# LLM4FaaS: No-Code Application Development using LLMs and FaaS

arXiv: https://arxiv.org/abs/2502.14450 · HTML: https://mlsanity.com/paper/2502.14450/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2025.

## Built on by (most cited first, 2 of 2)

- [AutoStreamPipe: LLM Assisted Automatic Generation of Data Stream Processing Pipelines](https://mlsanity.com/paper/2510.23408/) (2025)
- [Is Vibe Coding the Future? An Empirical Assessment of LLM Generated Codes for Construction Safety](https://mlsanity.com/paper/2604.12311/) (2026)

## Abstract (arXiv)

Large language models (LLMs) show great capabilities in generating code from natural language descriptions, bringing programming power closer to non-technical users. However, their lack of expertise in operating the generated code remains a key barrier to realizing customized applications. Function-as-a-Service (FaaS) platforms offer a high level of abstraction for code execution and deployment, allowing users to run LLM-generated code without requiring technical expertise or incurring operational overhead. In this paper, we present LLM4FaaS, a no-code application development approach that integrates LLMs and FaaS platforms to enable non-technical users to build and run customized applications using only natural language. By deploying LLM-generated code through FaaS, LLM4FaaS abstracts away infrastructure management and boilerplate code generation. We implement a proof-of-concept prototype based on an open-source FaaS platform, and evaluate it using real prompts from non-technical users. Experiments with GPT-4o show that LLM4FaaS can automatically build and deploy code in 71.47% of cases, outperforming a non-FaaS baseline at 43.48% and an existing LLM-based platform at 14.55%, narrowing the gap to human performance at 88.99%. Further analysis of code quality, programming language diversity, latency, and consistency demonstrates a balanced performance in terms of efficiency, maintainability and availability.