Ramp Launches AI Model Router After 3 Years of Internal Use, Cuts Costs by 40%

Ramp Enters AI Inference Market With Router Launch

Corporate expense management platform Ramp launched Router on Wednesday evening, an AI model router that enables users and companies to access multiple large language models through a single API1

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. The service represents Ramp's entry into the fast-growing AI inference market, where it now competes directly with established players like OpenRouter2

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Router has been quietly powering Ramp's internal AI workloads for approximately three years before its public debut2

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. During this period, the tool cut Ramp's own AI costs by about 30%, while early customers are reporting average cost reductions of 40%3

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. The service is currently available only in the United States and will remain free to use through the remainder of 2026, though users must still cover AI model inference costs1

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. Ramp is offering a $26 credit as part of the launch promotion but has not disclosed pricing for 20272

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How Router Works as an AI Traffic Controller

Router functions as an AI traffic controller by sitting between applications and AI providers, intelligently directing each request to the most cost-effective model capable of handling the task3

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. Instead of locking companies into a single model, Router evaluates every request and routes it to the cheapest model that can still deliver quality results3

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The service provides access to models from OpenAI, Anthropic, DeepSeek, Moonshot, Minimax, Nvidia, xAI, and Z.ai1

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. Router offers several routing strategies to help customers optimize their AI usage based on specific preferences1

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. One strategy allows users to set preferences for model providers' flex usage tiers, while another enables Router to select models based on up to three user-specified benchmarks1

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. The system can route complex problems to expensive models while handling simpler tasks with cheaper alternatives, and it automatically switches to backup models if a provider experiences downtime2

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Cost Optimization Through Intelligent Model Selection

Ramp built its own testing system using real engineering work at the company because public rankings of AI models didn't answer the specific cost questions Ramp needed addressed3

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. This approach mirrors the discipline companies already use to control cloud-computing costs, deciding in real time whether a task requires an expensive tool or a cheaper alternative3

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The dashboard provides comprehensive visibility into usage and performance metrics, including token spend, cost, latency, fallback attempts, and other critical details1

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. This transparency allows businesses to track token consumption, costs, response times, and fallback behavior across their AI operations2

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. The service sits inside the same platform companies already use to track software and employee expenses, extending that visibility to AI spending3

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Strategic Positioning Against Stripe and OpenRouter

Ramp's Router launch comes shortly after Stripe agreed to acquire OpenRouter, an independent startup, in a deal reportedly worth at least $7.5 billion3

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. While Router resembles OpenRouter in function, Ramp currently supports fewer models than OpenRouter's marketplace of more than 400 models from over 80 providers2

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. OpenRouter processes more than 10 trillion tokens daily and serves more than 10 million developers3

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Both Stripe and Ramp are building cost-control layers for AI, betting that as businesses spread their AI work across many models instead of picking just one, the more valuable business won't be building the smartest model but deciding which model gets selected next3

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. For Ramp, entering the model routing business offers a dual opportunity: tapping the booming AI inference market while offering existing clients a service that integrates seamlessly with its AI token usage monitoring and token spend management products1

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Data Retention and Future Growth Implications

Router implements an opt-out data retention policy that records model inputs, outputs, and tool calls for one year by default1

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. Ramp states it will remove personally identifiable information before using that content to improve the product1

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If Router proves as attractive of a model testing arena as OpenRouter has become, Ramp may build long-standing relationships with AI labs and inference providers worldwide1

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. This could help Ramp, which raised $750 million at a $44 billion valuation in June, gain new customers and create a new entry point for selling its expense management products1

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. The company stated that the new capital would be used to further expand its AI-focused product development for customers2

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. As companies process AI requests across millions of queries, managing inference costs becomes increasingly critical, positioning Router to address a growing market need3

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