Union.ai Raises $38.1M for AI Workflow Infrastructure to Bridge Pilot-Production Gap
Union.ai closed a $38.1 million Series A round led by NEA, with participation from Nava Ventures and new investor Mozilla Ventures, targeting the pilot-to-production infrastructure gap that has become a critical bottleneck for enterprise AI deployment.
This funding addresses a fundamental architectural challenge: legacy software infrastructure designed for deterministic processes struggles with AI workflows that adapt and make runtime decisions. Union’s orchestration platform, built around the open-source Flyte framework with 80+ million downloads, enables dynamic AI workflows that can recover from failures and make decisions at runtime—exactly what autonomous agents require for production deployment.
The Deterministic vs. Non-Deterministic Infrastructure Gap
Traditional workflow orchestration tools assume predictable, linear processes where each step follows a predetermined path. AI agents and modern ML workflows operate differently—they branch, adapt, and make decisions based on data and context encountered during execution.
“Legacy software infrastructure and devtools struggle to handle AI development,” said CEO Ketan Umare, who previously developed Flyte’s underlying technology at Lyft. “These workflow tools were designed for basic and deterministic processes of traditional data workflows, not for the non-deterministic processes of AI workflows.”
This infrastructure mismatch creates brittleness in production AI systems. When workflows fail, traditional systems require complete restarts. When AI agents need to adapt their execution path based on runtime conditions, legacy orchestrators can’t support dynamic branching and looping.
Crash-Resilient Architecture for Agentic Systems
Union’s platform solves this with several key architectural innovations that align with agentic AI requirements:
Pure Python Authoring: Enables teams to write, test, and version workflows locally in pure Python, then scale them in cloud environments without infrastructure complexity.
Dynamic Runtime Decision-Making: Supports fully dynamic workflows for agentic applications, enabling autonomous agents to modify their execution paths based on real-time conditions while maintaining workflow integrity.
Crash-Resilient Execution: Workflows resume from failure points rather than restarting completely, with built-in caching and automatic retry logic—critical for long-running agent tasks.
Infrastructure-Aware Orchestration: Automatically provisions compute resources only when needed, enabling cost-efficient scaling for intermittent agent workloads.
Enterprise Adoption Evidence
Union’s customer base expanded 2.6X in 2025 while revenue grew 3X, indicating strong enterprise demand for AI workflow infrastructure. The platform serves 3,500+ companies including Spotify (audio processing), Carfax (data pipelines), and Hopper (travel optimization).
The company’s open-source foundation provides validation—Flyte’s 80+ million downloads suggest widespread adoption of the orchestration approach, while Union.ai’s commercial platform adds enterprise-grade governance, observability, and infrastructure management.
Mozilla Ventures’ participation signals broader ecosystem support. “Mozilla’s open-source AI strategy is rooted in the belief that openness, choice, and developer agency are essential as we define the new AI tech stack,” said Managing Partner Mohamed Nanabhay.
Infrastructure Consolidation Trend
Union.ai’s Series A follows a pattern of infrastructure consolidation in the AI deployment space. Companies like Temporal ($5B valuation) focus on durable execution, while others tackle governance (Portkey), security (Resemble AI), or context management (Potpie AI).
The common theme: enterprise teams need specialized infrastructure to move AI systems from experimentation to reliable production deployment. Generic cloud services and traditional DevOps tools weren’t designed for the unique requirements of agentic systems.
Looking Forward: Workflow Infrastructure as AI Standard
Over the next 12-18 months, expect AI workflow orchestration to become standard infrastructure, similar to how Kubernetes became the default for container orchestration. As autonomous agents become more sophisticated, the infrastructure supporting their execution needs to match that complexity.
Union.ai’s focus on crash-resilient, dynamic workflows addresses a fundamental scaling challenge: AI agents need infrastructure that can adapt, recover, and make decisions—not just execute predetermined sequences. With 40+ employees and active hiring, the company is positioning to capture this emerging infrastructure requirement.
The shift toward agentic AI systems creates new infrastructure requirements that traditional workflow tools can’t meet. While Union.ai tackles orchestration and execution resilience, platforms like Overclock address the complementary challenge of making AI agent capabilities accessible through natural language interfaces, bridging the gap between technical infrastructure and business automation.