If I white-label an AI tool, is the vendor still responsible for its output?

Responsible to you, on the terms of your agreement with them — and to nobody else. Your customer has no contract with the vendor, does not know the vendor exists, and will bring every claim, refund demand and complaint to you. Whether you can move that loss upstream depends entirely on indemnities and liability caps you negotiated before the incident, not on the fact that you did not build the model.

Does rebranding make me the provider under AI regulation?

In several regimes, yes. Placing a system on the market under your own name or trade mark is a common trigger for assuming the obligations of the original provider, as is making a substantial modification or changing the intended purpose. The practical effect is that a reseller who considers itself a distributor can be carrying documentation, risk management and conformity duties it has never looked at.

What are the most important contract terms to get right?

An indemnity that covers third-party IP claims arising from model outputs, a liability cap that is not a multiple of your fees to them (your exposure scales with your customers, not their revenue), a change notification commitment for model versions and deprecations, an audit or documentation right sufficient to answer your own customers' questions, deletion and data-use terms you can pass through verbatim, and a survival period for support after termination.

Do I have to disclose that the AI is someone else's?

Naming the vendor is often commercially undesirable and rarely strictly required, but disclosing that a third-party subprocessor handles the data usually is, under data protection terms and increasingly under enterprise procurement. The failure mode is not the non-disclosure itself — it is representing to customers that data never leaves your systems while it is being sent to an inference API.

Can I promise accuracy figures the vendor gave me?

Only if you are willing to own them. Once you repeat a performance number under your brand it becomes your representation to your customer, measured against your customer's data rather than the vendor's benchmark. Repeating a marketing figure without the test conditions attached is the most common way a resale relationship produces a misrepresentation claim you cannot pass upstream.

What happens if the vendor shuts down or deprecates the model?

Your customers' contracts do not pause. A model deprecation with ninety days' notice is a routine vendor decision and a service-level breach for you if your commitments assumed continuity. Negotiate deprecation notice periods and a wind-down window that exceed your own customer notice obligations, and keep an evaluation harness so you can qualify a replacement quickly rather than discovering behaviour changes in production.

White-Label AI Liability 2026: Who Answers When You Resell Someone Else's Model Under Your Brand

Three Things Move and One Thing Does Not

The rebrand moves the contract, the representations and the incident response to you. Your customer signs with you, reads your marketing, calls your number and reads your terms. When an output is wrong, defamatory, discriminatory or infringing, you are the party in the room.

What does not move is control. You cannot retrain the model, inspect its training data, change how it behaves at the margins, or promise a fix on a date. You are accountable for a system you can configure but not correct — which is a tolerable position with the right contract and an untenable one with a standard reseller agreement.

Every recommendation below follows from that single asymmetry: buy back, in the contract, as much of the control you lack as the vendor will sell.

The Branding Trigger in Regulation

Product-safety-style frameworks — the model the EU AI Act follows — treat placing a system on the market under your own name or trade mark as an act that transfers the original provider's obligations to you. The same result usually follows from making a substantial modification or from putting the system to a purpose the original provider did not intend.

This catches resellers who think of themselves as distributors. A distributor passes a product along; a white-labeller presents it as their own. If your marketing describes "our AI", your documentation carries your name, and your customer has never heard of the underlying vendor, arguing that you were merely reselling is a difficult position taken after the fact.

Sector rules stack on top. If the capability sits inside a regulated activity — lending, insurance, healthcare, employment screening — the sectoral obligations attach to the entity performing the activity, and no amount of upstream contracting relocates them.

The Terms That Decide Whether You Can Pass Loss Upstream

Output IP indemnity. Not just an indemnity for claims that the software infringes, but for claims arising from what the model generates. Check the conditions attached — many are void if you modify prompts, disable filters or exceed usage terms, which is to say if you use the product normally.
A cap that reflects your exposure. A limit set at twelve months of the fees you pay the vendor is standard and structurally wrong for a white-label: your downstream contracts may be worth many times that. Negotiate a separate, higher cap for indemnified claims, or price the residual risk in.
Change and deprecation notice. A committed notice period for model version changes and end-of-life, longer than the notice you owe your own customers. Without it, an upstream deprecation is automatically a downstream breach.
Documentation sufficient for your buyers. The right to receive intended-purpose statements, limitations, performance characteristics and subprocessor lists, in a form you can share. Your enterprise customers will ask; "we cannot tell you" is a lost deal.
Data terms you can pass through verbatim. If you promise customers no training on their data, that promise must be identical upstream, including for logs, abuse-monitoring retention and fine-tuning. A gap here is a misrepresentation waiting for its first security questionnaire.
Deletion that actually reaches them. Your deletion SLA cannot be shorter than the vendor's, and their retention for trust-and-safety review is usually longer than either of you told the customer.
Post-termination continuity. A wind-down window and export rights, so a contract dispute upstream does not instantly break the product your customers depend on.

What to Say to Customers

The two failure modes are opposite and both common. Some resellers over-claim: they repeat vendor benchmark figures as guarantees, describe the system as proprietary when it is an API call, and assert that data never leaves their infrastructure. Others hide so thoroughly that a routine subprocessor question turns into a trust problem.

The workable middle is specific and boring. Say that the capability is powered by third-party models, disclose the subprocessor in the standard place, describe performance in terms of what you have measured on your own workload rather than what the vendor published, and state plainly what the system should not be used for. None of that damages the brand; all of it is the difference between a support ticket and a claim.

Then align your own terms. Your customer contract should carry an AI-specific clause covering output accuracy, required human review for consequential uses, acceptable use, and a liability cap that is compatible with what you recovered upstream. Selling under an unlimited-warranty template while buying under a capped one is the arrangement that leaves you holding everything.

A Pre-Launch Checklist

Write down your role. Reseller, deployer or provider-by-branding, for each regime that could apply. Decide it deliberately rather than discovering it in a questionnaire.
Diff the two contracts. Put your customer terms beside your vendor terms and mark every promise you make that you have not bought. That list is your uninsured exposure.
Audit the marketing copy. Every accuracy number, every "proprietary", every claim about where data goes. Remove what you cannot evidence on your own workload.
Build an evaluation harness. A fixed test set you can rerun when the vendor changes the model, so behaviour drift is detected by you and not reported by a customer.
Plan the substitution. Know which alternative model you would move to and what it would take. Portability is the only real leverage you have at renewal.
Check the insurance. Technology errors and omissions cover often excludes or under-covers AI output claims; confirm rather than assume, before the launch and not after.

Common Questions

We are an agency delivering AI work to clients. Same analysis?

Largely, with one addition: deliverables. Agencies sit under work-made-for-hire and ownership expectations that assume the output is authored, and AI-assisted deliverables complicate what you can actually assign. Address ownership of AI-assisted output in the client agreement, separately from the reseller questions here.

Does a disclaimer in our terms solve this?

It helps against contract claims from a sophisticated counterparty and does very little against regulatory duties, consumer protection rules or third-party claims from someone who never signed anything. Disclaimers are a component of the answer, not the answer.

The vendor will not negotiate. What then?

Price the risk instead of ignoring it. Narrow the use cases you sell into, require human review for consequential decisions, cap your own downstream liability accordingly, and be explicit internally that this line carries unrecoverable exposure. A deliberate acceptance is a defensible position; an unnoticed one is not.

Do we need to name the underlying model to customers?

Usually not by name, but you do need to disclose that a third party processes their data, and enterprise buyers will require the subprocessor list. Reserve the right in your vendor agreement to make that disclosure, since some agreements restrict it.

What if we fine-tune the model ourselves?

Then you have moved further toward being the provider, and the vendor's indemnities frequently exclude fine-tuned behaviour. Fine-tuning is often the right product decision and it should be taken knowing it shifts both the regulatory characterisation and the contractual protection.

How is this different from reselling ordinary SaaS?

Two ways. Ordinary software fails deterministically and visibly, while a model fails plausibly and silently, so the harm is discovered late. And AI-specific regulation attaches obligations to branding and modification in a way general software law does not, which means the rebrand itself is a legally significant act rather than a marketing one.

Your Marketing Page Is the Representation

The accuracy claims, the "proprietary AI" language and the data-handling statements on your site are what a customer relied on — and what AI assistants repeat when someone asks them about your product.

Run a free scan of your site to see what you are currently claiming about your AI features.