Controlling LLM Spend from AI Features Embedded in Vendor Software

Treat embedded AI as a control-surface problem

Vendors are adding assistants, summarization and copilots to software you already run, and they bill for them through credits, add-on SKUs, per-seat uplifts or usage-based pricing. These features usually sit outside your own model gateway. The result is that platform teams cannot easily say which features consume LLM capacity, who triggers them, or how that activity maps to a charge.

Pricing is the visible symptom. The underlying issue is where you can exert control. Usage can grow through default-on features, unmetered usage, agentic or looping calls, and pricing you cannot inspect. Each of those has a different remedy, and the remedy depends on who owns the lever and whether the traffic is observable or interceptable.

That framing keeps the work practical. Instead of negotiating every vendor from scratch, you sort features into control classes and apply the levers each class actually offers.

Step 2: Classify each feature by available control

Once the register exists, place each feature in one of four classes. The classes differ by owner and by what they can and cannot do. Most estates will contain features in every class, which is why a layered approach is needed.

• Vendor admin settings: Owner: vendor, configured by your administrators. Controls: enablement, tenant or group scope, any quotas or reporting the vendor provides. Limit: you can only use what the vendor exposes, and enforcement happens inside the vendor's system where you cannot inspect it.
• Contract terms: Owner: procurement and legal. Controls: usage caps, overage notification, rate-card transparency, audit rights, opt-in for new AI features. Limit: terms set expectations and remedies but do not stop a request at runtime.
• Network or API-level mediation: Owner: platform engineering. Controls: routing vendor AI traffic, or calls made with your own API keys or models, through a point you operate. Limit: only works when the architecture allows it. Encrypted, vendor-hosted traffic that never crosses your infrastructure cannot be mediated.
• No technical control: Owner: procurement, security and the business. Controls: purchase decisions, contractual remedies, and exit or substitution. Limit: no runtime lever exists, so risk is managed through terms and review.

Step 3: Apply runtime enforcement where traffic passes through it

Where an embedded feature uses a BYO-model or API key option, or its calls route through infrastructure you control, you can enforce policy at runtime. The relevant controls operate on individual interactions and tool calls: allow, redact, block, rate limit, and escalate for human approval. Rate limiting caps how many interactions a user, feature or service can make in a period, which bounds loops and runaway automated calls. Blocking and allow rules can restrict which tools or data a feature can reach.

Escalation needs a definition before configuration. A January 2026 federal request for information on AI agent security lists approvals for consequential actions, tool and data restrictions, and network access permissions among its control examples, and cites least privilege and zero trust as underlying practices. Decide which tool calls count as consequential in your environment, then route those to a human approver and let low-risk calls proceed.

Trussed enforces policy in the execution path for this class of traffic, across models, agents, tools and MCP servers, and generates audit-ready evidence of what was enforced. It applies only where interactions or tool calls route through it. It does not meter vendor billing, reconcile charges against invoices, or reach features that never cross an enforcement point. It also does not provide end-to-end tracing of agentic workflows. Its scope is the individual interaction or tool call.

The same boundary applies to any enforcement tool. Where a feature runs entirely inside the vendor's environment, a runtime policy cannot touch it, and the control has to come from the vendor's settings or the contract.

Step 5: Layer the controls and retain evidence

A workable layering follows the classes. For every feature, set the vendor-side default to off unless a business owner opts in. Back that with contract terms covering caps, notice and reporting. Where traffic is interceptable, add runtime enforcement with rate limits and approvals for consequential actions. For features with no technical control, record the residual exposure and assign a named owner to review it.

Retain records of the controls you apply and how they are monitored, consistent with the NIST emphasis on documented risk controls. For vendor-side settings, that means dated configuration records and the contract terms in force. For runtime enforcement, it means a durable record of which policy applied to which interaction or tool call and what action was taken. Trussed generates this evidence from enforcement itself, so it reflects what was enforced at runtime, not a review after the fact.

Revisit the register when a vendor announces a new AI feature, changes its pricing model, or alters defaults. Those events are the points where control classes shift, and they are easiest to catch when notice terms and an owner are already in place.