AI Vendor Lock-In & Model Portability
AI Vendor Lock-In occurs when an architecture is too dependent on a single AI provider, making it difficult to switch to cheaper or better models.
Foundation models update silently, and pricing is volatile. Vendor lock-in prevents organizations from leveraging cheaper, faster models (like open-source SLMs), directly exposing them to the AI Volatility Tax.
Latest Publications & Research Activity
The Hidden Inflation of AI: Why Model Collapse Is a Business Risk
Your Claude API Bill Is Higher Than Your Revenue: Why Simple Python Tasks Are Blowing Up AI Costs
Why Redundant Requests Are Driving Hidden AI Costs
Frequently Asked Questions
Q:How do you avoid AI Vendor Lock-In?
By building an abstraction layer that allows you to easily route prompts to different models (e.g., switching from GPT-4 to Claude or Llama).
Inspectable Evidence Ledger
Classified evidence items supporting, extending, or refining this canonical research specification.
| Evidence Item | Publisher | Evidence Type | Strength | Role | Action |
|---|---|---|---|---|---|
| The Subprime Code Crisis | Built In | Editorial | ★★★★★ | Origin | Inspect ↗ |
Recommended Citation
Ewing, R. (2026). "AI Vendor Lock-In & Model Portability." Richard Ewing Research Canon. Available at: https://www.richardewing.io/concepts/ai-vendor-lock-in
@article{ewing_ai_vendor_lock_in,
author = {Ewing, Richard},
title = {AI Vendor Lock-In & Model Portability},
journal = {Richard Ewing Research Canon},
year = {2026},
url = {https://www.richardewing.io/concepts/ai-vendor-lock-in}
}