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The financing follows a $750 million round four months earlier. Ramp’s company-wide growth supports a widening business, but does not isolate demand for its AI tools.
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Ramp's $1.85 billion Series G, led by Dragoneer alongside four co-leads, gives the company room to extend from corporate finance software into managing businesses' AI spend. Its product bet combines token-use monitoring and optimization with AI automation across procurement, expenses, accounting and budgeting, aiming to oversee the economics of AI use—not just expense controls. The higher valuation accompanies company-reported $2 billion annualized revenue and positive cash flow, but those figures describe Ramp overall and do not isolate demand for its AI products.
The round came four months after Ramp raised $750 million at a $44 billion valuation, and brings its reported total funding to about $5 billion.
Ramp said annualized purchase volume topped $275 billion, up 175% year over year, and its customer count rose to 85,000; neither figure isolates AI-product adoption.
Ramp says most customers use at least two products, while a quarter use three or more.
Ramp now carries a $60 billion valuation, putting a higher price on a business that combines corporate spending software with tools for managing AI costs. The company announced a $1.85 billion Series G on October 15, 2026, four months after raising $750 million at a $44 billion valuation.
Dragoneer led the financing, with Thrive Capital, Founders Fund, Sands Capital and Avenir Growth joining as co-leads. Two dozen other institutional investors also participated, according to SiliconANGLE. The latest round brings Ramp’s total funding to about $5 billion, TechCrunch reported.
Other participants included Fidelity Management & Research Company, T. Rowe Price, GIC, Ontario Teachers’ Pension Plan, Capital International, General Catalyst and Lightspeed Venture Partners. Founders Fund and Khosla Ventures, the two firms through which early investor Keith Rabois has backed Ramp, both participated in this round.
In its own announcement, Ramp said it passed $2 billion in annualized revenue over the summer and was cash-flow positive. That compares with a reported $1.5 billion annualized revenue figure in June. Annualized revenue describes a revenue pace, rather than sales collected over a completed year; positive cash flow is also distinct from a statement of net profit.
The company also said annualized purchase volume exceeded $275 billion, up 175% from a year earlier. That figure measures purchases facilitated through Ramp, not revenue earned by the company. Its customer count reached 85,000, compared with 70,000 in June. Named customers include Visa, Uber, Shopify, Notion, Cursor and Figma.
Those figures describe the whole business. They are not a separate measure of demand for Ramp’s AI products. The company’s adoption numbers offer another view of its reach: Ramp said most customers use at least two products, while a quarter have adopted three or more.
Ramp’s founders also put numbers on the benefits they say customers receive: more than $1 billion saved each month and 1.5 million hours saved in September. Those are company claims about customer savings, separate from Ramp’s own revenue and cash flow. They describe its pitch to businesses: less spending and less administrative work.
AI opens up much more of the work it takes to run a company.
Eric Glyman, Ramp co-founder, quoted by SiliconANGLE
Ramp’s corporate card service lets finance teams set spending limits by supplier and product category. Its expense tools allow employees to check reimbursement policies before buying something. When a reimbursement request arrives, Ramp uses AI to determine whether it should be approved. Other tools identify unnecessary expenses, including unused subscriptions.
The company also uses AI agents to process supplier bids for contracts. TechCrunch described AI agents embedded across procurement, expense management, accounting and budgeting. Alongside that automation, Ramp offers tools for monitoring AI token usage and managing token spending—the usage units behind charges for model services.
That wider product range puts Ramp in competition with Rippling, which has moved from HR and payroll into corporate cards, expense management and bill payments. The two companies now compete for finance-team budgets, TechCrunch reported. Ramp’s AI spending tools extend its ambitions beyond that existing financial software rivalry.
Rabois made that ambition explicit in an interview with TechCrunch last month. He said he wanted Ramp to become more successful and important than Anthropic. He argued that managing customers’ AI spending would bring the companies into competition.
Rabois’s argument is an investor’s forecast, not a demonstrated outcome of the financing. His proposed route is specific: Ramp would become the place where companies manage token spending, optimize it and measure their return on investment. The ambition is to oversee the economics of customers’ AI use, rather than merely approve another software expense.
Story updates
Ramp says most of its customers now use at least two products, while the number generating more than $100,000 in revenue for the company grew 113% year over year. Disclosed in its October 15, 2026 financing announcement, those figures show how far its customer relationships extend beyond a single financial product.
The disclosures accompanied a $1.85 billion financing led by Dragoneer Investment Group at a $60 billion valuation. They add detail about the business behind that price: Ramp says customers increasingly use its platform for several jobs, from paying bills and closing the books to collecting revenue.
As of October 1, Ramp reported more than 85,000 customers and over $275 billion in annualized purchase volume, up 175% from a year earlier. Purchase volume measures spending handled through the platform, rather than Ramp’s own revenue. The company separately said it generated more than $2 billion in annualized revenue with positive cash flow.
Ramp’s announcement connects that broader financial role to its push into AI. Its stated approach joins tools that perform work with controls that show what the work costs. The product lineup spans model selection, spending oversight, shared knowledge and accounts receivable—the process of collecting money customers owe.
AI Token Spend Management gives finance and technical teams one place to understand and control AI spending across providers, teams, models, users and API keys.
Router.com sends model requests through one endpoint and chooses routes based on performance and cost. Ramp says it can cut inference costs—the cost of running models—by up to 40%.
Glass is a collaborative AI workspace that is not tied to one model provider. Ramp describes it as a place where an organization’s knowledge can accumulate.
Ramp Accounts Receivable automates steps between a signed contract and collected cash, including creating invoices, following up on payment, matching payments to invoices and recognizing revenue.
These are existing products highlighted in the financing announcement, not launches established by it. The multi-product figures cover Ramp’s overall customer base; they do not identify which combinations customers use or how much revenue comes from AI tools.
Testimonials published by Ramp offer a more specific view of the AI tools. AngelList controller Greg Cooley said Token Spend Management helped the company cut monthly AI spending by 17.5%. He described the savings as room to invest in more work, rather than a reason to stop expanding AI use.
Serval chief technology officer Alex McLeod described a different mechanism. Router gives its engineers access to models from several providers through one API, or software connection, and automatically optimizes costs. For Serval’s coding agents, he said, that meant cutting inference costs nearly in half, with the savings going back into building.
The examples measure different things: AngelList’s monthly AI bill and Serval’s model-running costs for coding agents. They are customer accounts selected and published by Ramp, not evidence of typical savings across its customer base. Neither supplies a measure of Ramp’s company-wide AI revenue.
Across its broader platform, Ramp says median customer savings reached 7.5%, compared with 5% a year earlier, and transactions required 22% less manual work. Its co-founders’ company update also put September time savings at 1.5 million hours. Those are company-supplied measures of financial operations overall, separate from the two AI customer testimonials.
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