Ramp presents an approach to cost-efficient LLM routing based on online learning and Thompson sampling. The router can update its choices as feedback arrives, balancing response quality, latency, and inference cost across available models or providers. The topic is relevant to production systems that operate multiple LLMs, but the supplied Hacker News metadata contains no benchmarks or implementation details. Specific routing signals, experiments, and claimed savings should therefore be verified against Ramp's original engineering post.
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