins, and boosts your engineering team's productivity. Software is no better than the team who built it. Our team is stacked with world-class machine learning and system researchers and engineers. Focus on your AI products and let our technology take care of optimum performance and lower cost for you. -
46
Core Scientific
Core Scientific
Core Scientific delivers purpose-built high-density colocation infrastructure and intelligent software solutions designed for demanding compute workloads such as AI, machine learning, high-performance computing, and digital asset mining. It features ready-to-scale high-density compute environments with contracted power capacity of over 1.3 GW, faster deployment timelines, and optimized cooling and power systems tailored for intensive workloads. Core Scientific’s digital mining offering incorporates proprietary software for fleet management capable of handling up to one million miners, real-time thermal monitoring, and hash-price economics analysis to optimize profitability. In its colocation and AI-focused infrastructure business, Core Scientific combines high-density racks (50–200 kW+ per rack) and enterprise-grade infrastructure to support AI model training/inference, cloud workloads, financial services analytics, government mission-critical systems, and healthcare research. -
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TensorWave
TensorWave
TensorWave is an AI and high-performance computing (HPC) cloud platform purpose-built for performance, powered exclusively by AMD Instinct Series GPUs. It delivers high-bandwidth, memory-optimized infrastructure that scales with your most demanding models, training, or inference. TensorWave offers access to AMD’s top-tier GPUs within seconds, including the MI300X and MI325X accelerators, which feature industry-leading memory capacity and bandwidth, with up to 256GB of HBM3E supporting 6.0TB/s. TensorWave's architecture includes UEC-ready capabilities that optimize the next generation of Ethernet for AI and HPC networking, and direct liquid cooling that delivers exceptional total cost of ownership with up to 51% data center energy cost savings. TensorWave provides high-speed network storage, ensuring game-changing performance, security, and scalability for AI pipelines. It offers plug-and-play compatibility with a wide range of tools and platforms, supporting models, libraries, etc. -
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Compute with Hivenet
Hivenet
Compute with Hivenet is the world's first truly distributed cloud computing platform, providing reliable and affordable on-demand computing power from a certified network of contributors. Designed for AI model training, inference, and other compute-intensive tasks, it provides secure, scalable, and on-demand GPU resources at up to 70% cost savings compared to traditional cloud providers. Powered by RTX 4090 GPUs, Compute rivals top-tier platforms, offering affordable, transparent pricing with no hidden fees. Compute is part of the Hivenet ecosystem, a comprehensive suite of distributed cloud solutions that prioritizes sustainability, security, and affordability. Through Hivenet, users can leverage their underutilized hardware to contribute to a powerful, distributed cloud infrastructure.Starting Price: $0.10/hour -
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PolySwarm
PolySwarm
Unlike in any other multiscanner, in PolySwarm there is money at stake: threat detection engines back their opinions with money, at the artifact level (file, URL, etc.), and are economically rewarded and penalized based on the accuracy of their determinations. The following process is automated and is executed by software (engines) in near real time. Users submit artifacts to PolySwarm’s network via API or web UI. Crowdsourced intelligence (engine determinations) and a final score (PolyScore) are sent back to the User. The money from the bounty and the assertions becomes the reward, which is securely escrowed in an Ethereum smart contract. Engines that made the right assertion are rewarded with the money from the initial bounty from the enterprise plus the money the losing engines included with their assertions.Starting Price: $299 per month -
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Mirantis Container Runtime
Mirantis
Mirantis Container Runtime (MCR), formerly Docker Engine Enterprise, is a secure, enterprise-grade container runtime that enables teams to build and run containers natively on Linux and Windows while using familiar Docker CLI, Dockerfiles, and APIs to power business-critical applications with industry-leading container engine technology and certified support for Kubernetes and Swarm. MCR is fully compatible with Docker-based workflows and toolchains, providing a seamless path from development to production and tested, validated releases across a broad set of operating systems with robust CVE patching and bug fixes to ensure workload stability. It delivers world-class security with FIPS 140-2 validated cryptographic modules, mandatory access controls such as AppArmor and SELinux, image signature verification, and support for sandboxed runtimes like Kata and gVisor to enforce trusted, compliant containers.
