Avoiding vendor lock-in with AI models: The business case for multi-model APIs

Vendor lock-in in AI models means that an application is so tightly bound to a single AI provider that a switch becomes technically complex and economically risky. This currently affects significantly more companies than two years ago, because AI applications are increasingly running in real business processes instead of pilot projects.

Table of Contents

• Why the risk is increasing right now

• How to recognize vendor lock-in

• What vendor lock-in actually costs

• The business case for a multi-model architecture

• How lock-in can be avoided in practice

• FAQ

• Conclusion

Key Takeaways

• Vendor lock-in in AI is rarely a contractual issue, but rather an architectural one: How deeply embedded is a provider in the code?

• The costs do not arise when switching, but rather from the inability to switch: worse pricing, lack of failover, blocked compliance requirements.

• According to a16z, around 37% of companies already run five or more models – single-vendor is no longer the standard case.

• This article provides the economic case; the technical implementation is covered in the article on the multi-model API.

Why the risk is increasing right now

In recent years, AI has moved from the testing phase into productive operations. Customer support, document processing, and internal tools now depend on real business processes. As long as AI was just an experiment, losing access to a model was a nuisance. Today, it can bring a process to a complete standstill: A much-cited example is the ChatGPT outage in January 2025, which affected GPT-4, 4o, and Mini models and hit companies that had relied exclusively on a single provider without a fallback option.

At the same time, the market for language models is developing very rapidly. New, more powerful, or cheaper models appear at short intervals. Anyone who is tightly bound to one provider misses out on this development because a switch would mean rewriting large parts of their own application.

Thirdly, regulation is playing an increasingly important role: The transparency obligations of the EU AI Act under Art. 50 have applied since August 2, 2026; the high-risk obligations under Annex III were postponed to December 2, 2027 by the Digital Omnibus. Both require documented data processing and origin. If an application is inextricably interwoven with a single, often non-European provider, it becomes more difficult to meet these requirements cleanly and verifiably.

How to recognize vendor lock-in

A few signs indicate an existing lock-in risk:

• All productive AI applications run exclusively through a single provider.

• A switch would mean redeveloping major parts of the application instead of just changing a configuration.

• There is no documented plan for what happens if this provider increases prices, changes terms of use, or temporarily fails.

• No one in the company really has an overview of which model is used in which process.

If two or more of these points apply, it is worth taking a closer look at your own architecture. Awareness of this risk is already high in practice: In a current market analysis (Trantor, 2026), 81% of surveyed business leaders stated they were concerned about dependency on individual AI providers, while at the same time only 6% believe they can switch their primary AI provider without significant operational disruption.

What vendor lock-in actually costs

The costs of a forced provider switch are rarely visible from the start, but become very clear as soon as the switch is required. According to an analysis by Kellton (2025), platform migrations cost 57% of surveyed IT leaders who already had to perform such a migration more than one million US dollars. The SaaS and AI cost management company Zylo quantifies the average hidden additional AI costs per organization at $384,500 per year and observed a 108% increase in AI costs for 2025, with 78% of surveyed IT leaders experiencing unexpected additional costs in the AI area.

Market trends support this: Menlo Ventures, "2025 Mid-Year LLM Market Update" documents how the shares of leading providers shifted significantly within half a year, while a16z, "How 100 Enterprise CIOs Are Building and Buying Gen AI" (2025) shows that around 37% of companies use five or more models productively and deliberately mix providers based on cost, capabilities, and risk. Those who hard-wire their applications today lose exactly this agility. What an interchangeable architecture looks like in practice is described in the article on the Multi-Model API.

The Business Case for a Multi-Model Architecture

Moving away from a single, fixed provider lock-in is initially a technical topic, but it has very concrete business implications:

• Negotiating Position: Those who are technically capable of switching providers negotiate from a much stronger position when it comes to prices or conditions.

• Cost Control: Tasks can be assigned to the most cost-effective suitable model, instead of running everything through a single, potentially more expensive model.

• Reliability: If a provider fails or is temporarily restricted, an application can automatically fall back on another model instead of coming to a complete standstill.

• Regulatory Flexibility: If requirements for data processing change or a new regulation comes into force, it is possible to react faster if the application is not bound to a single provider.

A second, often overlooked lock-in factor is the legal situation: Anyone bound to a provider without EU data residency cannot react to new compliance requirements without modifying the architecture. What paths are available is shown in Using OpenAI, Claude and Gemini in Europe.

How lock-in can be avoided in practice

The most important step is an abstraction layer between your own application and the individual model providers, usually in the form of a central API or a Gateway. The application then only talks to this layer, while in the background it is decided which model actually processes the request.

It is important to anchor as few provider-specific functions as possible directly in the application. Where special functions of a single provider are indispensable, they should be clearly isolated so that the core of the application remains independent.

Just as important is the organizational side: Contracts with AI providers should contain clear rules on data portability from the start, and there should be a regular assessment of how high the switching costs actually are at any given time.

Comparison: Vendor Lock-in vs. Multi-Model Architecture

FAQ

What is the difference between vendor lock-in and a conscious provider decision? A conscious decision for a provider is not a problem as long as a realistic path to switch exists. Lock-in only occurs when switching has become practically impossible from a technical or economic standpoint.

What are the typical costs of a forced provider switch? According to Kellton (2025), platform migrations cost more than one million US dollars for 57% of affected IT leaders, depending on the scope and degree of provider lock-in.

Is a multi-model API alone enough to avoid lock-in? It is the technical foundation, but it must be supplemented by organizational measures, such as clear contract clauses on data portability and a regular assessment of actual switching costs.

Does vendor lock-in only affect large companies? No. Smaller teams are often more heavily affected because they rarely have dedicated resources to handle a migration at short notice if a provider increases prices or fails.

What role does the EU AI Act play in lock-in? The transparency obligations under Art. 50, which have applied since August 2, 2026, and the high-risk obligations starting on December 2, 2027, require documented data processing. An application that is inextricably linked to a single, often non-European provider will have a harder time providing this evidence than a provider-independent architecture.

Conclusion

Vendor lock-in in AI models is not a purely technical detail, but a strategic risk that becomes more important with increasing dependency on AI in business processes. The effort to rely on a provider-independent architecture from the start is significantly lower than the cost of a forced switch under time pressure.

Kontinent was developed precisely for this problem: an interface that leaves companies the choice of model without tying them to a single provider. Anyone wishing to assess their own lock-in situation can get in touch with our team for a non-binding consultation.

Sources

• a16z, "How 100 Enterprise CIOs Are Building and Buying Gen AI" (2025)

• Menlo Ventures, "2025 Mid-Year LLM Market Update"

• Regulation (EU) 2024/1689 (AI Act)

• Kong Learning Center, "Vendor Lock-In in the Age of AI" (ChatGPT outage from January 23, 2025)

• Kellton – 57% of IT leaders spent over $1M on platform migrations · Trantor · Zylo, "2026 SaaS Management Index"

As of: August 27, 2026 · kontinent.ai.