Nowadays, most projects that utilize Artificial Intelligence (AI) models demand significant computational resources. Almost each time a new model comes out, and outperforms previous ones, it seems to require more computational resources to run efficiently. A lot of people will say that there are exceptions, such as the DeepSeek model, but that is not actually true. Models like DeepSeek are competitive with larger models but are not better than them. At least at this point, size seems to be directly correlated with the power of a model.
Traditionally, deploying AI at scale meant managing a very complex infrastructure, from provisioning servers or clusters to writing deployment scripts and even managing cloud-specific services. However, this overhead has not only become a major pain point for a lot of ML teams but has also become a limiting factor, stopping them from trying out new models and constraining their creativity. To avoid these limiting factors we need to adapt our approach, and this is exactly what Modal enables us to do as a unified cloud platform for running code for data and AI tasks.
