Cutting LLM Costs: A Better Way to Integrate AI for Your Business

Identify how much Large Language Models cost. Navigate the complexities of integrating AI into your business and learn the best approach.

The introduction of ChatGPT officially ushered in a new era of innovation driven by LLMs — or Large Language Model. Since then, it (and other language models) has evolved at a breakneck speed. Making models more sophisticated. Training on billions of data parameters, and fine-tuned for varying use cases.

This gave rise to GPTs. GPTs provide users the ability to customize ChatGPT to fit their specific needs.

Currently, companies across industries are racing to integrate the technology into their stack. Aiming to deliver improved operational efficiency and to gain a competitive edge.

Several notable companies have already integrated LLMs into their business. To name a few: IBM (AskHR App), Walmart (Use of GPT-4 and other open source LLMs), Shopify (Shopify Sidekick), and Brave (Leo). The number will undoubtedly grow in the coming years as AI models continue to advance.

As alluring as it seems, integrating AI into your business is not so easy. There are various complexities involved in the process. Mainly; sourcing high-quality data, development, training, and of course, the cost.

Cost of Building a Large Language Model

There are two ways to integrate a large language model into your business; Building your own LLM, or building on top of an existing LLM. Whether it’s proprietary or open-sourced. No doubt building a model from scratch is the most expensive route. It requires large amounts of data, computational power, teams of engineers, and infrastructure.

The cost of development is astronomical. As an example, OpenAI has spent over an estimate $1.9 billion to date, developing ChatGPT. Now, It’s reported that they are set to spend around $7 billion more on developing new AI models. However, it is also worth taking note of the different cost types and other recurring costs.

Different Cost Types in Deploying LLMs

The total cost of a large language model includes several different cost types. Each amount varies depending on the design or use case requirements of the model:

Inference Costs: Inference costs are expenses incurred during the operational phase of an LLM. The cost is determined by numerous factors such as model size, request volume, and infrastructure.

Setup and Maintenance Costs: Setup and maintenance involve several steps. It covers Fine-tuning, Integration, and Ongoing Maintenance.

Other Indirect Costs: Besides the direct expenses, there are other factors worth considering. These factors include; Data Preparation, Compliance and Security, Scalability Requirements, and Training Infrastructure.

Given these facts, this approach is very impractical and almost impossible. It would be more practical to consider training existing LLMs instead.

Building on Top of an Existing LLM

Building on top of an existing large language model is the more cost-effective approach. Simply put, it involves taking an established model and customizing it to fit your use case. Similar to taking a pre-built apartment and customizing it to your liking. You don’t need to build the foundation; you only need to add your personal touches.

Additionally, you’re leveraging a model with established capabilities. This gives you a reduced risk of failure.

The model only needs to learn the requirements and other relevant data to work as intended. This means you will need to train it.

Model Training

Model training involves feeding data into the model and adjusting its internal parameters. This aims to improve its performance and output on specific tasks. The process involves two notable methods: Retrieval-Augmented Generation (RAG) and Supervised Fine-Tuning (SFT).

RAG involves searching for relevant information from a massive dataset. This can be exclusive data from your enterprise. The model will use your data to enhance its generation process.

Meanwhile, SFT provides the model with labeled examples (data with desired outputs). This is to guide the model towards generating the outcomes you need it to. As the name suggests, it helps fine-tune the outputs of specific tasks and processes.

Now, the question becomes: how can you train an existing model? Well, to train the model you will need to hire expert trainers. Here’s how you can approach it:

In-House Training

Choosing to train the model in-house gives you full control over the process. You get to choose the data, customize the training, and have a cohesive team to facilitate the process.

The approach grants you full customization capabilities. It helps you create an AI model that perfectly aligns with your use case. This ensures you have complete control over your data and how it’s used.

You will also benefit from the strength of consistent and reliable team members. In-house trainers have a deep understanding of your company’s processes and culture. Therefore they are also a lot easier to communicate and collaborate with.

Furthermore, this also gives trainers opportunities for mentorship and development. Over time, this culminates in your team producing quality outputs.

However, it’s worth mentioning that the approach comes at high costs. These are due to training, benefits, and other associated costs. As well as hiring and retention challenges.

Outsourced Training

On the other hand, hiring freelance trainers or using crowdsourcing platforms can be quite cost-effective compared to in-house trainers. This also means that you will have access to a broad talent pool of trainers globally.

This approach is especially beneficial if your company has fluctuating workloads as it allows for scalability. You can easily adjust the team size based on your project needs.

Despite its strengths, there are drawbacks. The expertise and reliability of each freelancer can vary significantly which may result in poor or spotty outputs.

Finding skilled AI trainers on freelance platforms requires careful vetting. Lastly, collaborating with them can be more challenging since they are not fully integrated into your company.

Adaptive Workstack: A New Approach

As AI becomes ubiquitous, we believe it’s critical to reimagine the process of training large language models. We need an approach that gives research teams the agility to test new cases at breakneck speed to deliver advancements and breakthroughs. To enable teams to build the application layer with diversified high-quality data. Most importantly, an approach that delivers the training scale at reduced costs. Making AI development truly inclusive for everybody — And we have done just that!

Our Adaptive Workstack merges the strength of the in-house approach with the scalability of outsourced training. It focuses on three key aspects:

1. Quality Data Training — Model responses apply garbage data in garbage responses out approach. Which makes Quality Data critical. Our domain experts ensure quality training data.
2. Agility and Scale — Our system gives teams flexibility to test cases and do discovery projects at record speeds at the scale of data they require.
3. Cost reduction — Our customizable tiers enable organizations to choose the right team to deliver the results they need. Our results-based model ensures goals and budgets are met.

We understand the challenges and complexities of AI training. From the cost to the complexities of the training process. Our approach provides you with a cost-effective and scalable option.

Our team is your team. We operate synergistically as a remote extension of your internal team. Constantly communicating and collaborating. Guaranteeing your needs are met with complete transparency.

Breakthrough with AI. Discover a better way.