Cost Management › Summary

Cost management for LLM deployments requires sustained attention across four interconnected areas.

Cost modeling begins with the token-based cost model: cost per token is determined by infrastructure cost divided by throughput. GPU utilization is the primary lever, doubling utilization halves the effective cost per token. The prefill-decode distinction matters because these phases have different cost characteristics.

Cost optimization draws on model right-sizing (the most impactful decision), quantization (reduces memory footprint and improves memory-bandwidth-bound throughput), caching (prefix caching for shared prompts, semantic caching for repeated queries), speculative decoding (increases effective throughput for predictable generation tasks), and hardware strategy (reserved instances for baseline load, spot/preemptible for batch workloads).

Cost allocation requires request-level instrumentation with tenant, feature, and user metadata. The choice between showback and chargeback is organizational as much as technical; showback builds awareness without creating friction, while chargeback creates direct incentives for efficiency. Per-user limits prevent runaway spending from individual users.

Cost monitoring tracks costs at multiple granularities: real-time for operational awareness, daily for trend detection, and monthly for planning. Anomaly detection using EWMA baselines catches cost spikes early. Per-feature token tracking enables regression detection after deployments. Budget enforcement with graceful degradation maintains service availability under budget pressure by routing to cheaper model tiers rather than blocking requests entirely.

The next chapters continue examining production systems concerns, including quality monitoring and observability patterns that complement the cost monitoring approaches covered here.