Why Serving a 1M-Token AI Model in Production Is Harder Than It Looks

Large language models now advertise context windows of up to one million tokens, but supporting that capability and reliably serving it to concurrent users are two distinct engineering challenges. The key technical bottlenecks are prefill compute time, GPU memory consumed by KV caches, and per-request costs that scale with context length rather than query complexity. Production teams manage these pressures through techniques such as KV cache reuse, prefix sharing methods like RadixAttention, and cache-aware request routing. Retrieval-augmented generation serves as both an alternative and a complement when long context is not the most cost-efficient tool for a given workload. Without deliberate management of caching and routing strategies, teams risk unpredictable latency spikes, runaway infrastructure bills, and silent degradation in model accuracy as input length grows.