Which Generative AI Partner Fits Your Business?

Among AI newcomers, the most recognized generative AI applications are still OpenAI’s ChatGPT and Microsoft Copilot. But there’s also Claude, Gemini, DeepSeek, Grok, Mistral, and Perplexity — each powered by its own “engine,” or large language model (LLM). You could even build or enhance your own application using an embedded LLM. So how do you choose the generative AI partner that creates the most value for your business in this wild west of LLMs?  

Generative AI: the current landscape  

Two years ago, the performance differences between LLMs were clear. Today, these models are so advanced that you and I can barely tell them apart. They’ve absorbed the knowledge of the internet and can now engage in natural, humanlike conversation. Goodbye Google Search?  

What’s more, nearly all models are now multimodal: they don’t just understand and generate language, but can also code, create visuals, and search real-time information online. Goodbye spreadsheets and Photoshop? And in the blink of an eye — most models now support not only API integration with existing tools but also autonomous interaction with other systems, the so-called agents.  

Three critical differences  

Despite these advances, some core differences remain. These three should matter most to European businesses when selecting their AI partner:  

1. Data server location & jurisdiction
This wasn’t an issue back when global relations were more stable. But in today’s political climate, many organizations feel uneasy storing data on servers subject to U.S. or Chinese law.  

Important: even if a U.S. provider hosts servers in Europe, those servers may still fall under American jurisdiction — allowing intelligence services access under specific conditions.  

If that’s a concern, your options are either open-source models that you can download and host on your own (European) infrastructure, or the one major European LLM provider with its own model and chatbot: Mistral AI.  

2. Training data policy
Each time you interact with an LLM, that interaction may be used to further train the model. Not all providers are transparent about how they handle this data — or where their training data comes from. Usually, paid subscriptions allow you to opt out of data use for training, but it’s essential to check the fine print.  

As for training data sources, open-source providers like Meta (Llama), Mistral, and DeepSeek are generally more transparent than big tech companies such as OpenAI. If this issue matters to your organization, a deep dive into terms and product documentation is strongly advised.  

3. Open source vs. proprietary code 
Just like with software, LLM developers decide whether to keep their models’ code closed or open. This isn’t just about transparency: open-source models offer far more flexibility to adapt to your specific needs — if you have the knowledge and resources to support them. But that freedom comes with responsibility: updates, infrastructure, compliance — all of that becomes your burden.  

Given how fast LLMs evolve, many companies choose proprietary providers like OpenAI. These models may offer less flexibility, but significantly reduce complexity and risk.  

Our advice?  

Also consider pricing models, innovation pace, and what level of performance you truly need. Do you really need a Ferrari, or will a solid four-wheeler take you much further than where you are today? Sound familiar? These are the same questions we’ve asked when adopting cloud solutions, ERP systems, or making software choices. But AI moves even faster, with unclear regulation and no fixed future path.  

Don’t commit too early 
In this rapidly evolving field, it’s smart not to lock yourself in too soon. Don’t rely solely on sales pitches — experiment with different providers to discover what actually works for your organization.  

Balance standard with custom solutions
What’s most likely the wisest strategy? Use more standard applications and LLMs for core processes, and experiment with customization only for the areas that are truly unique to your business. Which provider is best? That depends entirely on your priorities.  

Stay curious and keep learning
Whatever you choose — don’t tie yourself to one provider for the next 10 years. Yes, managing multiple solutions may seem less cost-effective, but in a field that evolves at lightning speed, adaptability wins. Take this time to learn what works, discover what really matters to your business, and identify which generative AI partner can solve meaningful problems.

Curious how leading organizations are leveraging AI to generate real business value? 

Then the Advanced Program Driving Business Value with AI is the perfect next step for you. Immerse yourself in real-world business scenarios where AI and data analytics are the driving forces behind breakthrough decisions.

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The 7 key preconditions to truly create value with AI and data in your organization

Will generative AI take your business to the next level? Casually exploring what AI is and what it can do is a good starting point. But that’s very different from truly assessing whether AI can generate real value for your organization — and what that would look like in practice.

In this article, Frieda van Belle, core faculty member of the Advanced Program Driving Business Value with AI, outlines the seven key preconditions for unlocking the full potential of AI and data in your organization.