Union.ai is a managed workflow orchestration platform built on top of the open-source Flyte project. It is designed to help data science, machine learning, and data engineering teams build, run, and scale AI and ML pipelines in production environments. The platform provides infrastructure abstractions that allow practitioners to focus on writing Python-based tasks and workflows without managing the underlying Kubernetes or cloud infrastructure. Union.ai supports multi-cloud deployments and offers features such as workflow versioning, caching, lineage tracking, resource management, and observability. It targets organizations that need reproducible, scalable, and auditable ML pipelines. The platform is available as a fully managed cloud service (Union Cloud) as well as a self-hosted option (Union BYOC — Bring Your Own Cloud), giving teams flexibility in how they deploy and manage their infrastructure. Union.ai also offers a free tier aimed at individual practitioners and small teams exploring ML orchestration.
Target audience and deployment
- Solo / Freelancer
- Startup
- SMB
- Mid-market
- Enterprise
- Cloud
- Self-hosted
- API
Key features
Use cases
- Orchestrate machine learning training pipelines
- Deploy and serve AI model inference workflows
- Automate data processing and feature engineering
- Manage and track ML experiment lineage
- Scale compute resources dynamically for AI workloads
- Collaborate on shared ML workflow definitions
Best for
- ML Engineers who need to orchestrate and scale production machine learning pipelines
- Data Scientists who need reproducible, versioned workflow execution without managing infrastructure
- Data Engineers who need to automate and monitor large-scale data processing jobs
- Platform Teams who need to provide a managed ML infrastructure layer to internal stakeholders
Integrations
Developer
GitHub, Docker, Kubernetes
AI models included
PyTorch, TensorFlow, Hugging Face
Databases
AWS S3, Google Cloud Storage, Azure Blob Storage, Snowflake
Other
AWS, Google Cloud, Azure