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Glossary

Cloud cost and AI governance, defined

Plain definitions of FinOps, cloud cost optimization, LLM gateway, AI guardrails, prompt injection, shadow AI and agent control — written to be quoted directly.

Short answer

FinOps is the operating model that makes cloud spend a shared responsibility of finance and engineering; cloud cost optimization is the continuous work of removing waste inside it. On the AI side, an LLM gateway is the single control point every model request passes through, AI guardrails are the runtime checks it applies to prompts and responses, and AI governance is the whole enforced system of allowed models, permitted data, agent permissions, budgets and audit evidence.

Cloud Optimization

Cloud cost terms

Cloud cost optimization

Cloud cost optimization is the continuous practice of finding and removing wasted cloud spend — idle compute, oversized instances, unattached storage, forgotten environments and inefficient commitments — without degrading performance.

It is continuous rather than a one-off cleanup: cloud estates drift back toward waste within weeks as teams ship. A working programme combines automated waste detection, rightsizing recommendations, commitment coverage, storage lifecycle rules and an owner for every line of spend. Industry benchmarks put wasted cloud spend at up to 30% of the average bill.

Cloud Optimization →

FinOps

FinOps is the operating model that makes cloud spend a shared responsibility of finance, engineering and leadership, with a repeating Inform, Optimize and Operate lifecycle.

Cloud cost management is the tooling layer; FinOps is the practice around it — allocation and tagging standards, forecasting and variance reviews, unit-economics reporting, and decision rights for who may approve spend. bluebill delivers both the platform and the practitioners so the model runs without a separate internal programme.

FinOps →

Cloud unit economics

Cloud unit economics expresses infrastructure cost per unit of business value — cost per customer, per tenant, per transaction — instead of cost per service.

It is the metric that makes cloud spend legible to a board: absolute spend can rise while cost per customer falls, which is a healthy scaleup. Producing it requires reliable allocation (tags, accounts, Kubernetes namespaces) mapped onto business dimensions from the product or billing system.

Cloud cost workflows →

Rightsizing

Rightsizing is matching the size of a cloud resource to its observed utilization, so an over-provisioned instance, database or volume is moved to the smallest configuration that still meets its performance target.

It relies on utilization history (CPU, memory, IOPS, throughput) over a representative window rather than a single day, plus headroom rules for spiky workloads. Rightsizing is usually the second-largest saving after removing idle resources entirely.

Idle resource reclamation

Idle resource reclamation is the systematic shutdown or deletion of cloud resources that are running but doing no useful work — stopped-instance volumes, unattached IPs, empty load balancers, abandoned test environments.

Idle waste is the fastest saving available because it carries no performance risk: nothing is using the resource. The hard part is organizational, not technical — proving the resource is unused, finding an owner, and scheduling deletion with a rollback window.

Commitment strategy

Commitment strategy is the plan for how much of a stable cloud workload to cover with reserved instances or savings plans, at what term, to lower the rate paid without locking in capacity the business will not use.

It follows optimization, never precedes it: committing to an oversized fleet locks in waste for one to three years. Good practice covers the durable baseline of demand, leaves burst capacity on demand, and revisits coverage each quarter as the estate changes.

AI Governance

AI governance terms

AI governance

AI governance is the set of enforced controls over how an organization uses AI — which models are allowed, what data may reach them, what agents may do, what it costs, and what audit evidence exists afterwards.

Governance is only real when it is enforced at runtime rather than written in a policy document. In practice that means a control point every request passes through, identity attached to each call, guardrails on input and output, budget ceilings per team, and an immutable log that can be handed to an auditor.

AI Governance →

LLM gateway

An LLM gateway is a single entry point for all model traffic that adds routing, central key management, rate limits, budgets, guardrails and observability without changing every application.

Because all traffic passes through one place, a gateway can route each request to the cheapest model that meets the quality bar, fail over when a provider degrades, cache repeated calls, attribute cost to a team, and enforce policy consistently across every application.

LLM gateway →LLM gateway vs. AI proxy →

AI proxy

An AI proxy forwards model requests and centralizes API keys; it is plumbing, whereas an LLM gateway adds routing, budgets, guardrails, cost attribution and audit logging on top of that plumbing.

A proxy is enough for one team, one provider and no regulated data. Once several teams, several providers or sensitive data are involved, the missing control layer — who spent what, which model handled which request, what was redacted — is exactly what a gateway supplies.

LLM gateway vs. AI proxy →

AI guardrails

AI guardrails are runtime checks on prompts and responses that block prompt injection, redact PII and secrets, enforce policy, and prevent data leaks before a request reaches a model or a response reaches a user.

Guardrails work in four layers: input (injection and jailbreak detection), data (PII and secret redaction), output (policy and leak screening), and action (which tools an agent may call). Each layer produces evidence, which is what turns guardrails into a compliance artefact rather than a filter.

AI guardrails →

Prompt injection

Prompt injection is an attack where instructions hidden in user input, a document, a web page or a tool response cause a model to ignore its own instructions and act on the attacker's behalf.

It is the most common practical attack on LLM applications because any content an agent reads is potential instruction. Defence is layered: treat retrieved content as untrusted data, detect instruction-like patterns at the gateway, restrict what tools an agent may call, and require confirmation for irreversible actions.

AI guardrails →

Shadow AI

Shadow AI is employee use of AI tools outside any approved channel — personal accounts, unmanaged API keys, browser extensions — where the organization has no record of what data was sent or what it cost.

Shadow AI is usually a symptom of friction rather than defiance: the sanctioned path is slower or missing. The durable fix is to make the governed route the easiest one — a gateway with fast approval for new models — rather than to block tools people need to do their work.

AI cost attribution

AI cost attribution assigns every token and every model call to a team, product, environment or customer, so AI spend can be budgeted and charged back like any other cost line.

It requires identity on each request at the gateway, not a monthly provider invoice. With attribution in place you can set per-team budget ceilings, spot a runaway agent within minutes, and answer the question finance always asks: which product is this spend for?

Autonomous agent control

Autonomous agent control is the set of scoped permissions, tool allow-lists, budget ceilings and anomaly detection that limit what an AI agent may do before it is given production access.

An agent multiplies both usefulness and blast radius: it can loop, spend and act. Control means least-privilege credentials per agent, an explicit list of callable tools, hard spend and step limits, human confirmation for irreversible actions, and a trace of every step for post-incident review.

AI governance workflows →

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