The buzzword for 2025 is AI Agents and using generative AI for quicker workflow.

For SaaS and tech-centric companies, this means using it alongside your tech stack in the most effective way possible 

Odds are you have a full team of developers and are still barely meeting project deadlines - using generative AI can remove this issue and we’ll show you how.

What is Generative AI?

Generative AI refers to a category of artificial intelligence designed to create new content, such as text, images, music, or code. It works by training models on vast datasets and using patterns from that data to generate outputs.

Examples include text-based models like GPT and image generators like DALL·E. These tools have applications across industries like healthcare, entertainment, finance, and customer support.

Companies like Mckinsey went so far as to state generative AI could add the equivalent of $2.6 trillion to $4.4 trillion annually to companies (in 63 use cases they looked at)

Frameworks for Generative AI

Generative AI relies on several frameworks, tools, and programming environments to function effectively. These include deep learning frameworks, data preprocessing tools, programming languages, and deployment platforms.

These tools simplify complex processes and amplify the potential of AI in tech industries by enabling faster innovation, smarter automation, and deeper insights. As generative AI reshapes industries, these frameworks are driving breakthroughs in creativity, problem-solving, and efficiency.

1. Deep Learning Frameworks

Deep learning frameworks are the building blocks for training and fine-tuning AI models. They help developers create smarter algorithms that can solve problems faster and more efficiently.

By powering applications like virtual assistants, autonomous vehicles, and content-generation tools, these frameworks are revolutionizing industries. Some standout frameworks are:

• TensorFlow: A widely used library for building and deploying machine learning models.
• PyTorch: Known for its flexibility and user-friendly interface, PyTorch is favored by researchers and developers alike.
• JAX: A library optimized for high-performance machine learning, offering automatic differentiation.

Each framework provides tools for designing, training, and optimizing neural networks tailored to specific use cases.

2. Data Preprocessing Tools

High-quality data is the lifeblood of generative AI models. Data preprocessing tools help clean, structure, and prepare data to ensure models learn from accurate inputs.

As data becomes more complex and voluminous, these tools are essential for minimizing bias, identifying patterns, and maximizing model effectiveness. Here are some tools that simplify this vital step:

• Pandas: A Python library for data manipulation and analysis.
• NumPy: Essential for numerical operations and array processing.
• Apache Spark: A powerful tool for processing large datasets in distributed environments.

Effective preprocessing ensures that models learn from accurate, high-quality data, reducing the risk of bias or errors.

3. Programming Languages

The programming language used shapes how flexible and efficient generative AI projects can be.  Developers choose languages based on their specific needs, such as scalability, speed, or ease of use.

For many, Python remains the top choice because of its simplicity and rich ecosystem. Other options include:

• Python: The most widely used language for AI due to its extensive libraries and ease of use.
• R: Preferred for statistical analysis and data visualization.
• Julia: Known for its speed, particularly in numerical computations.

4. Deployment Tools and Platforms

Once an AI model is ready, it needs to be deployed seamlessly to serve users in real-world applications. Deployment tools and platforms streamline this process, ensuring performance, reliability, and scalability.

These tools are pivotal in delivering AI solutions that adapt to user demands and scale efficiently. Some top picks are:

• Docker: For containerizing applications, ensuring consistency across environments.
• Kubernetes: Ideal for scaling AI solutions and managing workloads.
• AWS, Azure, and Google Cloud: Cloud platforms that provide scalable infrastructure and pre-built AI services.

What are the Different Generative AI Tech Stack Layers?

A well-structured generative AI tech stack is organized into three primary layers: the infrastructure layer, the model layer, and the application layer.

A lot like a layered cake, each part builds on the other to create something powerful. Each layer plays an essential and distinct role in the AI lifecycle adding value to your tech stack.

The Infrastructure Layer

The infrastructure layer is the foundation of generative AI. It powers the complex computations needed to train and run models effectively, ensuring they can scale as demands grow.

Today, generative AI has changed this layer by making hardware more efficient and cloud platforms smarter. Practical ways this helps tech companies include:

• Hardware: High-performance GPUs and TPUs now deliver faster results, enabling companies to process massive datasets in record time.
• Cloud Services: Modern cloud solutions like AWS, Azure, and Google Cloud offer elastic resources, letting businesses adjust their computing power on demand without heavy upfront costs.
• Data Storage: Advanced data storage options, such as AI-optimized data lakes, make it easier to organize and analyze complex datasets, driving faster insights.

The Model Layer

The model layer is where the magic happens (or at least the most transformative work) —AI models are designed, trained, and refined to deliver exceptional outputs.

Generative AI innovations have enhanced this layer, making it more efficient and accessible for tech companies. Key ways this impacts the industry include:

• Pre-trained Models: Models like GPT-4 and DALL-E offer a head start, reducing time-to-market for new applications while still delivering top-notch results.
• Custom Models: Companies can now create tailor-made solutions that address specific industry challenges, from predictive maintenance to personalized recommendations.
• Training Pipelines: Streamlined pipelines, using tools like MLflow or Kubeflow, cut down development time and allow teams to focus on innovation instead of repetitive tasks.

The Application Layer

The application layer connects generative AI with real-world users, translating complex algorithms into tangible benefits. This layer has evolved to prioritize user-centric design and seamless delivery. Examples of how this transformation benefits tech companies include:

• User Interfaces: Intuitive chatbots, dashboards, and mobile apps now bring AI's capabilities directly to users, simplifying complex processes.
• Integration APIs: Smarter APIs enable smoother connections between AI models and existing workflows, creating efficiencies and enhancing productivity.
• Monitoring and Feedback: Advanced monitoring systems now provide real-time insights into model performance, while feedback loops ensure AI adapts to changing user needs and delivers consistent value.