Unionai

What is Unionai?

Unionai, specifically Union Cloud, is a platform from Union.ai for orchestrating AI, data, and analytics workflows. It uses a common architectural pattern that reduces repetitive code and can cut total pipeline costs by up to 90%. The platform is built to scale, optimized for resources, and works with hybrid and multicloud data environments, offering features like caching and task-aware resourcing for efficient resource use.

Who created Unionai?

UnionAI was founded in 2021 by the engineers who originally created Flyte. The team had prior experience building AI platforms for companies including Lyft, Google, Facebook, and Paypal. The company secured $10 million in seed funding, and later announced a $19.1 million Series A round. Key figures involved include Scott Sandell, CEO & Chief Investment Officer at NEA, and Manish Patel, Founder at Nava Ventures.

What is Unionai used for?

Unionai supports data and AI work in several ways:

• Managed Services — no infrastructure to maintain yourself
• Pipeline cost reduction of up to 90%
• Efficient workflows with caching
• Task-aware resourcing for smart resource allocation
• Hybrid and multicloud ecosystem support
• Reduced compute costs and time to value
• Protection from low-quality data that could affect your models
• Hassle-free ML microservices deployment
• Rapid development and deployment
• Infinitely scalable workflow platform

It is already used by Fortune 100 companies.

Who is Unionai for?

Unionai suits professionals working with data and AI, including data scientists, engineers, bioinformaticians, software developers, financial analysts, AI engineers, and machine learning engineers.

Getting started

1. Sign up with your GitHub account to get instant access to run workflows at cloud scale, with $30 in free compute credit.
2. Review pricing for CPU cores, GPUs (such as T4 and A100), and memory, billed per hour, to manage your budget.
3. Use Union Cloud, the platform’s orchestrated workflow tool, to simplify AI, data, and analytics tasks.
4. Optimize resources with caching and task-aware resourcing to streamline workflows and allocate resources efficiently.
5. Leverage hybrid and multicloud support to develop and deploy AI and analytics at any scale.