Pay-i

# Pay-i: High-Level Overview

Pay-i is a financial operations (FinOps) platform designed to help enterprises understand and optimize the economics of generative AI deployments.[1] Founded in 2023 and based in Redmond, Washington, the company addresses a critical gap in enterprise AI adoption: the difficulty of connecting AI model usage to concrete business outcomes and financial performance.[5] Pay-i serves technology companies and enterprises deploying generative AI applications by providing real-time cost allocation, budget control, and analytics that translate AI interactions into measurable unit economics and ROI.[1][5]

The core problem Pay-i solves is fundamental to modern AI operations. As enterprises race to deploy GenAI for productivity gains and new revenue streams, most lack visibility into the true financial footprint of their AI workloads.[5] Pay-i's platform enables organizations to track the amount of revenue that an AI service generates, the cost of operating it, and related metrics—down to the token level—while forecasting future financial performance and identifying optimization opportunities before deployment.[2]

# Origin Story

Pay-i was founded in 2023 by David Tepper, Erik Winters, and Doron Holan, each bringing deep expertise from enterprise technology backgrounds.[2] Tepper, the CEO, previously served as a principal project management architect at Microsoft's Azure business, positioning him to understand cloud infrastructure and cost management at scale. Winters, the COO, is a long-time startup executive, while Holan, the CTO, was a former Windows architect—a combination of cloud, operations, and systems expertise that directly informed the company's focus on AI cost observability.[2]

The company launched publicly in May 2025 with $4.9 million in seed funding led by Fuse Partners and Tola Capital, with participation from Firestreak, Pear VC, Gaia Capital, and multiple angel investors.[2] The timing of the launch reflected a market inflection point: as large language models became increasingly expensive to operate and enterprises struggled to forecast AI workload costs, the need for specialized financial tooling became acute.

# Core Differentiators

Pay-i's competitive advantages center on purpose-built design for generative AI economics:

• Token-level cost tracking: The platform provides granular visibility into AI expenses across models, providers, and modalities—from individual token consumption to entire agentic workflows—with patent-pending precision that enables cost control down to the penny.[6]

• Pre-launch forecasting: Developers can forecast inference costs and model economics before deployment, enabling them to switch to lower-cost models or adjust pricing strategies before launch to avoid budget overruns.[2]

• Business outcome alignment: Unlike generic cloud cost tools, Pay-i connects GenAI performance metrics directly to core business KPIs—lead conversions, revenue per session, churn rate—to prove that every AI dollar delivers measurable value.[6]

• Observability and troubleshooting: The platform functions as an enterprise-grade observability tool, identifying which prompts or AI agent steps caused malfunctions and tracking error rates, hallucinations, and cost-to-fail metrics.[2][6]

• A/B testing for AI configurations: Pay-i's unique functionality highlights optimal tradeoffs between cost, latency, and output quality, allowing teams to make data-driven decisions about model selection and configuration.[6]

• Cross-functional unification: The platform serves as a single source of truth for finance, product, and engineering teams, breaking down silos through shared dashboards and metrics.[5]

# Role in the Broader Tech Landscape

Pay-i operates at the intersection of two major trends reshaping enterprise technology: the explosive adoption of generative AI and the maturation of FinOps as a discipline.[5] The company is riding the wave of enterprises moving from AI experimentation to production deployment—a phase where cost management and ROI accountability become critical.[2]

The timing is particularly significant because most enterprises lack the financial infrastructure to manage probabilistic, usage-based AI costs at scale.[5] Traditional cloud cost management tools were designed for predictable infrastructure spending, not the variable, model-dependent costs of LLM inference. Pay-i fills this gap as organizations move toward what the company describes as an "agentic, probabilistic future where intelligence is woven into every workflow."[5]

The company's influence extends beyond its direct customers. By making AI economics transparent and measurable, Pay-i enables enterprises to make more rational decisions about AI investment—potentially accelerating adoption where ROI is clear and preventing wasteful spending where it isn't. This contributes to a more sustainable AI ecosystem where innovation is balanced with financial discipline.

# Quick Take & Future Outlook

Pay-i is well-positioned to become the standard financial observability layer for enterprise GenAI operations. As AI spending grows and CFOs demand accountability for AI investments, the company's focus on connecting usage to business outcomes addresses an increasingly urgent need. The founding team's deep roots in cloud infrastructure and systems architecture suggest they understand the technical and organizational challenges enterprises face.

The company's evolution will likely follow the trajectory of other successful FinOps platforms: expanding from cost tracking to become a strategic tool for AI governance, helping enterprises optimize model selection, pricing strategies, and resource allocation. As agentic AI systems become more complex and expensive to operate, the ability to understand and control their financial impact will become table stakes for enterprise adoption.

Pay-i's success ultimately depends on whether enterprises can prove that their GenAI investments deliver measurable business value—and Pay-i is betting that by making that connection visible and quantifiable, it can help them do exactly that.