By Deborah C. Mowry

If you run Gen-AI in production, lock-in isn’t a theory; it’s an impending problem. Below are recent, verifiable incidents and patterns we see with enterprise teams, plus a practical “self-audit” to stress-test your stack.

What “lock-in” looks like in 2025

1. Model retirements break things, sometimes by region.
   Azure confirmed retirement dates for older GPT-4 “0613” variants, including a June 6, 2025 retirement, and it’s explicit that availability varies by region. Q&A forums showed that teams in Switzerland North couldn’t find an in-region replacement and Azure wasn’t budging. (Microsoft Learn)
   
   

2. Outages stall roadmaps when you’re locked in
   On June 10, 2025, OpenAI reported a global disruption affecting ChatGPT and some API endpoints. Coverage and status updates tracked hours of elevated errors and latency. Zendesk publicly noted errors/latency in features that depend on the OpenAI engine that day. Perplexity also reported a partial outage the same morning, though they didn’t directly point to OpenAI as the cause. Another example of this risk in reality is mid-August, especially on the 14th, Anthropic had elevated Model/API errors. They resolved the same day, but let it serve as another reminder to design for provider incidents. (The Verge, Zendesk Support, status.perplexity.com, Anthropic History)
   
   

3. Big vendors are designing their systems for lock-in.
   Forrester warns large enterprise vendors are using AI to deepen platform lock-in and end discounting for enterprises with pricing increases often appearing around contract renewal time. Their survey shows that major vendors are “keenly aware” of the monumental organizational effort associated with switching vendors. (Computer Weekly, The Register, Forrester)
   
   

4. Licenses and terms shape your options.
   Meta’s Llama 3 license restricts using their outputs to train other models.  This is a smart move on their part because if you have the technical expertise and budget to migrate off the model by leveraging your outputs to retrain a cheaper model, or even if you’re just using Llama 3 for synthesized data pipelines, there are legal and contractual constraints preventing that. It’s another example of lock-in being part of the design. Let’s note that Llama 3 restricted this. Llama 3.1 allows it. (Llama)
   
   

5. Pricing moves fast.
   Google cut Gemini 1.5 Pro API prices by ~50–64% (approximate range across SKUs and tiers) on certain prompt sizes. OpenAI launched 4o-mini at $0.15 / $0.60 per 1M input/output tokens. If you were locked into one provider, you couldn't arbitrage these drops. (Google Developers Blog, Master Concept, OpenAI)
   
   

6. Vendors, even well-funded ones, can fail.
   Builder.ai, once backed by Microsoft and valued over $1B, entered insolvency in May 2025. Contingency planning matters. Anurag Gulati (a startup founder from Bengaluru) was mid-project when progress froze. After investing around $7,000, he pulled the plug and hired his own dev team to rebuild. (TechCrunch, Financial Times, Rest of World)
   
   

7. Legal uncertainty is now a platform risk.
   A May 13, 2025 preservation order in New York Times  v. OpenAI compels OpenAI to retain consumer ChatGPT and most API output logs going forward.  OpenAI complied, but did not inform their users until July 5th, 2025.  OpenAI says Enterprise and Edu workspaces, and API traffic under Zero Data Retention are not included in the scope of the data that will be retained.  Access is limited to a small, audited legal and security team. If you rely only on consumer or standard API tiers, treat this as a governance exposure. (OpenAI, Reuters, The Verge)

Governance failures are lock-in too

Lock-in is more than data ownership or pricing. It’s also about who controls your governance story. If a vendor dictates retention, access, and policy, your compliance status is at the mercy of their choices.

• Samsung’s internal ban (2023): Engineers pasted proprietary code and meeting notes into ChatGPT for debugging. That data was stored outside Samsung’s perimeter, triggering an immediate company-wide ban on generative AI tools. This wasn’t a model outage, but a governance failure with no redaction, no outbound-data policy, and probably no training on healthy AI practices. The entire team lost access to AI overnight. (Reuters)
  
  

• Regulations evolve: With the EU AI Act in force, models will face tiered risk classifications. We should acknowledge that more requirements might be added in the future as humanity continues to gain awareness of the risk AI poses with more experience, information, and perspective. What if an amendment is made to the act? If your company’s only approved provider doesn’t meet the new compliance for your category (say, medical or financial use), what is your plan?. 
  
  

• Legal holds: As we’ve seen in the New York Times v. OpenAI case, courts can mandate data retention on consumer or API logs. If you don’t control where your prompts are stored, your compliance strategy is tied directly to whatever lawsuits and regulations they get tangled up in.

Sidebar — Host risk ≠ model lock-in (and why we plan for both).

Neutral platforms like Hugging Face are great for experimentation, but if your production app stores long-lived API keys or runs only in a third-party “Space,” you are impacted by that platform’s crises. In 2024, Hugging Face revealed an intruder got into Spaces, putting stored tokens and API keys at risk. Our approach with Fusion Business is to keep primary credentials and org memory in your cloud, route model calls through your gateway with short-lived tokens, and maintain multi-model failover. The goal is the same as avoiding model lock-in: no single external choke-point can stall your roadmap. (Hugging Face)

A 60-second self-audit for CTOs

• Failover: Can you re-point 80% of prompts to a second model within 1 day?
  
  

• Region matrix: If your region loses a model, do you have an in-region fallback? (Map residency + availability.) (Microsoft Learn)
  
  

• Org memory: Are prompts, embeddings, and logs stored outside your vendor’s account (in your cloud)?
  
  

• Contracts: Do you have written commitments on deprecations/notice, rate limits, and pricing stability? (Microsoft Learn)
  
  

• Licenses: Do model licenses or API terms restrict synthetic-data use or migration paths you depend on? (Llama)

  
  

• Cost capture: When prices drop or a cheaper tier launches, can you route to it without rewrites? (OpenAI, Google Developers Blog)

If any answer makes you uneasy, you’re not alone. The goal isn’t perfection; it’s optionality on demand.

Why self-hosting matters

When you run models in your own cloud or perimeter:

• You decide retention. Logs, prompts, and embeddings stay in your tenancy, not a vendor’s.
  
  

• You define policy. Apply DLP, redaction, or guardrails that match your regulatory environment.
  
  

• You isolate risk. A third-party breach (like Hugging Face’s) doesn’t expose your production secrets.
  
  

• You can prove compliance. Auditors see your logs, not whatever slice a vendor provides.

Governance is leverage. The teams that can show regulators, boards, and customers exactly where data flows (and prove they can swap providers without policy drift) are the ones that stay ahead.

Patterns we recommend

• Decouple: Put a control layer in front of models so prompts/tools don’t depend on a single provider’s SDKs or quirks.
  
  

• Prompt/Embedding Vault: Keep your prompt templates, embeddings, and chat history in your cloud (not the model provider’s tenant).
  
  

• Region + Vendor Matrix: Treat region/model availability as a living matrix; assume variance by cloud and plan failovers.
  
  

• Cost Router: Define a quality target and route to the cheapest model that meets it; revisit as prices shift.
  
  

• Contractual Guardrails: Bake in deprecation notice, export formats, and SLA/SLOs for rate limits and incidents.
  
  

• Secret Hygiene: Don’t let vendor-hosted apps hold the crown jewels; rotate keys and reduce risk.

How Fusion Business helps

• Vendor & model agnosticism: Swap between OpenAI (4o/4.1/mini), Anthropic (Claude 3.x), Google (Gemini 1.5/2.5), and top OSS without rewriting prompts.
  
  

• Your cloud, your memory: Prompts, embeddings, chat history, and logs live in your cloud for portability and compliance.
  
  

• Cost optimizer: We route to the lowest-cost eligible model that hits your quality/latency targets and honor region constraints as needed.
  
  

• Admin & guardrails: Centralized access, audit logs, and policy management aligned to your governance model.

Our Offer: Book a 30-minute discovery call. You’ll walk me through your stack and a workflow you’d like to have run agentically. We come back with a personalized demo at your own URL with the agentic workflow ready to go and a free 30-day PoC on your data.

→ Grab a time (20 min): Book

Case notes you can forward

• June 10, 2025—OpenAI incident: Elevated errors/latency across ChatGPT and some APIs; third-party products (e.g., Zendesk AI features) reported user-visible errors; Perplexity posted a partial outage. If your assistants can’t fail over, your roadmap stops. (The Verge, Zendesk Support, status.perplexity.com)
  
  

• Azure GPT-4 0613 retirement: Documented retirement dates and region-specific availability profiles—teams bound to specific regions faced abrupt re-architecting. Retirements require notice, but notice isn’t a drop-in replacement. (Microsoft Learn)
  
  

• Anthropic elevated errors (Aug 14, 2025): Model/API errors resolved same day, another reminder to design for provider incidents. (status.anthropic.com)
  
  

• Hugging Face Spaces secrets breach (2024): Unauthorized access to app secrets; tokens revoked. Third-party hosting is convenient until it isn’t. (Hugging Face)
  
  

• Forrester on pricing/renewals: Large vendors are ending discounts and bundling AI to deepen lock-in; renewal is when terms change. Plan leverage in advance. (Computer Weekly, The Register)

  
  
  Sources & further reading

• Model availability & retirements (Azure): Retirement policy & timelines; region variability; retired models list. (Microsoft Learn)
  
  

• OpenAI outage (June 10, 2025): Round-ups and vendor status notes (OpenAI, Zendesk, Perplexity). (The Verge, Zendesk Support, status.perplexity.com)
  
  

• Forrester on discounting/lock-in: Summaries highlighting tightening renewal dynamics. (Computer Weekly, The Register)
  
  

• Hugging Face Spaces secrets incident (2024): Primary disclosure. (Hugging Face)
  
  

• License constraints: Llama 3 license on training with outputs; OpenAI enterprise data/ownership. (Llama, OpenAI)
  
  

• Pricing shifts: OpenAI 4o-mini pricing; Google Gemini 1.5 Pro reduction. (OpenAI, Google Developers Blog)