While cloud platforms offer extensive services for running and scaling applications, automatically deploying code from GitHub repositories to cloud infrastructure remains a manual, error-prone process requiring specialized expertise. Current DevOps tools and Infrastructure-as-Code (IaC) frameworks rely on fixed templates and cannot adapt to diverse application requirements automatically. We propose CloudBot, a toolkit that automates the complete deployment workflow by integrating code analysis, IaC generation, and infrastructure provisioning into a unified pipeline. CloudBot employs a pipeline where specialized components, GitHub Analyst, Cloud Architect, and Cloud Engineer, work sequentially to extract requirements, design infrastructure, and generate validated Terraform templates. The toolkit uses large language models enhanced with retrievalaugmented generation to map application needs to infrastructure specifications. We evaluate CloudBot with AWS cloud across three use cases: text processing, image analysis, and video processing. CloudBot achieves consistent deployment success, with all configurations deploying and executing correctly. Comparing generated infrastructure against expert-written baselines reveals similarity scores of 18-43% across syntax, semantic, and functional dimensions. While generated configurations successfully deploy, they tend toward over-provisioning compared to minimal expert specifications. As the first end-to-end automated deployment system, CloudBot demonstrates LLM-based infrastructure automation feasibility while establishing quantitative baselines for measuring future improvements toward expert-level generation quality.
Oduns et al. (Wed,) studied this question.