Ramp launches its own AI model router, called Router
Ramp, the fintech company behind corporate expense management, introduced Router on Wednesday—a new API-based platform enabling customers to seamlessly integrate and switch between large language models from providers including OpenAI, Anthropic, and DeepSeek. The company has employed Router internally for its own AI needs over the past three years and is now opening it to US-based users at no charge through the end of 2026, alongside a $26 launch credit, though 2027 pricing remains unannounced.
Router includes flexible routing strategies to help organisations optimise model selection—users can prioritise lower-cost inference tiers, rely on algorithm-chosen providers based on custom performance metrics, or route computationally demanding tasks exclusively to premium models. The platform provides visibility into spend, latency, and token usage through a dedicated dashboard, alongside a one-year default data retention policy with promised removal of personally identifiable information before using captured data to enhance the product. For Ramp, the move positions itself to capitalise on the expanding AI inference market whilst offering its existing customer base integrated model-switching capabilities within its broader expense-tracking ecosystem.
- Ramp, a corporate expense platform, unveiled Router, a service that routes AI requests across multiple language model providers
- The tool is free through 2026 (plus model inference costs), includes routing strategies for cost/performance optimization, and expands Ramp's AI infrastructure offerings
New here? Start with this
Large language models are AI systems that generate text and help with tasks such as writing, coding and answering questions. They are offered by different providers, and organisations may use several because their costs, speed and performance vary.
A model router is software that directs each request to a chosen AI model. It can let an organisation switch providers or set rules based on cost, speed or the type of task, while tracking usage.
Ramp is a US fintech company best known for tools that help businesses manage expenses. Offering AI infrastructure alongside those services reflects a wider effort by technology companies to make it easier for organisations to use and manage AI systems.
Both sides, in good faith
The strongest fair case each way — we don't pick a winner.
The case for
Enterprises benefit from flexibility and avoiding vendor lock-in, particularly as the LLM market remains competitive and no single provider excels across all use cases. Router enables organisations to route work to the most cost-effective or technically capable model for each application, whilst providing spending visibility that has become essential as AI costs grow. The ability to optimise infrastructure based on custom performance metrics represents a genuine strategic advantage in a rapidly evolving market.
The case against
The operational complexity introduced by multi-model routing outweighs marginal gains from optimisation. Organisations derive greater value from standardised, deep relationships with one or two trusted providers rather than fragmenting infrastructure across many options. Adding another intermediary creates latency, operational overhead, and new security considerations, whilst industry-wide standardisation ultimately benefits customers more than constant provider switching and vendor fragmentation.