Investors Demand Embedded Payments
Venture and growth investors from RMS and UBS are now zeroing in on embedded payments as a key metric for platform valuation. They want to see SaaS companies moving beyond licensing to transaction-based revenue, arguing that platforms controlling their own risk and reconciliation see 2-3x higher attach rates. For founders, a clear payment monetization strategy is no longer optional—it's a requirement for attracting capital.
Vertical SaaS companies are increasingly embedding financial services to break through the growth constraints of subscription-only models. By adding payments, lending, and insurance products, platforms can increase customer revenue by a factor of 2 to 5, turning cost centers into significant profit drivers and expanding their total addressable market. The Payment Facilitator (PayFac) model is the primary mechanism for this shift, allowing platforms to onboard their users as sub-merchants under a master account. While becoming a full PayFac involves significant compliance and risk overhead, the rise of PayFac-as-a-Service (PFaaS) providers allows software companies to white-label payment services, accelerating time-to-market while the partner handles the regulatory heavy lifting. Restaurant platform Toast exemplifies the scale of this strategy, reporting $195.1 billion in Gross Payment Volume for the full year 2025. The company's gross profit from subscription and financial technology solutions grew 33% year-over-year, demonstrating how deeply integrated payments become a core part of the platform's value and revenue. The conversation is now shifting to settlement speed, with real-time payment (RTP) networks like FedNow and The Clearing House enabling instant, 24/7 fund availability. Unlike traditional ACH transfers that rely on batch processing, real-time rails settle transactions individually within seconds, fundamentally changing cash flow for businesses and their sub-merchants. For platforms with global ambitions, managing cross-border payments introduces significant complexity, with businesses losing an average of 3-5% of international revenue to hidden fees and unfavorable exchange rates. Navigating varying regulatory requirements, local payment preferences (like SEPA in Europe), and currency risk is a critical challenge that sophisticated payment infrastructures aim to solve. AI is moving beyond fraud detection to optimize core payment operations, with machine learning models now used for intelligent payment routing. These systems analyze transactions in milliseconds to select the optimal processor based on cost, authorization probability, and latency, directly impacting success rates and operational efficiency.
