AI’nt That Easy #27: How to Calculate the Cost of Running LLM-Based Applications in Production

Launching and scaling an LLM-powered application in production can be expensive. Whether you’re a startup or an enterprise, understanding the cost components is crucial to plan budgets and optimize resources.

The cost of deploying and running an LLM (Large Language Model) based application in production depends on several factors. These costs can broadly be categorized into infrastructure, development, and operational expenses.

Photo by: Sagacity

In this blog, we’ll break down the key factors influencing the cost of LLM-based applications.

1. Model Type and Size

Model Size:

• Larger models (like GPT-4) require more computational resources (CPU/GPU/TPU), leading to higher costs.

Customization:

• Fine-tuned or customized models increase cost due to training and hosting needs.

2. Compute Infrastructure

Hardware:

• GPUs/TPUs are often required for inference and training, which are more expensive than CPUs.
• Cloud-based solutions (AWS, GCP, Azure) versus on-premises setups.