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Online Learning for Cost-Efficient LLM Routing
Ramp’s router uses Thompson sampling to balance model costs and latency, achieving a 30% reduction in AI inference expenses.
What: Ramp’s routing system employs Exponentially Weighted Moving Averages (EWMA) to track provider failure rates and Thompson sampling to estimate latency distributions, allowing it to select the most cost-effective model tier for specific deadlines.
Why it matters: This indicates that managing AI costs now requires sophisticated, state-aware routing logic rather than simply relying on static model selection as inference providers become more varied in performance and pricing.
Decoder
- Thompson sampling: A heuristic for multi-armed bandit problems that balances exploring unknown strategies and exploiting known high-performing ones by sampling from the probability distribution of each option's success.
- EWMA: A statistical tool that applies weighting factors which decrease exponentially over time, giving more importance to recent observations than older ones.
Original article
Ramp Router learns provider failure rates through EWMA and latency distributions through Thompson sampling, then chooses the cheapest model and service tier likely to meet each deadline. Ramp reports 30% savings in Ramp Inspect without performance loss.
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