What is LLM cost management? Tracking tokens, usage, and spend

LLM cost management combines tracking, monitoring, forecasting, budgeting, attribution, and optimization to keep AI application spending within acceptable quality, latency, and reliability requirements. Investigating a bill increase begins with reconciling provider usage and invoices, then distinguishing greater traffic from higher cost per call by examining changes in models, prompts, retrieval, cache usage, retries, agent behavior, and tenant or feature mix. Provider invoices alone often lack the request-level context needed to identify the responsible customer, feature, prompt version, or agent run, making instrumentation that records token categories, model details, metadata, latency, and quality outcomes essential. Per-request costs include input, output, cached, reasoning, retry, tool, and agent-step charges, with pricing varying by provider, model tier, context size, discounts, and service configuration. Effective systems connect dashboards and alerts to underlying traces, use normalized pricing and careful attribution, reconcile estimates with bills, and evaluate unit metrics such as cost per quality-approved output rather than total spend alone. Cost reductions can come from shorter prompts, caching, model routing, batch processing, retry limits, agent budgets, and moving deterministic work into conventional code, but each change should be tested against consistent quality and performance criteria. Braintrust is presented as a platform for retaining trace metadata, analyzing cost and quality together, testing alternatives, gating regressions in CI, and monitoring deployed workloads through dashboards and alerts.

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