Liquid Inference is serving 10B tokens/day and growing 200% week over week. Most of the traffic is agents and long chats. Across 470k charged jobs, 88.7% of input tokens were cache reads. Here’s how our matching algorithm prices offers with cache hit rate included: In multi-turn and agentic workloads, the biggest cost and latency wins come from prefix/KV cache reuse. Each turn resends the full conversation, so if a provider still has that prefix in its cache it can bill those tokens at its cache-read rate, often a small fraction of the normal input rate. Naive load balancing or pure price auctions scatter a session across different providers or replicas, forcing full prefill on every turn and destroying the savings. Our matching algorithm ranks offers on the expected charge inclusive of cache hit rate rather than the full list price: 1. We measure how much of each workspace’s input repeats from one turn to the next (7-day window, at least 50 jobs). 2. We multiply that share by the measured cache hit rate of each deployment. 3. We price those tokens at the offer’s cache-read rate and subtract the expected saving from the offer’s ranking charge. 4. The deployment that served the previous turn keeps a 5% margin, so a cheaper offer has to win by more than 5% to pull a warm session away. In other words, for the right price we will abandon the cache and choose the cheaper alternative. The credit only changes the order of the offers. A wrong estimate cannot make a job cost more than its price cap. Every receipt shows the cache share we expected, the share that was realized, and the credit in dollars. That’s how we continue to improve on cache hit rate and provide the best economics for our clients’ sessions.