# Model-as-a-Service (MaaS): A Survey

arXiv: https://arxiv.org/abs/2311.05804 · HTML: https://mlsanity.com/paper/2311.05804/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 80 references that are in the map.
- First posted on arXiv 10 Nov 2023.

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Extracting Training Data from Large Language Models](https://mlsanity.com/paper/2012.07805/) (2020)
- [PaLM-E: An Embodied Multimodal Language Model](https://mlsanity.com/paper/2303.03378/) (2023)
- [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://mlsanity.com/paper/1909.08053/) (2019)
- [Large Language Models as Optimizers](https://mlsanity.com/paper/2309.03409/) (2023)
- [Learning Not to Learn: Training Deep Neural Networks with Biased Data](https://mlsanity.com/paper/1812.10352/) (2018)
- [Contextual Object Detection with Multimodal Large Language Models](https://mlsanity.com/paper/2305.18279/) (2023)
- [The Programmer's Assistant: Conversational Interaction with a Large Language Model for Software Development](https://mlsanity.com/paper/2302.07080/) (2023)
- [Art and the science of generative AI: A deeper dive](https://mlsanity.com/paper/2306.04141/) (2023)
- [The unreasonable effectiveness of few-shot learning for machine translation](https://mlsanity.com/paper/2302.01398/) (2023)
- [ChatGPT for Shaping the Future of Dentistry: The Potential of Multi-Modal Large Language Model](https://mlsanity.com/paper/2304.03086/) (2023)
- [A Survey of Utility-Oriented Pattern Mining](https://mlsanity.com/paper/1805.10511/) (2018)
- [CommerceMM: Large-Scale Commerce MultiModal Representation Learning with Omni Retrieval](https://mlsanity.com/paper/2202.07247/) (2022)
- [PQK: Model Compression via Pruning, Quantization, and Knowledge Distillation](https://mlsanity.com/paper/2106.14681/) (2021)
- [An Automated Survey of Generative Artificial Intelligence: Large Language Models, Architectures, Protocols, and Applications](https://mlsanity.com/paper/2306.02781/) (2023)
- [Fake News Detectors are Biased against Texts Generated by Large Language Models](https://mlsanity.com/paper/2309.08674/) (2023)
- [Federated Learning Attacks and Defenses: A Survey](https://mlsanity.com/paper/2211.14952/) (2022)
- [Web 3.0: The Future of Internet](https://mlsanity.com/paper/2304.06032/) (2023)
- [SkillGPT: a RESTful API service for skill extraction and standardization using a Large Language Model](https://mlsanity.com/paper/2304.11060/) (2023)
- [Privacy Preserving Utility Mining: A Survey](https://mlsanity.com/paper/1811.07389/) (2018)
- [Internet of Behaviors: A Survey](https://mlsanity.com/paper/2211.15588/) (2022)
- [Big Data Meets Metaverse: A Survey](https://mlsanity.com/paper/2210.16282/) (2022)
- [Web3: The Next Internet Revolution](https://mlsanity.com/paper/2304.06111/) (2023)
- [A Survey of Knowledge Enhanced Pre-trained Models](https://mlsanity.com/paper/2110.00269/) (2021)

## Built on by (most cited first, 25 of 25)

- [Multimodal Large Language Models: A Survey](https://mlsanity.com/paper/2311.13165/) (2023)
- [Large Language Models in Law: A Survey](https://mlsanity.com/paper/2312.03718/) (2023)
- [Large Language Models in Education: Vision and Opportunities](https://mlsanity.com/paper/2311.13160/) (2023)
- [Large Language Models for Medicine: A Survey](https://mlsanity.com/paper/2405.13055/) (2024)
- [Large Language Models for Education: A Survey](https://mlsanity.com/paper/2405.13001/) (2024)
- [Watermarking Techniques for Large Language Models: A Survey](https://mlsanity.com/paper/2409.00089/) (2024)
- [Mixture of Experts (MoE): A Big Data Perspective](https://mlsanity.com/paper/2501.16352/) (2025)
- [A Survey of Controllable Learning: Methods and Applications in Information Retrieval](https://mlsanity.com/paper/2407.06083/) (2024)
- [EcoAct: Economic Agent Determines When to Register What Action](https://mlsanity.com/paper/2411.01643/) (2024)
- [ZeroFlow: Overcoming Catastrophic Forgetting is Easier than You Think](https://mlsanity.com/paper/2501.01045/) (2025)
- [PARA: Parameter-Efficient Fine-tuning with Prompt Aware Representation Adjustment](https://mlsanity.com/paper/2502.01033/) (2025)
- [SMCP: Secure Model Context Protocol](https://mlsanity.com/paper/2602.01129/) (2026)
- [AlayaDB: The Data Foundation for Efficient and Effective Long-context LLM Inference](https://mlsanity.com/paper/2504.10326/) (2025)
- [ADKGD: Anomaly Detection in Knowledge Graphs with Dual-Channel Training](https://mlsanity.com/paper/2501.07078/) (2025)
- [Model Privacy: A Unified Framework for Understanding Model Stealing Attacks and Defenses](https://mlsanity.com/paper/2502.15567/) (2025)
- [MaaSO: SLO-aware Orchestration of Heterogeneous Model Instances for MaaS](https://mlsanity.com/paper/2509.06362/) (2025)
- [Advances in Large Language Models for Medicine](https://mlsanity.com/paper/2509.18690/) (2025)
- [AgentSLA : Towards a Service Level Agreement for AI Agents](https://mlsanity.com/paper/2511.02885/) (2025)
- [Enhancing Temporal Awareness in LLMs for Temporal Point Processes](https://mlsanity.com/paper/2601.00845/) (2025)
- [Private Prediction via PAC Privacy](https://mlsanity.com/paper/2601.14033/) (2026)
- [Federated Inference: Toward Privacy-Preserving Collaborative and Incentivized Model Serving](https://mlsanity.com/paper/2603.02214/) (2026)
- [MMMS: Multi-Modal Multi-Surface Interactive Segmentation](https://mlsanity.com/paper/2509.12963/) (2025)
- [SAGE: Style-Adaptive Generalization for Privacy-Constrained Semantic Segmentation Across Domains](https://mlsanity.com/paper/2512.02369/) (2025)
- [DeepSeek Robustness Against Semantic-Character Dual-Space Mutated Prompt Injection](https://mlsanity.com/paper/2604.12548/) (2026)
- [A Chain Is Only as Strong as Its Weakest Link: A Scoping Review of System Integration Audits in AI](https://mlsanity.com/paper/2608.04921/) (2026)

## Abstract (arXiv)

Due to the increased number of parameters and data in the pre-trained model exceeding a certain level, a foundation model (e.g., a large language model) can significantly improve downstream task performance and emerge with some novel special abilities (e.g., deep learning, complex reasoning, and human alignment) that were not present before. Foundation models are a form of generative artificial intelligence (GenAI), and Model-as-a-Service (MaaS) has emerged as a groundbreaking paradigm that revolutionizes the deployment and utilization of GenAI models. MaaS represents a paradigm shift in how we use AI technologies and provides a scalable and accessible solution for developers and users to leverage pre-trained AI models without the need for extensive infrastructure or expertise in model training. In this paper, the introduction aims to provide a comprehensive overview of MaaS, its significance, and its implications for various industries. We provide a brief review of the development history of "X-as-a-Service" based on cloud computing and present the key technologies involved in MaaS. The development of GenAI models will become more democratized and flourish. We also review recent application studies of MaaS. Finally, we highlight several challenges and future issues in this promising area. MaaS is a new deployment and service paradigm for different AI-based models. We hope this review will inspire future research in the field of MaaS.