Business Model: Models-as-a-Service



The Model-as-a-Service archetype of business models provides pre-trained machine learning models, made available as services over the internet, for free or for a fee. 

Model-as-a-Service is not New to Foundation Models, the Newest Type of AI Offering

Note: given the rapid adoption and fervor in the space, we will update this post more frequently vs. other more evergreen business model library tear-downs. Last update: July 24, 2023

Artificial Intelligence (AI): Artificial Intelligence refers to the simulation of human intelligence in machines that are programmed to perform tasks that typically require human intelligence, including subfields such as machine learning, natural language processing, computer vision, robotics, and more.

Machine Learning (ML): Machine Learning is a subset of AI and focuses on the development of algorithms that allow computers to learn from data and improve their performance on a specific task over time without being explicitly programmed. ML algorithms use statistical techniques to identify patterns and make predictions or decisions based on the patterns observed in the data.

Deep Learning: Deep Learning is a specialized subset of machine learning which involves the use of artificial neural networks with multiple layers to process and learn from vast amounts of data. Deep learning has been particularly successful in tasks such as image and speech recognition, natural language processing, and winning complex games like Go and Chess.

Foundation Models: Foundation Models refer to large-scale language models that serve as pre-trained models for various natural language processing tasks. These models are usually trained on extensive datasets and can be fine-tuned or adapted to specific tasks to achieve high-level performance. 


Business Models in Use

Alibaba Models | Google PaLM2 | Google MedPaLM | OpenAI GPT3 and GPT 4 | Microsoft Co-Pilot with OpenAI

Cohere | Jaspar | Mistral AI | Anthropic | Builder AI 

Model-as-a-Service Evaluator

Value to Customer



Key Performance Indicators

Value to Business Offering MaaS



Key Performance Indicators

When it Works Well

Co-Designed with Customers and End Users

Many MaaS offerings are being trialed as pilot projects within the IT department. The best partnerships deliberately design new workflows to augment the humans in the loop, co-design with end-user customers to determine which tasks are truly burdensome and need to be automated, and consult with vulnerable stakeholder groups who may be affected by decisions model in the model. 

Goes Beyond Ethics to Adopt a Human Rights Framework  

An ethical AI framework prioritizes the detection and mitigation of bias in AI models offered through MaaS, but leaves these decisions up to the creators of the model. A human rights framework goes further to acknowledge universal human rights, ensuring universality – human rights are universal and apply to all people in the world, right to equality and non-discrimination, participation, transparency, accountability and remedy. 

Increases Likelihood of Positive Emergent Outcomes

Emergence refers to the unexpected and remarkable properties and capabilities that arise when these models are deployed and interact with vast amounts of data. Unexpected positive outcomes may occur generating and creating combinations that had never been considered, or positively changing the way humans create and work. Model developers  that consider the full ecosystem that they inhabit, including the negative unintended consequences of emergence, will be better prepared. 


Models-as-a-service can efficiently handle increasing workloads and demand, dynamically allocating resources to ensure smooth operation during peak periods, reducing latency, and providing cost-effective solutions. The concept of homogenization in MaaS ensures consistent and predictable behavior of AI models across all users, unifying the service’s offerings, processing inputs uniformly, and maintaining stability.

Security Processes are Addressed 

When a robust security process is implemented, a company adopted a model-as-a-service conducts a thorough threat assessment, securing data with encryption and access controls, implementing strong authentication and authorization mechanisms, ensuring secure APIs and communication, conducting regular security audits and monitoring, maintaining up-to-date patches, providing security training to employees, and planning for disaster recovery and incident response. 

Challenges to the MaaS Model in Use

Highly Competitive Space

Major tech giants, including Amazon, Microsoft in collaboration with OpenAI, Alphabet-Google, and Meta-Facebook, are making substantial investments in capital expenditures to pre-train advanced machine learning, deep learning, and foundation models. The stakes are enormous, encompassing the future of cloud services, advertising, and the ability to participate in the healthcare domain. With such a significant prize at stake, the competition is fierce, and aspiring companies are raising substantial funds to compete in this space. Startups are building war chests to enter the arena and challenge the dominance of these tech giants.


AI models can produce incorrect or wrong answers without giving any sense of awareness to the user that the model is wrong. 

If a foundation model lacks a comprehensive understanding of context, it may generate hallucinations by extrapolating or combining information inappropriately.

AI models do not currently express adequate uncertainty about predictions generated. In the absence of a clear answer, a foundation model might produce these hallucinations as a way to provide an output.



High Financial and Energy Costs

Model as a Service (MaaS) incurs high energy and financial costs due to the substantial computational power needed for hosting AI models, leading to continuous energy consumption and increased electricity usage in data centers. The need for powerful server along with cooling systems, contributes to the overall operational expenses. Additionally, data transfer and bandwidth costs add to the financial burden.  Developers need to rigorously mitigate these financial and energy risks. 

Bias in Training Models and Practices

Microsoft’s first attempt to launch a trial chatbot model on Twitter in 2016 resulted in resulted in the model being shut down as it started to spew lewd and racist tweets. 

If AI is trained on biased data, it can potentially amplify and perpetuate these biases in the generated content, leading to discriminatory or unfair outcomes, causing harm. 

AI-enabled bias in criminal justice, loan and credit scoring, and hiring have been determined to cause real harm to humans, today. 

Black Boxes

Many AI models, particularly deep learning-based models, are often considered “black boxes” due to their complexity, making it challenging to understand their decision-making process and troubleshoot errors effectively. This lack of explainability will hamper the adoption of models-as-a-service. 


Foundation models that enable generative AI can be used to create highly realistic fake images, videos, and text, making it challenging to discern between what is real and what is generated. This can contribute to the spread of misinformation and fake content, leading to potential social and political consequences.

Privacy Concerns

The output of these models could be used to create synthetic data that resembles real individuals, raising concerns about privacy and data protection. This synthetic data could be exploited for various malicious purposes.


Trends in MaaS

Rapid Adoption / Fervor

The rapid adoption of ChatGPT caught most tech leaders and investors off guard, and all have recently re-cast their strategic plan to lay claim to a specific AI strategy. Wall Street has favored companies re-tooling their technology to be rebuilt with AI, and venture capitalists have radically re-shifted their investments to back capital-consuming startups. Every day a new foundation model use case emerges. 

Existential Threat Warnings

A number of statements from scientists and tech leaders warned that artificial intelligence poses an existential threat to humanity. “Mitigating the risk of extinction from AI should be a global priority alongside other societal scale risks such as pandemics and nuclear war.” This is in contrast to bias and harm researchers who point to the threat that may be unleashed from further deployment of AI without addressing the consequences. 


Open Source

The fast-scaling OpenAI GPT3 and GPT4 have been criticized for operating in a closed-source environment, reducing the likelihood that bugs and bias will be fixed. Recently, open source projects have emerged that encourage developers enables scrutiny by the wider scientific community and enables others to build on it and learn from it, 

Human Rights

An ethical AI framework prioritizes the detection and mitigation of bias in AI models offered through MaaS, but leaves these decisions up to the creators of the model. A human rights framework goes further to acknowledge universal human rights, ensuring universality – human rights are universal and apply to all people in the world, right to equality and non-discrimination, participation, transparency, accountability and remedy. 

Key MaaS Mechanisms to Test

The frenzy of activity is currently all about experimentation, riding the possibilities of emergence, mitigating risk, and finding use cases. We recommend also developing a strategy for how this technology can create, capture, and distribute value to ecosystem stakeholders. 

Before You Consider MaaS

  • Do you have access to a data model that can be pre-trained? 
  • Do you have a value proposition? A proposed workflow to change? A human to augment? 
  • How will you address potential bias iand implement mechanisms to ensure fairness? 

Testing the Model

  • Do you have early adopters identified to test your model? 
  • Do you have a complimentary business model hypothesis (pay-per-use, subscription, other) to test? 
  • Can our MaaS solution be used by developers, or by business users with no or low code skills?

More on MaaS

Implementation of Artificial Intelligence (AI): Roadmap for Business Model InnovationReim et al, AI 1.2 (2020).

The Economics of Large Language Models, Sunyan, Substack, (2023).

Artificial intelligence and business models in the sustainable development goals perspective: A systematic literature review, Di Vaio, (2020).

On the Opportunities and Risks of Foundation ModelsBommasani et al, arXiv preprint arXiv:2108.07258 (2021)