Ramp Router claims to cut AI costs by up to 30%

Reddit r/ArtificialInteligence Tools

Summary

Ramp is open-sourcing its internal LLM router that automatically selects the best model for each request to optimize cost and performance.

Ramp has been using an internal LLM router for a few years and they're now opening it up publicly. The pitch is basically one OpenAI-compatible endpoint that automatically picks the best model for each request as pricing and capabilities change between GPT, Claude, Gemini, Grok, Qwen, DeepSeek, etc. Maybe I'm missing something, but this feels like it could save a lot of engineering time if it actually works well. Has anyone here looked into how they're deciding which model gets each request? Is it mainly cost optimization, latency, quality benchmarks, or something more dynamic? Curious whether people think this is the direction AI infrastructure is heading or if most companies will still want to manage model selection themselves.
Original Article

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Ramp has launched Router, an AI model routing service that enables companies to access and switch between various large language models via an API, with features for cost optimization and benchmark-based routing.

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Router by Ramp is a new product designed to save money on AI token usage by helping users manage and reduce API-related costs.

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Router by Ramp is a tool that reduces AI inference costs by up to 40% by routing requests to the lowest-cost model that meets performance needs, offering a single endpoint for multiple models.