Ramp's New Tool Lets Developers Switch Between AI Models Automatically

Ramp launched Router on the evening of August 20, 2026, a service that lets developers use and switch between different AI models from multiple companies through a single connection point. The service is available at router.com, a web address Ramp acquired for the launch. Router is currently available only in the United States.

Normally, if a developer wants to use AI models from different companies — say, OpenAI, Anthropic, or others — they need to set up separate connections to each one. Router gives them a single connection point instead, as Ramp described in its launch announcement. When a request comes in, Router decides which AI model should handle it, based on two things: cost and quality. Every request goes to the cheapest approved model that still meets the user's quality standard, and the selection happens automatically. Ramp's product page confirms this approach.

The service works with models from OpenAI, Anthropic, DeepSeek, Moonshot, Minimax, Nvidia, xAI, and Z.ai. TechCrunch reported the full provider list in its August 20 coverage.

Router offers several ways to control how requests are routed. Users can take advantage of discounted pricing tiers from model providers, route based on up to three performance benchmarks they choose, send only difficult problems to more expensive models, and test out different models without changing their live setup. A dashboard shows how much they are spending, how fast models respond, and other details.

Behind the scenes, Router uses a statistical technique called Thompson sampling to pick the fastest and most reliable model for each request, as detailed in a Ramp engineering post published July 20, 2026. Thompson sampling is a method that learns from experience — it tries different options, observes the results, and gradually gets better at choosing. The company says it has been using the router for its own AI work for three years before offering it as a product.

Router is free to use for the rest of 2026, though users still pay for the actual AI model usage — the compute charges from whichever model handles a given request. The launch includes a $26 credit offer.

On the data side, Router keeps records by default. It stores model inputs, outputs, and tool calls for one year unless the user opts out. Ramp says it will remove personal information before using stored content to improve the product. For teams with strict data rules, this default matters: the service keeps your data unless you tell it not to, rather than deleting it right away.

Router enters a category that already has established players. Ramp itself published a comparison with OpenRouter, describing its core features as AI model routing, combining multiple APIs, and cost optimization. OpenRouter has built a reputation as a neutral middleman across model providers. Ramp is now competing on the same front, setting itself apart with its Thompson-sampling-based routing and its spending dashboard. Whether that technical advantage wins users away from an established competitor is a different question from whether the routing approach itself works well.

The broader context here is that model routing is shifting from a convenience to core infrastructure. As companies spend more on AI, the difference between always using the most powerful model and routing each request to the cheapest model that gets the job done adds up fast. Ramp's dashboard, which shows exactly how much each request costs, speaks to a concern that both finance and engineering teams share. That Ramp is a corporate-spend company entering the AI-infrastructure market is not a coincidence — the product's focus on cost visibility fits the company's background.

Ramp raised $750 million at a $44 billion valuation in June 2026, per TechCrunch. That funding gave Ramp the resources to buy a premium web address like router.com and to offer the service for free through the end of the year.

For teams that already use multiple AI models through custom code or another routing service, Ramp's offering makes it easier to try a different approach. The Thompson-sampling method addresses a real problem: some models are cheap but respond unpredictably slowly, which can break applications that need consistent response times. A routing system that constantly learns and adjusts is a different proposition from simple rules based on cost alone.

The one-year data retention default and the U.S.-only availability are the immediate limitations. Teams with strict data-location requirements or operations in Europe will need to wait. For U.S.-based developers considering whether to combine their model connections behind a single endpoint, Router is available now.