Summarize

Relying on a single GenAI provider can lead to rising costs, service disruptions, compliance challenges, and limited innovation. Businesses that use multiple models or open-source alternatives are better protected and more adaptable. A flexible AI strategy, built on best practices, model diversity, and data control, helps reduce risk, maintain continuity, and support future growth.

--------------------------------------------------------------------------------------------------------------------

Generative AI (GenAI) is evolving rapidly. Every few months, big companies release better, faster, and cheaper models. OpenAI’s GPT-4o, Anthropic’s Claude 3, and xAI’s Grok-4 are just some of the latest names making headlines.

For businesses, this is exciting, but it also brings hidden risks. Relying too much on one AI provider can leave your company exposed to rising costs, service outages, compliance issues, or slow innovation. Model dependency is a growing concern that often goes unnoticed until it’s too late

This blog breaks down the risks of depending on a single GenAI provider and shows how to build a more flexible, reliable AI strategy.

https://blogimagesynoptix.blob.core.windows.net/images/Model Dependecy -1.jpg

The Hidden Risks of Depending on One GenAI Provider

Price Increases

Many AI providers have lowered their prices recently. In early 2024, OpenAI cut the cost of GPT-4 Turbo by around 70%, and Google made big reductions to its Gemini 1.5 API costs by 78%.

These price drops look great at first, but once your business is tied to a provider’s tools, it becomes hard (and expensive) to switch. That gives the provider room to raise prices or add hidden fees later, like charges for moving data in or out of their system.

Service Outages Can Disrupt Your Business

No GenAI provider can promise that their service will always be online. For example, in June 2025, OpenAI had a major outage that lasted several hours. During that time, ChatGPT and GPT-4o were completely unavailable.

Many businesses that relied only on OpenAI couldn’t run their AI tools. This caused delays, lost productivity, and even damaged our reputation.

If your system depends on just one model, and that model goes down, your entire operation can stop. That’s why it’s important to have a backup AI model directory in place.

Regulatory and Compliance Risks

AI laws around the world are becoming stricter. For example, the EU has passed the AI Act, and many countries now have rules about where data must be stored and how it can be used.

If a GenAI model is hosted in another country like the U.S., it might not follow local privacy laws such as GDPR or HIPAA. This can be a serious problem for industries like finance and healthcare.

Being locked into one AI provider makes it harder to stay compliant. But if your AI setup is flexible, you can choose where your models run, whether that’s in a specific region, on your own servers, or in a private cloud, to meet legal requirements more easily.

Innovation Can Slow Down

If your business relies on just one AI provider, you’re limited to whatever features and updates that the provider decides to work on. If they stop focusing on something important to your business or don’t improve fast enough, you could fall behind competitors using other, more advanced tools.

Relying on one provider also means there’s less pressure on them to keep improving. This can slow down innovation in the areas that matter most to your goals.

https://blogimagesynoptix.blob.core.windows.net/images/Model Dependecy -2.jpg

Real-World Lessons for Enterprises

Instacart’s Smart Multi-Model Setup

Instacart’s AI assistant, “Ask Instacart,” doesn’t rely on just one model. It uses both OpenAI’s GPT-4 and its own custom-trained models, choosing the best one for each task. This setup helps them avoid being locked into one provider, save money, and keep performance consistent.

The Impact of the OpenAI Outage

When OpenAI went down in June 2025, many companies that depended only on its API were caught off guard. They couldn’t switch quickly and had to pause key operations. On the other hand, businesses using multiple models stayed up and running by switching providers right away.

AI Lock-In Is a Bigger Risk Than It Seems

One UK company said using just one AI provider feels even more limiting than traditional cloud services. When you build everything your data, prompts, and workflows around one model, switching becomes difficult, expensive, and time-consuming. That makes it even harder to stay flexible as your needs change.

How to Build a Flexible GenAI Strategy (So You’re Not Stuck with One Provider)

To avoid the risks of relying on a single GenAI provider, businesses should build systems that are flexible and able to adapt quickly. Here’s how:

Use Multiple Models

Set up your systems so they can work with more than one AI model. Tools like LangChain, Semantic Kernel, or your own custom setup can help route each task to the best model based on cost, speed, or reliability.

Some companies already use 5 to 10 models at a time. That way, if one model goes down or gets too expensive, they can switch instantly without any major issues.

Try Open-Source AI Models

Open-source models like Llama 3, Mistral, or Nous Hermes are free to use and can be run on your own servers or in private cloud environments.

Benefits include:

Full control over data privacy and security
No per-use fees
Easier to customise for your industry or business needs
Can be run offline for extra security

This is especially useful in areas like healthcare, finance, and government, where data rules are strict.

https://blogimagesynoptix.blob.core.windows.net/images/Model Dependecy -3.jpg

Follow GenAI Best Practices (Also Called LLMOps)

Think of managing GenAI like managing any other software system. Good practices include

Track performance and costs to catch issues early
Use modular workflows (like RAG) so your logic isn’t tied to just one model
Store and manage prompts like code, which makes them reusable, easier to test, and easier to move to new models if needed

This kind of setup makes it faster and safer to adapt as your AI needs to grow or change.

Flexibility Is the Smarter Choice

Generative AI is helping businesses work faster and smarter but relying on just one AI provider comes with risks. It can slow you down, limit your options, and make it harder to stay in control.

The better approach is to stay flexible.

By using multiple AI models, exploring open-source tools, and following best practices for managing AI systems, businesses can reduce risk and stay ready for whatever comes next.

Need help getting started? Explore AI Consulting, open-source options, or multi-model setups with expert guidance.

Avoiding the Pitfalls of GenAI Model Dependency: Why Every Enterprise Needs a Plan B