What Are Foundation Models? Types, Training, Examples & Uses

Quick Overview:

• Foundational models are pretrained AI models which are trained over large datasets and are used for multiple works like content creation, programming, and so on.

• Developing a foundation model consists of gathering and cleaning the dataset, selection of architecture, training a model, maybe fine-tuning, instructions tuning, evaluation and finally deployment of the model.

• Common types include language, computer vision, image generation, audio, video, and multimodal models. 

• Some of the examples of such models are GPT, Gemini, Claude, Llama, Gemma, Imagen, Veo, and Stable Diffusion.

• In this blog you will learn foundational models in detail along with their working, training, types, popular models, applications and difference between foundational model and LLM.

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What Are Foundation Models?

A‍‍‍ foundation model in AI essentially is a model that is pre-trained with a huge quantity of data which can be used for different tasks. Either it can be run directly, with prompts or one can even do the adaptation of it using methods like fine-tuning to get the best model.

In simple terms, the foundation model meaning an AI model that is used as a starting point for building multiple AI applications.

As an example, the foundation model can be used to create chatbots, summarization tools, coding assistants, and document analysis systems. It does not require the developer to create a new model for every kind of application.

In fact, they have gained so much popularity that they are rapidly becoming an important part of AI research. According to 2025 AI Index Report from Stanford University, researchers released 149 foundation models in 2023 which is more than double the number from the previous year ‍‍‍2022.

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Characteristics of Foundation Models

Foundation models have a number of properties which differentiate them from models trained for a particular, narrowly defined task. 

Below are some of these characteristics of the foundational models:

Now after understanding the basics of foundational models, now let’s understand how these models are trained.

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How Are Foundation Models Trained?

Training a foundational model involves collection of data, selection of a suitable architecture, pretraining of model, adapting it for useful tasks and evaluation of its performance. 

Let’s break down the exact process below:

1. Data Collection and Preparation

A major task before any model training is to collect and prepare the data so that it's exactly what's needed for the training. While a language model would be fine with text or code, a multimodal one may require text with images, videos, and audios.

Then the developers clean data by removing duplicates, errors correction, and filtering unsuitable material.

For example, a DeepMind project called Chinchilla was training on 1.4 trillion tokens and this gives us a clue that data is one of the main factors determining the success of a large language model.

2. Choosing the Model Architecture

Then, the developers have to select one architecture based on the type of data and tasks that the model have to perform. They can use architecture such as:

• Transformers: Commonly used for language processing and many multimodal tasks.

• Diffusion models: Commonly used for image generation.

• Other architectures: Used for specialized tasks involving particular data types.

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3. Pretraining the Model

In the process of pretraining, the model is able to understand general patterns in a large dataset. Language models learn by predicting the next token or missing parts of the text.

For example, Google’s PaLM language model contained 540 billion parameters and was trained on 768 billion tokens using 6,144 TPU v4 chips.

4. Fine-Tuning for Specific Tasks

After the pretraining phase, the developers can also fine tune the model which adjusts the model parameters.

This is optional because developers can also use prompting or other methods to adapt a pretrained model.

5. Instruction Tuning and Alignment

Instruction tuning aims at the model to learn how to respond to the instructions that have been given to it. Alignment method can help the model to prefer certain responses and thus limit undesired ones.

Example: The model learns to respond to "Explain our refund policy" with a clear explanation instead of generating unrelated information.

6. Evaluation and Deployment

Before they come out with the actual product the developers check the model's abilities in different ways: correctness, logical abilities, safety, prejudices, bias and efficiency in the tasks it's supposed to handle.

Example: A customer support model can be tested on questions about refunds, delivery delays, product returns, and account issues.

Training large models also requires substantial resources. Stanford's 2025 AI Index Report estimated that the computing cost of training GPT-4 was around $79 million, based on cloud computing rental prices. This is an estimate, not the model's full development cost.

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Types of Foundation Models

There are different types of foundation models depending on the process and the task they support. Below are the common types of foundational models:

1. Language Foundation Models

Language foundation models learns from the  patterns in text and code. A single model can support 3 tasks, such as summarising a report, answering questions, and translating text.

Example: You can upload a 20-page report and ask the model to summarise it in 5 key points.

2. Computer Vision Foundation Models

Computer vision models process the images and visual information. They can identify objects, classify images, and analyze the scenes.

Example: A retail application can use a vision model to identify 3 products in a photograph and separate them from the background.

3. Image Generation Models

Image generation models creates the images from text prompts and other inputs. You can describe a scene, specify its style, and generate visual options.

Example: A designer enter 1 prompt describing a modern electric car and generates 3 different design concepts.

4. Audio and Speech Foundation Models

Audio and speech models processes the spoken language and other audio signals. They are able to covert speech into text, translate the conversations, and generating the spoken responses.

Example: A 10-minute meeting recording can be converted into text and organized into 3 sections: key discussion points, decisions, and action items.

5. Video Foundation Models

Video foundation models are able to generate and analyze the moving visual content. They can create videos from text prompt or images.

Example: A marketer enters 1 prompt describing a new product and creates a short video with 3 scenes showing its design, features, and use.

6. Multimodal Foundation Models

Multimodal foundation models work with 2 or more types of data, including text, images, audio, and video. They can combine information from different inputs to answer questions or generate content.

Example: You upload 1 chart, ask 2 questions about its trends, and request a 3-point explanation in text. The model uses the visual information to prepare its response.

Now let’s explore some of the popular foundational models of the types we discussed above with their capabilities.

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Popular Foundation Models and Their Capabilities

Several organizations have developed foundational models for different tasks. Some of the most popular models are:

There is a common confusion among the difference between foundation models vs LLM. So now let’s explore the key differences between both.

Foundation Models vs Large Language Models

Large language models are a subset of foundation models. They focus on language-related tasks, while the broader foundation model category also includes models built for vision, audio, image generation, video, and multimodal applications. 

The key difference between these are:

After exploring a lot about the foundational models in AI, let’s take a look at some of the real world applications of these models.

Applications of Foundation Models

Foundation models are used in wide range of applications such as customer support, healthcare, finance, etc.

Let’s explore each of the applications in detail:

• Customer Support: Develop chatbots, assist customers with their questions or requests and summarize conversations.

• Healthcare: Extract and summarize medical information, interpret and explain medical imaging supported with the foundation model.

• Software Development: Write code, find and fix software bugs, write program manuals.

• Education: Explain concepts, summarize lessons, and create practice questions.

• Marketing and Media: Produce text, create pictures, and develop a wide variety of content creatively by the AI.

• Finance: Examine a lot of different kinds of financial documents, for instance, the bank statements or budget plans, or summarize the financial ‍‍‍reports.

Conclusion

As of now, it is clear to you that foundational models are a kind of reusable starting point for developing an AI application. They learn from the general patterns, from large datasets and can be adapted for language processing, computer vision, multimodal tasks etc.

Understanding how foundation models are trained and customised helps you to select an appropriate model for a particular application.