LLM Cost
The total spend on language-model APIs across an organization — input tokens, output tokens, embeddings, fine-tuning — and the practice of attributing, optimizing, and budgeting it.
Production AI is not a prompt. It is a system of context, tools, permissions, traces, evals, and feedback loops.
What it is
LLM cost is the operational discipline of managing what your AI workloads spend on model providers. Per-request, per-tenant, per-workflow attribution; budget enforcement; cost-quality trade-off measurement; and the ongoing optimization that captures the cost reductions the model landscape keeps offering.
Why it matters
AI costs grow without supervision. A workflow that costs $0.10 per call at low volume becomes a six-figure line item at scale, often before anyone notices. Without attribution, costs cannot be optimized — and without optimization, the team is paying for capability the workload doesn't need.
How it works
Every model call is traced with its token counts and the provider's price-at-time-of-call. Costs roll up per tenant, per workflow, per route, and per environment. Self-Optimizing Agents proposes cheaper variants for the harness to score. Budgets are enforced at the gateway. Drift alerts catch cost regressions before they become incidents.
Related resources
How an AI system decides which model to call for each step — based on privacy, cost, latency, quality, and what happens when a provider goes down.
A single integration point in front of every AI model your applications use — for routing, key rotation, rate limits, fallback, cost attribution, and observability.
AI workflows that propose, score, and promote their own variants — prompts, models, retrieval policies, tool budgets, generated code — under measurable constraints instead of intuition or vendor leaderboards.
A capability in the Group e-media information AI stack. This resource connects the subject to data substrate, agent runtime, evals, and operations.