Union.ai Secures $38.1M to Build Next-Gen AI Development Infrastructure

In a significant boost to the AI engineering ecosystem, Bellevue, Washington-based startup Union.ai has closed a $38.1 million Series A funding round, marking a major milestone in its mission to revolutionize how companies build and deploy AI systems at scale.

The funding round was led by NEA (New Enterprise Associates), with participation from existing investor Nava Ventures and new investor Mozilla Ventures. Notably, this total includes a previously announced $19.1 million portion raised in 2023, bringing Union.ai’s total Series A funding to $38.1 million.

From Open-Source Roots to Commercial Powerhouse

Union.ai stands at the forefront of what it calls “AI development infrastructure,” positioning itself as a comprehensive solution for engineering teams grappling with the complexities of modern AI workflows. At the heart of this ecosystem lies Flyte, an open-source orchestration tool that has become instrumental in running complex machine learning and data workflows across thousands of organizations.

The company’s evolution from its open-source origins to a full-fledged commercial entity represents a broader trend in the AI infrastructure space. As AI systems grow increasingly sophisticated and mission-critical, the tools that support their development and deployment have become just as vital as the models themselves.

Why Traditional Infrastructure Falls Short

Union.ai’s CEO and co-founder, Ketan Umare, emphasizes a fundamental shift in how AI development differs from traditional software engineering. “Building AI requires a fundamentally different approach than traditional software, and engineering teams are now embracing that,” Umare stated, highlighting the core challenge that Union.ai aims to solve.

The distinction lies in the nature of AI workflows themselves. Unlike traditional deterministic software processes that follow predictable paths, AI workflows are inherently non-deterministic. They involve agents that must adapt, learn, and recover from failures dynamically during runtime. This fundamental difference renders much of the existing software infrastructure inadequate for modern AI development needs.

The AI Development Infrastructure Revolution

Union.ai is pioneering what it describes as an entirely new category of infrastructure specifically designed for AI development. The company’s vision extends far beyond simple workflow orchestration to encompass the entire AI development lifecycle, including training, inference, and observability.

In a detailed LinkedIn post, the company elaborated on this vision: “This funding comes at an inflection point for AI: engineering teams are discovering that legacy software infrastructure and devtools struggle to handle AI development. They were designed for basic and deterministic processes of traditional data workflows, not for the non-deterministic processes of AI workflows, which expect agents to adapt and recover from failure at runtime.”

The company’s approach enables engineering teams to develop dynamic, durable AI workflows and agents while dramatically reducing the time spent maintaining brittle pipelines. This represents a paradigm shift from the traditional approach of building static, failure-prone systems to creating resilient, adaptive AI infrastructure.

Impressive Growth Metrics Signal Market Validation

The market appears to be validating Union.ai’s approach with impressive growth metrics. The startup reports that its revenue grew 3X in 2025, while its customer base expanded 2.6X during the same period. These figures suggest strong product-market fit and growing demand for sophisticated AI development tools.

Union.ai’s customer roster includes notable names such as Spotify, HederaDx, Carfax, and Hopper, demonstrating the broad applicability of its platform across different industries and use cases. From music streaming services to automotive history providers and travel booking platforms, these customers represent diverse sectors united by their need for robust AI infrastructure.

Union 2.0 and Flyte 2: The Next Generation

The fresh capital injection will fuel the commercial launch of Union 2.0 and continued development of Flyte 2, the next iterations of the company’s core products. These updates promise several groundbreaking features that address critical pain points in AI development:

Pure Python Authoring: Simplifying the process of creating complex workflows by allowing developers to write them directly in Python, reducing the learning curve and accelerating development.

Improved Debugging: Enhanced tools for identifying and resolving issues in AI workflows, crucial for maintaining reliability in production environments.

Runtime Decision-Making: The ability for workflows to make intelligent decisions during execution, enabling more sophisticated and adaptive AI systems.

Crash-Resilient Workflows: Building fault tolerance directly into the infrastructure, ensuring that AI systems can recover gracefully from failures without manual intervention.

The Man Behind the Mission

Ketan Umare’s journey to founding Union.ai reflects the deep technical expertise driving the company’s success. He helped develop the underlying technology for Flyte while working as an engineer at Lyft, gaining firsthand experience with the challenges of scaling machine learning operations in production environments.

Prior to Lyft, Umare’s experience at Amazon and Oracle provided him with a strong foundation in building scalable systems and enterprise software. In 2020, he co-founded Union.ai with Haytham Abuelfutuh, combining their complementary expertise to tackle the AI infrastructure challenge.

Riding the AI Infrastructure Wave

Union.ai’s success comes amid a broader surge in investment and innovation in AI infrastructure. The timing is particularly noteworthy given the recent announcement by Temporal, another Seattle-region “durable execution” company, which secured $300 million in funding, reaching a $5 billion valuation.

This wave of investment in infrastructure companies reflects a maturing AI ecosystem where the focus is shifting from pure model development to the practical challenges of deploying and maintaining AI systems in production. As companies move beyond AI experimentation to real-world deployment, the demand for robust, scalable infrastructure has become paramount.

Building the Future of AI Development

With over 40 employees and active hiring initiatives, Union.ai is positioning itself as a key player in shaping how AI systems are built and deployed. The company’s approach addresses a critical gap in the current AI landscape: while significant attention and resources have been devoted to developing increasingly sophisticated AI models, comparatively less focus has been placed on the infrastructure that enables these models to function reliably at scale.

Union.ai’s vision extends beyond simply providing tools to encompassing the creation of an entire ecosystem that supports the unique requirements of AI development. This includes handling the inherent uncertainty and variability of AI systems, providing the observability needed to understand complex model behavior, and ensuring the reliability required for mission-critical applications.

The Road Ahead

As AI continues to transform industries and redefine what’s possible with technology, the infrastructure supporting these systems will play an increasingly crucial role. Union.ai’s successful funding round and impressive growth metrics suggest that the market recognizes this reality and is willing to invest in solutions that address the unique challenges of AI development.

The company’s focus on creating resilient, adaptive infrastructure for non-deterministic workflows positions it at the intersection of several critical trends in technology: the rise of AI, the need for reliable production systems, and the growing complexity of software development. As organizations across industries continue to integrate AI into their operations, the tools and infrastructure provided by companies like Union.ai will become essential components of the modern technology stack.

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