- Efficient Model Access: Ramp has introduced Router.com to provide a unified API endpoint for switching between multiple large language models based on performance and budget.
- Significant Savings: Early adopters report a 40% average reduction in AI inference costs through automated routing and optimized service tiers.
Unified AI Infrastructure via Router.com
Ramp officially entered the AI infrastructure market on August 19, 2026, with the release of Router.com. This unified AI model routing service is designed to cut companies’ rising AI bills by providing a single API endpoint. Through this interface, developers can access and switch between various large language models (LLMs) dynamically, prioritizing either cost efficiency or high performance depending on the specific requirements of the task. This release follows a pattern of tools designed to cut enterprise costs across the sector.
The technology behind the router was developed internally at Ramp over a period of three years. Before its public debut, the system was heavily utilized within the company, processing more than 2.75 trillion tokens per month. This internal testing ensured the platform could handle massive scale and high-intensity production environments. The service currently supports models from a wide array of providers, including OpenAI, Anthropic, SpaceXAI, xAI (Grok), DeepSeek, and Nvidia. Ramp has also indicated that support for Google Gemini is expected to arrive in the near future.
Advanced Routing and Cost Optimization
One of the primary features of the platform is the Flex tier. This feature automatically routes requests to discounted service tiers when their real-time latency matches that of standard tiers, allowing companies to maintain performance while lowering expenses. Such innovations in online learning for cost-efficient LLM routing have contributed to an average reduction in inference costs of 40% for early users. To further incentivize adoption, the routing service is free through the end of 2026 and includes a $26 launch credit for new accounts.
The platform is built with over 100 distinct optimizations. These include caching, compression, and dynamic fallback mechanisms. These features are designed to ensure 99.9%+ reliability, which is a requirement for enterprises integrating a native security LLM or other mission-critical agents. Developers can also utilize Shadow models to test new AI models against real production traffic. This allows for performance comparisons without impacting the end-user experience, making it easier to evaluate if a stealth AI model or a newer release provides a tangible benefit over existing deployments.
Performance Benchmarking and Market Availability
To assist developers in making informed decisions about model selection, Ramp utilizes a proprietary benchmark known as Ramp SWE-Bench. This evaluation tool is derived from real production tasks rather than synthetic datasets, offering a more accurate representation of how a model will perform in professional applications. This focus on practical performance comes at a time when the AI gateway market is shifting rapidly, evidenced by Stripe’s recent $7.5 billion acquisition of OpenRouter.
Currently, availability for Router.com is limited to customers located in the United States. Regarding data handling, the service follows an opt-out policy where model inputs, outputs, and tool calls are retained for one year by default. This data retention policy is a standard practice for developers managing sensitive enterprise information within AI workflows.
