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 using cheaper, faster models (like open-source SLMs), directly exposing them to the AI Volatility Tax.
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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.
Direct Relationships (1)
Transitive Neighbors (Connected via Hop 1)
Extended Causal Ripple Effects
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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).
Canonical Specification Origin
The architectural trap where application logic, prompt engineering, and data pipelines are heavily coupled to a specific proprietary AI provider, preventing migration when costs rise or performance degrades.
Corpus Interconnections
Richard Ewing artifacts developed around this canonical framework, including publications, execution tools, and diagnostic models.
External Adoption & Peer Citations
Documented instances where independent researchers, engineering teams, and publications have cited, implemented, or referenced this concept outside Richard Ewing’s ecosystem.
External Evidence: No independently verified references recorded yet.
This concept is part of Richard Ewing’s original baseline canon. External citations and implementations are added only upon rigorous empirical verification.
Inspectable Evidence Ledger
Classified evidence items supporting, extending, or refining this canonical research specification.
| Evidence Item | Publisher | Evidence Type | Strength | Role | Action |
|---|---|---|---|---|---|
| The Negative-Carry 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}
}