How to Choose Your AI Backbone: Top AI Infrastructure Recommendations

Companies worldwide are locked in an AI arms race. In 2024, the value of the global AI market reached $184 billion. Generative AI (GenAI) can improve a worker’s performance by nearly 40%, increase corporate profits by $4.4 trillion a year, and improve diagnostic accuracy in the medical field, making it a valuable tool for companies across industries.

It’s clear the technology isn’t a novelty: it’s now an integral part of many companies’ operations. 

As companies optimize and improve, developing a comprehensive AI implementation strategy is key to smooth AI adoption. Developing or commissioning a custom AI solution can seem daunting, and some business owners might not know where to start.

Fortunately, there are ways leaders can identify and choose the AI backbone best suited for their organization. This guide summarizes the components of an AI backbone and how to navigate the process of building one.

What is an AI Backbone?

An AI backbone is the core infrastructure behind AI solutions. It supports the development, deployment, and execution of an artificial intelligence system. An AI backbone consists of four primary components:

• Data
• Computational Resources
• AI Models
• AI Frameworks

These components work together to facilitate AI-powered automation, analytics, and decision-making within a business.

Key Components of an AI Infrastructure

A strong AI backbone is built on a foundation of effective models, efficient frameworks, and the high-quality data and computational resources they need. These components work together to create scalable AI solutions that drive innovation and handle complex tasks.

AI Models

Many different types of AI models have emerged over the years. Classic Machine Learning, for example, refers to traditional algorithms and techniques for building predictive models by identifying patterns in data. It relies on simpler and more interpretable models.

However, as AI advances, one category has become a staple in modern enterprise AI infrastructure: deep learning. Deep learning models are the core of an AI backbone, enabling breakthroughs in tasks like natural language processing, computer vision, and speech recognition. 

Unlike traditional machine learning models, deep learning architectures like neural networks can automatically extract features without manual engineering, making them highly adaptable to diverse domains. Their ability to learn complex patterns from vast amounts of data allows them to power an array of systems: from chatbots and image recognition services to autonomous vehicles and medical diagnostics.

LLMs in particular, a type of deep learning model, have revolutionized many AI systems by enabling human-like text generation, translation, and contextual understanding.

Frameworks

An AI framework is a software library or platform that provides pre-built tools, algorithms, and infrastructure for developing, training, and deploying AI models.

Selecting the right AI framework is essential to the modern AI backbone and overall AI technology stack. AI frameworks provide the tools and libraries to efficiently build, train, and deploy machine learning and deep learning models. They simplify complex mathematical operations, optimize computations, and offer scalable solutions for large datasets.

Popular AI frameworks like TensorFlow, PyTorch, and JAX enable researchers and developers to experiment with cutting-edge models, including Large Language Models (LLMs) and neural networks. These frameworks support hardware acceleration with GPUs, making AI development more accessible and efficient.

In addition to these frameworks, open-source AI models play a crucial role in democratizing AI research and application. Models like DeepSeek, LLaMA, and Mistral offer powerful alternatives to proprietary systems, allowing organizations to fine-tune and deploy AI solutions without heavy dependence on commercial APIs. 

Open-source models are valuable for customization, transparency, and cost-efficiency, enabling enterprises to tailor AI solutions to their specific needs while maintaining control over data privacy and model behavior.

Computational Resources

Computational resources are the processing power, memory, and storage needed to train, deploy, and run AI models efficiently at scale. These resources form the foundation of enterprise AI infrastructure, supporting model training and deployment. The better and more specialized the hardware, the larger and more efficient a model can be.

For example, while regular ML models like random forest and SVM require minimal resources, large-scale models like LLMs need extensive deployment and real-time processing storage. Companies looking to accelerate their deep learning tasks will also likely invest in GPUs, or specialized hardware designed for parallel computation.

However, while additional resources make for a stronger AI backbone, the high cost of advanced hardware presents a significant challenge for many companies. The thousands of GPUs needed to build and train LLMs can cost millions of dollars, with OpenAI's GPT-3 language model requiring at least $5 million worth of GPUs per training run.

These costs will only worsen as AI models become more complex, requiring substantially more processing power, memory, and storage to function. Sam Mugel, Chief Technology Officer of Multiverse, estimates that training the next generation of LLM will cost upwards of $1 billion in the next few years. 

Fortunately, cost-effective methods are available for businesses seeking an affordable and scalable AI backbone. Adnan Masood, Chief AI Architect at UST, and Jacob Dencik, Research Director at IBV, outlined a list of potential strategies companies can employ to reduce the costs of their computational resources. These include: 

• Incorporating small models trained on high-quality data when appropriate
• Reusing and fine-tuning existing models
• Using quantization to reduce the memory the model needs

Many companies are also turning to AI cloud platforms to reduce costs, as they offer various benefits such as pay-as-you-go models to reduce infrastructure costs, elastic computing capabilities, and remote accessibility. Statista notes that AI cloud platforms are now “widely regarded as the most effective way to tap into the potential of AI fully,” and has even claimed that they are “no longer just an option but a strategic necessity.”

Data Management

Data plays a fundamental role in creating an AI backbone, shaping how AI models are built, trained, and evaluated. There are three primary types of data in machine learning: 

1. Structured data, which is easily searchable and stored in databases, spreadsheets, and other organized formats.

2. Unstructured data, which is complex, less organized, and often consists of images, videos, and text documents.

3. Semi-structured data, which lacks the organization of structured data, but utilizes tags or other markers to differentiate content.

Data quality, quantity, and diversity directly impact a model’s ability to learn patterns, generalize to new examples, and make accurate predictions. Without enough high-quality data, a system is more likely to create unreliable and unsatisfactory results and may struggle to adapt to new situations. 

Unfortunately, many companies face issues when assembling a dataset. A Harvard Business Review survey discovered that 46% of data leaders identified data quality as the greatest challenge to realizing GenAI's potential in their organizations, and only 37% agreed that their organizations had the right data foundation for GenAI.

Improper AI training data is often biased, scarce, inconsistent, or outdated, making for a weaker AI backbone. Additionally, if the data is poorly organized or is left vulnerable to breaches, their data quality may be at further risk. Companies can counter these threats by running their data through a thorough AI data management pipeline before implementation. 

Why is Choosing the Right AI Backbone Important?

AI models are everywhere, with a McKinsey & Co. survey finding that 92% of executives expect to boost spending on AI in the next three years. However, only one percent of respondents called their companies “mature” on the deployment spectrum: or, that their AI is fully integrated into workflows. McKinsey identifies the common culprits holding enterprise AI infrastructure back as poorly designed or executed strategies. 

While machine learning models and other systems are promising, they are only as effective as the AI backbone they are built around. If a company doesn’t have the right AI technology stack in place, then the AI will be costlier, less effective, and unsustainable in the long term.

“Deploying a computationally intensive deep learning model on legacy on-premise infrastructure without adequate hardware acceleration can result in slow inference times and higher operational costs,” says Luciano Barabosa, a PhD AI engineer at FullStack.

How to Choose the Right AI Backbone for Your Business: Step-by-Step Guide

Selecting the right AI backbone is critical for ensuring scalability, efficiency, and long-term success. While navigating the growing number of AI models and computing options may be overwhelming, there are several steps business owners can take to choose the backbone that’s right for their operations.

Step 1: Assess Requirements

Before companies invest in their AI implementation strategy, they must identify and outline their specific goals and needs. An AI backbone designed to automate customer service will take a much different shape than one made to improve predictive analytics. Similarly, larger companies will likely require larger, more advanced systems than smaller organizations with simpler needs. 

By defining objectives, companies will better understand which models, data sources, and computational resources are right for their AI strategy. 

Step 2: Infrastructure

The next step in constructing an AI backbone is analyzing the business’ IT infrastructure. Its existing hardware, systems, and the quality and amount of available data will impact the type of model it can implement. The company should then determine whether it needs on-premises solutions, cloud-based AI, or a hybrid model to meet its AI demands. 

Step 3: Refining Data Quality & Management

Once a company has identified available data for its systems, the next step is running it through an AI data management pipeline. This consists of correcting any errors or inconsistencies to “clean” the data, normalizing values, and scaling features. Once the data is fed into the model, it must be properly stored: either in a traditional database or in AI cloud platforms. 

How Can You Develop Your AI Implementation Strategy?

After selecting a backbone, the next step is developing an AI implementation strategy. A strong AI strategy should incorporate a company’s short and long-term goals, allowing it to scale its model as it grows and its needs change. Businesses can accomplish this by creating a roadmap for their operations to follow and by working with qualified AI experts to ensure they maintain a scalable AI backbone.