  In this paper, we investigate the intersection of large generative AI models
and cloud-native computing architectures. Recent large models such as ChatGPT,
while revolutionary in their capabilities, face challenges like escalating
costs and demand for high-end GPUs. Drawing analogies between
large-model-as-a-service (LMaaS) and cloud database-as-a-service (DBaaS), we
describe an AI-native computing paradigm that harnesses the power of both
cloud-native technologies (e.g., multi-tenancy and serverless computing) and
advanced machine learning runtime (e.g., batched LoRA inference). These joint
efforts aim to optimize costs-of-goods-sold (COGS) and improve resource
accessibility. The journey of merging these two domains is just at the
beginning and we hope to stimulate future research and development in this
area.
