Ramp Cuts AI Costs by Over 25% With Smart LLM Routing System That Adapts in Real Time

Ramp cuts AI costs by over 25% using a real-time LLM routing system powered by Thompson Sampling and adaptive algorithms that intelligently shift traffic between models based on live latency, failure rates, and cost — with an additional 30% savings achieved on its largest streaming workload.

• Ramp has developed a dynamic LLM routing strategy using online learning, specifically Thompson Sampling and exponentially-weighted moving averages, to intelligently route AI requests across models and service tiers based on real-time latency and failure data.
• The routing system scores each model option by combining failure probability, latency deadline risk, and relative cost, then reorders routing preferences accordingly, allowing it to automatically shift traffic away from degraded providers and toward cheaper alternatives when performance is equivalent.
• After deploying the strategy, Ramp achieved over 25% cost savings with a simultaneous reduction in error rate, and has since expanded the approach to streaming use cases, yielding an additional 30% savings on its largest streaming workload.