AI cloud contracts: what to check before you sign

Using AI services in the cloud speeds up projects, but the contract often hides the real risks. Want to avoid nasty surprises with data use, surprise bills, or unclear liability if a model fails? Start by reading the contract with a checklist—everything else is just wishful thinking.

What to watch in AI cloud contracts

Data ownership and use: Confirm who owns the data you upload and how the vendor can use it. Ask for a clause that forbids the provider from training their models on your proprietary data unless you explicitly agree. Request the right to remove your data and to receive it back in a usable format.

Intellectual property and models: Clarify ownership of model outputs and any custom models trained on your inputs. If you want exclusive rights to a trained model, put that in writing. If the vendor retains ownership, get a clear license that lets you operate, export, and audit the model.

Liability and indemnity: AI can make wrong or biased decisions. Limit your liability and seek vendor promises on accuracy only where reasonable. Insist on mutual indemnities—especially for third-party IP claims and data breaches caused by the vendor.

Security and compliance: Demand specific security measures (encryption at rest and in transit, access controls, logging). If you must meet regulations like GDPR or sector rules, require vendor compliance and assistance for audits and breach notifications.

Service levels and availability: SLAs should include uptime guarantees, response times for incidents, and credits for failures. Define what constitutes an outage for AI services, since degraded model quality can be as harmful as downtime.

How to negotiate better terms

Pricing and usage caps: AI workloads can explode cost. Push for predictable billing (flat fees, committed use discounts, or caps) and transparency—detailed usage reports and alert thresholds before overage charges kick in.

Portability and exit plan: Ask for data and model export formats, and a clear timeline for handing back assets on termination. Include a short transition period with vendor support to avoid being locked in.

Audit and transparency rights: Request audit access to logs and model training records relevant to your data. Ask for model explainability features or documentation so you can trace decisions when needed.

Testing and acceptance: Before full deployment, negotiate a pilot or acceptance test to validate model performance and integration. Make payment milestones contingent on passing these tests.

Small changes in contract language can save big headaches later. Start with the clauses above, document your must-haves, and involve legal and security teams early. If you want a quick checklist to share with vendors or teammates, I can draft one tailored to your use case.