Union.ai

dstack is an open-source container orchestrator designed for AI and ML teams. It simplifies workload orchestration and maximizes GPU utilization across any cloud provider, on-premise cluster, or accelerated hardware. It provides a unified compute layer, streamlining development, training, and model deployment.

UbiOps is a powerful MLOps platform for AI model serving, orchestration, and training. It enables data scientists and AI teams to seamlessly deploy, manage, and scale their models on any infrastructure—local, hybrid, or multi-cloud—without deep engineering expertise. The platform handles containerization, API creation, and auto-scaling, accelerating the path from development to production for various AI applications, including Generative AI and Computer Vision.

A human-centric Python framework, originally from Netflix, for building and managing real-life data science, ML, and AI projects. It simplifies workflow orchestration, data management, and model deployment, enabling rapid prototyping and scalable production pipelines.

Dagworks provides a suite of open-source developer tools, Hamilton and Burr, designed to build, debug, and observe reliable AI applications. Hamilton standardizes ML and data pipelines for faster iteration and clear lineage, while Burr simplifies the creation of complex, stateful RAG and agentic systems with built-in observability.

Remyx is an ExperimentOps platform designed for AI development. It helps AI and product teams operationalize knowledge by providing a collaborative studio for structured, reusable, and traceable experiments. By focusing on custom metrics and guided learning loops, Remyx accelerates the AI development lifecycle, ensuring that AI systems are aligned with real-world business goals and user impact.

dstack is an open-source container orchestrator designed for AI and ML teams. It simplifies workload orchestration and maximizes GPU utilization across any cloud provider, on-premise cluster, or accelerated hardware. It provides a unified compute layer, streamlining development, training, and model deployment.

UbiOps is a powerful MLOps platform for AI model serving, orchestration, and training. It enables data scientists and AI teams to seamlessly deploy, manage, and scale their models on any infrastructure—local, hybrid, or multi-cloud—without deep engineering expertise. The platform handles containerization, API creation, and auto-scaling, accelerating the path from development to production for various AI applications, including Generative AI and Computer Vision.

OctoAI is a high-performance compute platform for developers to run, tune, and scale generative AI models efficiently. It offers optimized, production-ready API endpoints for popular open-source models like Llama, Mixtral, and Stable Diffusion. By focusing on deep system optimizations, OctoAI provides faster inference speeds and lower costs, enabling businesses to build and deploy scalable AI applications without managing complex infrastructure.

Replicate is a cloud platform for developers to run, fine-tune, and deploy AI models via a simple API. It eliminates the need for managing complex infrastructure, offering access to thousands of models with pay-per-use pricing and automatic scaling.

Modal is a high-performance, serverless infrastructure platform for AI and ML developers. It allows you to run Python functions in the cloud with a single line of code, providing instant access to GPUs, automatic scaling from zero to thousands of containers, and pay-per-second pricing. Eliminate infrastructure overhead and focus on building and deploying compute-intensive applications like generative AI, batch processing, and data analysis.

Baseten is a production-grade inference platform for deploying, scaling, and managing AI models. It offers high-performance runtimes, seamless developer workflows, and flexible deployment options (cloud, self-hosted, hybrid). Ideal for engineering and ML teams building mission-critical AI applications.

MLflow is an open-source platform for managing the end-to-end machine learning lifecycle. It enables developers and data scientists to track experiments, package code into reproducible runs, version and share models, and deploy them to production, supporting both traditional ML and modern GenAI applications.

Orq.ai is an end-to-end Generative AI Collaboration Platform designed for software teams to scale LLM applications from prototype to production. It provides tools for experimentation, deployment, and observability, enabling teams to build, monitor, and optimize agentic AI systems with confidence and control.

Beam is a serverless cloud platform designed for developers to run, scale, and deploy AI/ML models and applications on GPUs with ease. It offers instant autoscaling, pay-per-second billing, and a streamlined workflow, allowing you to go from code to a scalable API in minutes without managing complex infrastructure.

Cerebrium is a serverless AI infrastructure platform designed for developers to deploy, manage, and scale machine learning models with ease. It abstracts away complex infrastructure, offering features like auto-scaling, fast cold starts, and pay-per-use GPU access, enabling teams to build high-performance AI applications without managing servers.

DataChain is a developer-first platform for managing "Heavy Data"—large-scale, unstructured, multimodal datasets. It enables teams to curate, enrich, and version data like videos, images, audio, and PDFs for AI applications, featuring Python-based ETL pipelines, full data lineage, and scalable processing from local IDE to cloud.

An integrated platform for AI research and development, providing a unified workspace, pre-trained models, and one-click deployment to accelerate the entire AI lifecycle. Ideal for developers, researchers, and enterprises.

A human-centric Python framework, originally from Netflix, for building and managing real-life data science, ML, and AI projects. It simplifies workflow orchestration, data management, and model deployment, enabling rapid prototyping and scalable production pipelines.

Dagworks provides a suite of open-source developer tools, Hamilton and Burr, designed to build, debug, and observe reliable AI applications. Hamilton standardizes ML and data pipelines for faster iteration and clear lineage, while Burr simplifies the creation of complex, stateful RAG and agentic systems with built-in observability.