Corporate spend management platform Ramp has formally entered into a Strategic Collaboration Agreement (SCA) with Amazon Web Services (AWS) alongside its debut on the AWS Marketplace. The alliance establishes a streamlined path for AWS enterprise customers to modernize, automate, and centralize back-office financial infrastructure using Ramp’s artificial intelligence engine.
By making Ramp accessible within the AWS Marketplace, joint clients can acquire and deploy Ramp’s financial subitem which includes corporate cards, expense management tools, accounts payable, procurement workflows, corporate travel booking, banking rails, automated bookkeeping, and AI controls directly against their pre-committed AWS Cloud spend.
Accelerating Enterprise Financial Automation in the Agentic Era
Licensed through Ramp, this agreement gives autonomous AI agents to embed directly into a company’s operations on a daily basis. In an effort to get rid of manual data entry, streamline approval processes, and manage budgets real-time through corporate accounting departments – the ramp team have embedded the autonomous AI agents into their daily work processes.
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Working continuously on the Ramp platform, these systems code the invoices, apply rule sets, and handle the vendor invoices automatically. This 24/7 support gives them not only continuous execution but also ensures that administrative governance rules are not compromised. All moves made by the autonomous agents can be recorded, undone, and restricted only by strict enforceable guidelines.
Among other key elements of the deal is:
AWS Marketplace procurement integration: companies are allowed to use the AWS Committed Spend agreements as a source to finance Ramp, which eliminates the hurdles of vendor onboarding from the sides of the procurement and IT teams.
Back-office automation on a continual basis: the deployment of AI agents handles tasks like expense classification, invoice matching, and policy compliance, working all day and night.
Ramp Stack for Accounting Firms: is an AI agent-enabled workflow environment meant to be tailored to the workflow way of the accounting firm. The tool allows firms to complete their month-end closing much quicker while providing the necessary documentation and audit logs of their activities.
Ramp AI Token Spend Management: designed to help finance and engineering teams gain a clear understanding and control of Large Language Model (LLM) and AI API usage, it is a monitoring structure of cost. It helps prevent unexpected surging costs through cost overruns.
“Enterprise finance teams are ready for AI to take manual work out of the back office, but they need a practical path to adoption,” said Guy Cartwright, VP, Channel Sales at Ramp. “This collaboration helps customers bring more automation and control into finance operations without adding friction for their procurement or IT teams.”
Addressing the Cost Dynamics of Modern AI Workloads
A key driver of the partnership is the rapid rise of enterprise AI investments, which have introduced new operational line items into corporate balance sheets. By combining cloud procurement capabilities with granular API cost tracking, Ramp and AWS provide financial leaders with direct visibility into variable AI infrastructure expenses.
The inclusion of Ramp AI Token Spend Management directly addresses the challenge of metered LLM costs, allowing cross-functional engineering and finance teams to govern AI usage within existing operational budgets.
Market Impact and Availability
Ramp’s platform availability on the AWS Marketplace is effective immediately, offering turnkey deployment options for enterprise cloud buyers globally.
Today, Ramp serves as the primary financial technology layer for more than 70,000 organizations ranging from small businesses to Fortune 100 corporations and powers over $200 billion in cumulative annualized purchase volume. Operational data shows that median enterprise customers achieve approximately 5% direct savings on overall expense allocations and realize a 16% revenue growth rate during their first year of platform deployment.
