Selecting the right foundation 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.
With a host of LLMs (Large Language Models) like GPT-4, Claude, LLaMA, etc., CTOs must navigate through many factors, AI model architecture, cost, scalability, security and ethical considerations, to choose the right model for their company.
So, we have decided to break down the selection criteria for foundation models. This will equip you, as a CTO, with the insights to make informed decisions about AI applications and AI technology adoption.
What are Foundation Models?
Generative AI models usually mean foundation models. Foundational models are the large-scale machine learning models that have been pre-trained on massive input data to perform a wide range of tasks like content generation, problem-solving, and much more.
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., showcasing the versatility of deep learning models in modern artificial intelligence.
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.
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Selection Criteria: How to Choose the Right Foundation Model
Not all AI 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?
Now, one approach is to just pick the largest, most massive large language model (LLM) 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.
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 foundation 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 AI application and generative AI without this critical piece of information.
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 AI 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?
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 AI 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 foundation model has been trained on data specifically for your purposes.
Pre-trained foundation models are fine tuned for specific use cases such as sentiment analysis or maybe text generation, which are common AI applications. That's important to know because if a machine learning 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 foundtation 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.
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.
There are other benchmarks like-
- MMLU effectively measures general language comprehension across multiple subjects.
- HELM assesses bias, fairness, and generalization across diverse AI tasks.
- GPT-4 excels in general reasoning, while LLaMA models are optimized for efficiency in research and enterprise contexts.
The second factor- reliability, is a function of several factors like consistency, explainability and trustworthiness. Also, how well an AI model avoids toxicity like hate speech. Reliability comes down to trust, and trust is built through transparency and traceability of the input data used to train foundation models.
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 machine learner model is faster and has minimal differences in accuracy to the larger model.
The way to find out is to simply select the foundational model that's likely to deliver the desired output and well, test it.
Also, check for the domain-specific performance. If you’re in finance, legal, or healthcare, you need deep learning models fine-tuned for compliance-heavy environments. Similarly, if your focus is customer experience, choose a model that excels in natural language processing (NLP) and conversational AI, two key AI applications in modern AI technology.
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 AI model with your own enterprise input data, you might need to deploy it on-premises.
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, required for training and running deep learning models.
So, the main question is which option would you choose. A quick snapshot of cloud based vs on prem deployment.
5. Fine tuning capability
A one-size-fits-all maching learning model rarely works. You’ll need to fine-tune for domain-specific tasks and understand whether the foundation model supports fine-tuning or not.
Open source models like LLaMA allow extensive customization. These generative AI enable users to fine-tune them according to specific needs. This leads to better performance on targeted tasks.
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.
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. Deep learning 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.
But If you choose to run fine-tuned machine learning 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 input 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 AI 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.
Select the generative AI models by providers that adhere to required compliances:
- 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 foundatio models in artificial intelligence by setting ethical guidelines for AI deployment.
Here’s how:
- 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 technology from reinforcing existing inequalities.
- Fairness and Inclusivity - Companies prefer those foundation 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 deep learning 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 AI 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 based on human language instructions. Additionally, Microsoft has embedded generative AI capabilities into its Azure cloud services, enabling businesses to leverage AI technology for data analysis and application development.
Scaling Generative AI Across Your Enterprise
Successfully scaling foundation models requires a strategic approach that goes beyond technology, it’s about embedding AI into key business processes. Start by identifying workflows where AI can provide immediate impact, such as automating reporting, generating insights from training data, or streamlining cross-team communication. By connecting AI models to existing tools like Salesforce, Slack, or Notion, organizations can reduce manual effort while maintaining operational continuity.
Unlocking Business Value with Foundation Models
While scaling focuses on operational efficiency, unlocking business value emphasizes growth, revenue, and competitive advantage. Foundation models empower entrepreneurs to innovate faster by enabling new products, services, and customer experiences that were previously resource-intensive or impossible. For example, companies can use AI to create personalized marketing campaigns, enhance customer support with advanced natural language processing, or generate high-quality visual content using image generation.
Beyond direct operational improvements, foundation models can inform strategic decisions by uncovering insights hidden in training data, identifying emerging trends, and highlighting opportunities for new generative AI applications. Entrepreneurs who integrate these models thoughtfully can focus on long-term growth, differentiate their offerings, and stay ahead in a rapidly evolving AI-driven market.
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.
This shift toward multimodal AI is opening up exciting new possibilities across different industries.
- 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
Choosing the right foundation model isn’t just about performance it’s about aligning with business goals, infrastructure, and security. So, when selecting the AI model don't forget to check out these factors and decide wisely.
Zams takes the technical burden out of AI adoption. Our no-code platform lets enterprises plug in foundation models seamlessly, powerful, scalable, and ready to drive results across diverse generative AI applications.
As multimodal models and large foundation models evolve, combining deep learning models with specialized machine learning models will be key for enterprises aiming to scale efficiently and stay competitive.
FAQ
1. What are foundational models in generative AI?
Foundational models are large-scale AI models pre-trained on massive datasets to handle a wide range of tasks, such as text generation, image creation, and problem-solving. They form the base for fine-tuning specific applications, making them a critical choice for enterprise AI strategies. Because they are designed to generalize across multiple domains, foundation models are a critical choice for enterprise AI technology strategies and help businesses leverage generative AI capabilities efficiently.
2. How do I choose the right foundation model for my sales team?
For sales teams, the right foundation model depends on your goals whether that’s automating CRM updates, analyzing customer interactions, or generating personalized outreach. Consider a large language models (LLMs) or a deep learning model fine-tuned for natural language processing (NLP) if you need advanced lead scoring or real-time sales insights, or automated email generation.
3. What’s the difference between a deep learning model and a machine learning model in this context?
Machine learning models are often designed for narrower, task-specific outcomes, while deep learning models especially large foundation models can generalize across many domains due to their complex neural network architecture and pre-training on massive datasets. Deep learning models are particularly useful for enterprises looking to implement multiple AI applications from a single foundation model, including generative AI capabilities like content creation, image generation, and predictive analytics.
4. When should B2B sales teams consider multimodal models?
B2B sales teams should explore multimodal models when dealing with multiple types of input data, such as analyzing text-based conversations, video calls, and customer sentiment together. These foundation models can help prioritize leads, personalize pitches, and predict buying intent more accurately.
5. What factors affect the cost of using large foundation models?
The cost depends on model size, compute requirements, and deployment choice (cloud vs on-prem). Large deep learning models require high-end GPUs and significant processing power, which can increase operational costs. Smaller, fine-tuned AI models offer a cost-effective alternative while still providing strong performance for specific AI applications, such as sales analytics, content generation, or lead scoring. Generative AI capabilities can be scaled according to your enterprise’s infrastructure and business needs, making it important to balance cost with performance and deployment flexibility.
