Foundational Models in Generative AI: What They Are & How to Choose the Right One

Selecting the right foundational models in the exploding generative AI field is a no-brainer. Enterprises that don’t choose wisely are at a higher risk of high costs, inefficiencies, and security challenges.

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With a host of LLMs (Large Language Models) like GPT-4, Claude, LLaMA, etc., CTOs must navigate through many factors—model architecture, cost, scalability, security and ethical considerations—to choose the right model for their company.

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So, we have decided to break down the selection criteria for foundational models. This will equip you, as a CTO, with the insights to make informed decisions. 

What are Foundational Models in Generative AI?

Generative AI models usually mean foundational models. Foundational models are the large-scale AI models that have been pre-trained on massive data to perform a wide range of tasks like content generation, problem-solving, and much more.

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Google’s multimodal foundation model Gemini —for example—can generalize and understand, operate on and combine different types of information like image, audio, text, code, videos, etc.  

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Thus, these models form the base (foundation) on which specific things can be built. Foundation models can be fine-tuned for a broad range of applications, hence becoming a strong starting point for many business use cases. 

Selection Criteria: How to Choose the Right Foundation Model 

Not all models are created equal. Some are better suited to certain tasks while others may be a better choice depending on your industry. How do you decide on which foundation model to pick? 

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Now, one approach is to just pick the largest, most massive model out there to execute every task. But with large models come costs like compute, complexity and variability. So often the better approach is to pick the right  size model for the specific use case you have.

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When choosing the right foundation model for your enterprise, you must balance factors like governance, use case, performance, data, scalability, security, and cost efficiency. Here’s seven parameters that are key to selecting the right foundational model.

1. Your Specific Use Case 

Even though foundational models are a new category promising revolutionary changes, you still need to know what business problem you want to solve. You will be unable to select the right model without this critical piece of information. 

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Once identified, break down the problem and ask these questions - 

• What exactly are you planning to use genAI for?
• What tasks do you want the model to perform?
• Do these tasks need constant manual intervention?
• Are these tasks too complex for a model? 
• Does the required outcome need to be in a specific format?

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Usually, the foundational models in generative AI are designed to produce entirely new content. So, you must really understand if that’s what you want your model to do. Answering these questions will help you narrow down your model options.

2. Available Foundational Models

Let’s say your company is already using Llama. You need to evaluate the model on its size, performance, and risks. And a good place to start here is with the model card. The model cards may tell you if the model has been trained on data specifically for your purposes. 

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Pre-trained foundation models are fine tuned for specific use cases such as sentiment analysis or maybe text generation. That's important to know because if a model is pre-trained on a use case close to yours, it may perform better. This can enable you to use zero shot prompting to obtain desired results. And that means you can simply ask the model to perform tasks without having to provide multiple completed examples first.

3. Evaluate Performance 

When it comes to evaluating model performance of a model, you should take these 3 factors into account. 

• Accuracy
• Reliability
• Speed

Accuracy denotes how close the generated output is to the desired output, and it can be measured objectively and repeatedly by choosing evaluation metrics that are relevant to your use cases. 

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So for example, if your use case is related to text translation, the B.L.E.U (Bilingual  Evaluation Understudy benchmark) can be used to indicate the quality of the generated translations.

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There are other benchmarks like- 

1. MMLU effectively measures general language comprehension across multiple subjects.
2. HELM assesses bias, fairness, and generalization across diverse AI tasks.
3. GPT-4 excels in general reasoning, while LLaMA models are optimized for efficiency in research and enterprise contexts.

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The second factor—reliability—is a function of several  factors like consistency, explainability and trustworthiness. Also, how well a model avoids toxicity like hate speech. Reliability comes down to trust, and trust is built through transparency and traceability of the training data. 

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And then the third factor is speed. How quickly does a user get a  response to a submitted prompt? Now, speed and accuracy are often a trade-off here. Larger models may be slower, but  perhaps deliver a more accurate answer. Or then again, maybe the smaller model is faster and has minimal differences in accuracy to the larger model.

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The way to find out is to simply  select the model that's likely to deliver the desired output and well, test it.

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Also, check for the domain-specific performance. If you’re in finance, legal, or healthcare, you need models fine-tuned for compliance-heavy environments. Similarly, If your focus is customer experience, choose a model that excels in NLP and conversational AI.

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Suggested Read: How To Know if Your Machine Learning Model Has Good Performance

4. Deployment Options

Suppose you want to go ahead with Llama. It’s an open source model, a public cloud. But if you decide to fine tune the model with your own enterprise data, you might need to deploy it on prem. 

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This means you can have your own version of Llama and can fine tune it. This will give you greater control, and more security compared to a public cloud environment. But it's an expensive proposition due to the huge number of GPUs.

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So, the main question is which option would you choose. A quick snapshot of cloud based vs on prem deployment. 

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5. Fine tuning capability 

A one-size-fits-all model rarely works. You’ll need to fine-tune for domain-specific tasks and understand whether the model supports fine-tuning or not. 

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Open source models like LLaMA allow extensive customization. These models enable users to fine-tune them according to specific needs. This leads to better performance on targeted tasks. 

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Whereas, other foundational models such as GPT-4 and Claude offer API-based access with limited fine-tuning capabilities. While they can be adapted to some extent, the depth of customization is not as extensive as with open-source models. Fine-tuning in these models is limited to how the model behaves rather than modifying its core structure. 

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So, you must carefully consider these models before choosing the appropriate one for your specific needs.

6. Cost and Compute Requirements

If you want to run large-scale foundational AI models, then it will require substantial computational resources. Models such as GPT-4, Claude, and PaLM need access to high-end GPUs (like A100 or H100) or TPU clusters for efficient processing. This requirement can lead to significant operational costs, especially when scaling up for extensive applications. 

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But If you choose to run fine-tuned models in-house, you must invest in AI-optimized cloud instances from providers like AWS, Azure, or Google Cloud Platform (GCP). This investment is important for maintaining the performance and efficiency of the models during deployment. Smaller foundational models like GPT-3.5 Turbo, Mistral 7B, LLaMA 2-13B, etc. offer competitive performance while lowering compute costs by 50% or more.

7. Governance 

Governance frameworks help companies navigate complex regulatory landscapes, such as the GDPR and the EU AI Act. These regulations impose strict requirements on data handling, privacy, and accountability, which directly impact the choice of foundational models. 

Data security and privacy

For companies handling sensitive data (healthcare, finance, government), security and compliance should be top priorities. API-based models (GPT-4, Claude) may store query logs for training. So, you must opt for those foundational models that allow self-hosting or have clear data policies. This is to protect against the misures of sensitive data. 

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Select the generative AI models by providers that adhere to required compliances - 

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• GDPR & CCPA: Protects user data and mandates consent for processing.
• HIPAA: Critical for healthcare applications.
• SOC 2 & ISO 27001: Key for enterprise-grade security frameworks.

Ethical Considerations

Governance frameworks help companies choose the right foundational models in AI by setting ethical guidelines for AI deployment. Here’s how - 

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• By mitigating bias - AI models trained on diverse and balanced datasets are less likely to reflect unfair biases. Governance encourages the use of these models to prevent AI from reinforcing existing inequalities.
• Fairness and Inclusivity - Companies prefer those models that ensure fair treatment for all users, regardless of background. Ethical governance pushes for AI that delivers equitable and unbiased decisions.

By factoring in the above characteristics of the foundational models, you can make the appropriate choice for your company. 

Real-World Use Cases of Foundation Models in Generative AI 

Generative AI Foundation models have been increasingly integrated into many companies  across multiple industries. Here are some noteworthy examples that show how they are using these models to enhance their operations and improve efficiency.

Amazon

Amazon has introduced Amazon Nova, a new generation of foundation models capable of processing text, images, and video. These models are designed to simplify tasks for both internal and external users. For example, this new model can generate videos and multimedia content. The models support a wide range of tasks across 200 languages and are customized for specific customer needs through fine-tuning with proprietary data. 

Salesforce

Salesforce launched Einstein GPT, a generative AI product that enhances CRM by automating content generation based on customer data. This tool helps businesses to create personalized marketing materials, automate email campaigns, and generate code for app development. significantly improving operational efficiency and customer engagement. 

Microsoft

Microsoft has integrated generative AI into its products, notably through Copilot, which assists users in various applications, including coding and content creation. Copilot helps streamline workflows by providing context-aware suggestions. Additionally, Microsoft has embedded generative AI capabilities into its Azure cloud services, enabling businesses to leverage AI for data analysis and application development. 

The Future of Foundation Models in Generative AI: What’s Next? 

By now, it’s clear  ‘what are foundational models in generative AI’ and how to choose the right one. Foundation models or LLMs are becoming smarter and more versatile, learning to process and generate text, images, audio, and video all at once. Yet, there are many things these models can't do by themselves, like understand different forms of inputs.

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This shift toward multimodal AI is opening up exciting new possibilities across different industries. 

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• Healthcare: In medical settings, multimodal AI can analyze patient data that includes text (doctor's notes), images (X-rays, MRIs), and structured data (vital signs) to improve diagnostics and treatment recommendations.
• Content Creation: These models can generate multimedia content, such as creating image captions from text descriptions or producing videos based on scripts, making them valuable tools for creative industries.
• Autonomous Systems: In robotics and autonomous vehicles, multimodal AI integrates data from various sensors (cameras, LIDAR) to navigate and make real-time decisions based on the environment.
• Customer Service: These multimodal systems can analyze text, voice tone, and facial expressions to gain deeper insights into customer satisfaction, enabling advanced chatbots to provide instant support.

Closing Thoughts: What are the Foundational models in Generative AI

Choosing the right foundation model isn’t just about performance—it’s about aligning with business goals, infrastructure, and security. So, when selecting the model don't forget to check out these factors and decide wisely.

At Zams, we help enterprises adopt the right foundational model without the technical burden. Our no-code AI platform enables CTOs to integrate powerful, scalable AI solutions seamlessly.

👉 Book a demo today to see how foundation models can optimize your business operations.