Exit Economics in Enterprise AI: What You Actually Keep When You Leave
enterpriseai
Vendor churn is easy to model. AI churn isn't — because the memory, embeddings, and workflows may not come with you. Here's how to price exit into the deal.
Every enterprise AI contract has an exit clause. Very few have honest exit economics. The gap between "you can leave" and "you can leave with everything that made the system valuable" is where lock-in actually lives.
What "exit" really covers
When procurement asks about exit, they usually mean the contract. When engineering asks, they mean the data dump. Neither captures what matters after eighteen months of use:
- Raw source documents you uploaded
- Derived artifacts: embeddings, chunk indexes, fine-tunes
- Organizational memory: the graph of entities, relationships, and decisions the system built
- Prompts, skills, and agent definitions your teams authored
- Audit trails and citation history tied to past answers
Most vendor contracts return the first bucket. Sometimes the fourth in an export format nobody else reads. The middle two — the parts that compound — stay behind.
The three exit questions worth asking before signing
- Portability of memory. If we leave on day 400, do we get the knowledge graph and embeddings in a format a successor system can ingest — or just the source files we already had?
- Portability of behavior. Do skills, agents, and guardrails export as portable definitions, or are they configuration inside the vendor's runtime?
- Portability of proof. Can we take the audit log — which answer cited which source, approved by whom — with us for regulators who don't care that we switched vendors?
Why hosted-only platforms can't answer these cleanly
A vendor whose business is renting inference has a structural reason to keep derived artifacts on their side of the wall. It isn't malice; it's the shape of the product. The embeddings live in their vector store. The graph lives in their schema. The audit log lives in their observability stack. Exporting all three in a form a competitor can reconstitute is, effectively, handing over the moat.
Self-hosted architectures invert this. When the runtime sits inside your infrastructure, the derived artifacts are already yours — exit is a matter of pointing a new front-end at existing storage. That's the practical meaning of bringing your own model and owning your enterprise memory.
A simple exit-cost worksheet
| Line item | Hosted vendor | Self-hosted |
|---|---|---|
| Recover source documents | Included | Already yours |
| Recover embeddings/graph | Often unavailable | Already yours |
| Rebuild skills/agents | Re-author from scratch | Portable definitions |
| Preserve audit trail | Export window, then gone | Retained indefinitely |
| Time to parity on new platform | 3–9 months | Weeks |
Price those rows into year-three TCO and the "cheaper" hosted option often isn't. The Silverberry AI Platform is designed so exit day looks like any other day — because the assets never left your walls in the first place.