How to size dedicated LLM inference for steady production traffic

Dedicated LLM inference can be less expensive than per-token shared pricing only when reserved GPU capacity is kept sufficiently utilized, making accurate sizing essential. Planning should begin with application-level logging of prompt and completion tokens separately, since prefill and decoding have different hardware demands, then aggregate this data into minute-level patterns over a representative week to identify baseline load, normal peaks, extreme spikes, and seasonal variation. Teams should benchmark their specific model, context lengths, output lengths, and concurrency to estimate per-GPU throughput, then size reservations with deliberate headroom based on latency requirements, peak-to-floor variation, expected growth, and whether workloads are interactive or batch-oriented. The cost comparison should include shared endpoint token charges, fixed reservation costs, hybrid spillover traffic, and retry costs caused by rate limiting, producing a utilization threshold at which dedicated capacity becomes economical. Additional considerations include the value of predictable throughput, spare capacity for additional tasks, more stable evaluations, and the operational responsibility of managing capacity, while actual utilization should be reassessed after deployment.

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