AI Token FinOps & Governance Glossary

AI chargeback allocates AI and LLM costs back to the teams, departments, or cost centers that incurred them. With per-request attribution and BYO provider keys, Behest makes each unit of AI spend traceable to an owner for clean internal billing.

AI cost attribution assigns every model call's token spend to the user, team, project, or feature that triggered it. It turns an aggregate provider invoice into a per-unit breakdown finance can allocate and charge back.

AI cost attribution traces each model call's token spend to the exact user, project, or feature that caused it. Cost allocation then distributes that spend — including shared and overhead costs — across teams or cost centers for budgeting and chargeback. Attribution measures; allocation divides.

AI FinOps brings cloud FinOps discipline — cost attribution, budgeting, and forecasting — to AI and LLM spend. It lets organizations see, allocate, and control what their AI usage actually costs.

An AI gateway sits in front of your LLM to route, log, and cache traffic. An AI backend operates the full layer — auth, CORS, memory, PII scrubbing, prompt-injection defense, rate limiting, and token budgets. A gateway observes; a backend operates.

AI governance is how an organization decides which AI models can run, what data they can access, and what audit evidence each request must produce. Behest enforces those decisions on the request path with model allowlists, PII scrubbing, and audit trails.

AI invoice reconciliation is the process of matching an AI provider's monthly bill against your own per-call token and cost records to confirm the charges are correct and fully attributed. It catches billing discrepancies and unexplained spend before finance pays the invoice.

AI Token FinOps is the control center for your AI spend: tracking, allocating, and controlling AI Token budget at the unit level. It brings the rigor companies already apply to cloud costs (attribution, budgets, forecasting) down to every model call, so AI cost becomes visible and predictable before the invoice arrives.

AI unit economics is the per-unit cost and margin analysis of AI features — the token cost of one request, user, session, or transaction measured against the revenue or value it produces. It tells you whether an AI product makes money at the unit level before you scale it.

An LLM gateway is a proxy that sits in front of one or more large language model providers to route requests, log usage, cache responses, and enforce rate limits. It centralizes model access but, on its own, stops short of cost attribution, budget enforcement, and governance.

LLM smart routing automatically sends each AI request to the cheapest or best-fit model that meets its quality bar. Depending on use case, routing to cheaper models can cut AI costs by up to ~30% — without changing application code.

PII scrubbing detects and removes personally identifiable information — names, emails, and more — from prompts before they reach an LLM. Behest's PII Shield, powered by Microsoft Presidio, can mask, redact, or block sensitive data on the request path.

Prompt injection is an attack where crafted input tricks an LLM into ignoring its instructions or leaking data. Behest's Sentinel detects and blocks common jailbreak and injection patterns on the request path before they reach the model.

Shadow AI is the use of AI models, tools, or APIs inside an organization without IT or security approval or oversight. It creates ungoverned cost, data-leak, and compliance risk because no one can see or control what those models do.

A token budget is a hard cap on the tokens or dollars a user, team, or project can spend on AI over a period. Behest enforces it on the request path, blocking or throttling calls before they exceed the limit.