Contracting with AI Vendors
- Colin Levy
- Apr 8
- 1 min read
In this guide, I explain why AI and artificial intelligence vendor contracts differ from standard SaaS, focusing on who owns AI outputs, whether your data trains third-party models, how hallucinations are handled, and how to measure performance for systems that behave probabilistically. I highlight how boilerplate terms on training data, subprocessors, output ownership, model drift, bias, and unilateral changes usually favor vendors, and I provide concrete replacement language and checklists so lawyers can renegotiate those clauses when contracting with AI vendors.
I address data, privacy, and privilege in depth, unpacking what “training on your data” really means, why embeddings and vector databases matter, what AI-specific DPAs must include, and how to protect privilege and confidentiality across sensitive practice areas while contracting with AI vendors. I then tackle liability, performance standards, and SLAs, arguing for output-level indemnification, hallucination-aware liability allocations, and accuracy and bias thresholds instead of “as is” disclaimers, along with exit terms that prevent lock-in and align with the EU Data Act.
The guide closes with practical negotiation tactics and risk-transfer strategies tailored to AI and artificial intelligence deals, including using industry templates and competitive intelligence, navigating new regulatory regimes like the EU AI Act and Colorado AI Act, and tying vendor liability caps to real insurance coverage so that contracting with AI vendors fairly allocates AI-specific risks while keeping human legal judgment at the center.