Imagine your business is on the brink of a breakthrough, powered by the remarkable potential of Large Language Models (LLMs). These models, with their ability to understand and generate text that feels almost human, could revolutionise the way you engage with customers, analyse data, and automate complex tasks. But as with any powerful tool, the true cost of harnessing this potential can be elusive.
At Eden AI, we’ve watched this play out more than once. The initial excitement of implementing LLMs can quickly shift to concern as unexpected costs begin to add up, potentially derailing your budget and stalling your progress. The real challenge isn’t just the upfront investment — it’s understanding the full scope of the costs involved in using these advanced models.
The Hidden Costs of LLMs
Think of LLMs like a high-performance sports car. It’s fast, powerful, and can take your business to new heights. But with that power comes a significant cost.
First, there’s the expense of developing or accessing the model. Training an LLM is akin to designing that sports car from the ground up — requiring a massive amount of computational power, often running on fleets of GPUs, and a vast dataset to fine-tune the engine. Even if you choose a pre-built model like ChatGPT or Llama, you’re still covering part of that investment, much like paying a premium for a ready-to-drive luxury car.
Next, there are the usage costs. The more you use it, the more you pay. Whether you’re charged per token — like paying per mile — or hosting your own model with its infrastructure, the expenses can quickly add up. Hosting a model like Llama3 on AWS cloud, for example, can run you nearly $24,000 a month, much like paying for premium gas every time you take your car out for a spin.
But perhaps the most surprising are the hidden costs — the ones that sneak up on you like unexpected tolls on a road trip. Additional API calls, larger input/output data sizes, and even the cost of storing and searching through data in vector databases can lead to unanticipated increases in your expenses. Before you know it, a manageable budget can start to spiral out of control.
Keeping Costs in Check
So, how do you prevent your AI journey from becoming a financial burden? At Eden AI, we’ve developed practical strategies to ensure that your path remains smooth, efficient, and within budget. When considering LLM deployment options, businesses have three main choices, each with its own cost implications:
- Third-party solutions, like OpenAI’s GPT models, charge based on the number of tokens processed, with GPT-4 being significantly more expensive than GPT-3.5. This option is straightforward but can become costly, especially for high-volume applications.
- Cloud-managed LLMs, such as Google Cloud Platform’s Palm-2, offer a different pricing model based on the number of characters processed rather than tokens. Palm-2 is competitively priced compared to GPT-3.5 and is much more affordable than GPT-4, making it a viable option for many businesses.
- Custom LLM hosting on cloud infrastructure, like deploying Llama-2 on Google Cloud’s VertexAI, provides complete control over the model but requires managing hardware resources (vCPUs, RAM, GPU). This approach involves more complexity and continuous maintenance but can be tailored to specific needs.
In terms of cost analysis, custom endpoints have a linear cost over time, as expenses are tied directly to hardware usage. In contrast, third-party and cloud-managed solutions’ costs fluctuate based on the number of conversations handled daily. Notably, custom hosting becomes cost-effective only when the number of daily conversations exceeds 8,000. Below this threshold, third-party or cloud-managed options are generally more economical, offering a better balance between cost and operational simplicity for businesses not operating at a very large scale.
Looking Ahead
The journey with LLMs is full of opportunities, but it also comes with its share of challenges. Costs can rise faster than anticipated, turning what was once a smooth ride into a bumpy road. However, by understanding the factors that drive these costs and working with experts like Eden AI, you can implement proactive measures to avoid these pitfalls.
At Eden AI, we’re committed to helping you make the most of your LLMs while keeping expenses in check. We’ll help you navigate the complexities, manage costs, and ensure that your AI strategy is both sustainable and profitable.
Ready to take control of your AI costs? Reach out to our team at specialists@edenai.co.za or visit us at https://edenai.co.za to start your journey toward a smarter, more efficient AI-powered future.
This post was enhanced using information from:
Benram, G. (2018) Understanding the cost of Large Language Models (LLMs) TensorOps
https://www.tensorops.ai/post/understanding-the-cost-of-large-language-models-llms
Guizeni, S. (2024) Unlocking the Secrets of LLM Operating Costs: A Comprehensive Guide
https://seifeur.com/how-much-does-it-cost-to-run-llms/
Debes, H. (2023) Cost Analysis of deploying LLMs: A comparative Study between Cloud Managed, Self-Hosted and 3rd Party LLMs Artefact Engineering and Data Science
https://medium.com/artefact-engineering-and-data-science/llms-deployment-a-practical-cost-analysis-e0c1b8eb08ca