LLM.co

Enterprise AI deployments are failing at alarming rates — and the contract structure you sign may be the biggest reason why. This episode breaks down the three dominant private LLM contract models and how to choose the right one before you sign.

Show Notes

With AI pilot failure rates hovering near eighty percent and tens of billions in enterprise spending producing little measurable return, the contract model behind a private LLM deployment deserves far more scrutiny than most buyers give it. This episode of LLM.co examines the three structures dominating the private LLM market today — drawing on the detailed breakdown of private LLM contract models on the LLM.co blog — and explains how each one allocates risk, ownership, and exit rights in ways that can make or break an enterprise AI program.

The episode covers:

  • Why contract structure is the real risk decision: Who absorbs a bad assumption — the buyer or the vendor — is determined entirely by which contract shape is signed, not by which model is chosen.
  • Fixed-scope builds: Best for well-bounded use cases with stable data and written acceptance criteria; regulated-industry builds often add $200K–$600K over baseline hosting, with vendors pricing in a 15–30% contingency buffer for unknowns. Teams evaluating a private AI contract review deployment are a prime fit — but only when the scope can be described unambiguously before signing.
  • The scope-drift failure mode: Fixed-scope contracts break down when evaluation criteria shift after stakeholders see early outputs — a hallucination that looked acceptable in the SOW can become a showstopper in demo week, and every change becomes a margin-carrying change order.
  • Managed appliance subscriptions: The vendor operates a private model inside the buyer's network perimeter; risk for model performance and drift shifts to the vendor while the buyer retains data governance. Modern hardware enables fine-tuning models up to 70B parameters on a single high-memory node, making a true LLM appliance viable even in air-gapped compliance environments.
  • Exit-clause essentials for appliance contracts: Before signing, secure a portable-format egress clause for weights and vector indexes, a data-destruction attestation with a defined timeline, and a right to run the system in read-only mode during wind-down — or "managed" quietly becomes lock-in.
  • Co-build engagements: Time-and-materials or retainer arrangements where vendor engineers embed under the buyer's technical leadership; the right fit when requirements are genuinely emergent, but IP allocation must explicitly name ownership of fine-tuned weights, training corpus derivatives, and evaluation harnesses — silence on any of those becomes costly at renewal.

For more on EU compliance obligations that intersect with all three contract structures, listen to EU AI Act Conformity for Self-Hosted LLMs: What Your Evidence File Must Prove. Source material and further reading are available at the LLM.co blog link above.

LLM.co

cstm.ai

What is LLM.co?

Private and custom large language models — the build, the boundaries and the bill. Fine-tuning versus retrieval, running models in your own environment, evaluation you can actually trust, data governance, and the questions to ask before a vendor answers them for you.

Each episode takes one decision a team is facing — whether your problem needs a custom model at all, how to evaluate output without fooling yourself, what "private" has to mean contractually — and works it through concretely. Written for engineering and data leaders putting a model into production. Five or six minutes, one idea, no demos.

Topics include fine-tuning versus retrieval, self-hosted and private deployment, evaluation you can trust, prompt and context design, data governance and retention, cost and latency tradeoffs, and what "private" has to mean contractually.

Produced by LLM.co, private and custom large language models. Full details, services and further reading at https://llm.co