How Does ChatGPT Make Money?

ChatGPT leverages a freemium model, with a free version with limited capability and a premium version (starting at $20/mo), which also comprises access in peak times, faster response times, and early access to new features and improvements. In addition, ChatGPT might also make money via API access.

ChatGPT Pricing Model

ChatGPT gained popularity, reaching over a million users in a few days.

And by January 2023, ChatGPT reached a hundred million users, and it keeps growing.

On the OpenAI website, as of December 2022, over 300 million visitors landed, a staggering growth in traffic compared to 18 million visitors in November.

By January, thanks to ChatGPT, more than 660 million visitors jumped on OpenAI’s website.


By early January, the team at OpenAI started to look into pricing options for its free tool, thus opening up a waitlist with a few very simple questions.


This pricing model is known as Van Westendorp Pricing Model.

By early February, OpenAI announced the official release of the pricing, starting at $20/mo.

The new plan provided.

  • General access to ChatGPT, even during peak times
  • Faster response times
  • Priority access to new features and improvements

This first pricing model is quite interesting, as it shows, OpenAI wants to try to bring in a wide user base, not only in the B2B space but also in the consumer space (meaning the pricing is as low as almost a Netflix plan).

Over time, we might see OpenAI release new pricing tiers for B2B.

ChatGPT API Access

Another revenue generation for ChatGPT might be the access to its API, which will give a chance to developers to integrate ChatGPT into any kind of tools they are building.

Another way the API access to ChatGPT might be working is via so-called tooling.

Meaning that developers might be able to build applications on top of ChatGPT through its APIs to make it more effective at specific tasks.

In both ways, the API part of ChatGPT is critical to transforming ChatGPT from a breakthrough product to a business platform and ecosystem able to rival companies like Apple and Google.

ChatGPT Premium

ChatGPT is available as a premium version that can be accessed for $20/mo, enabling the user to get a faster version that performs better.

The functionalities are the same as the free version; what changes is speed, performance, and ability to have tasks completed with more tokens, compared to the free version, which might stop after a specific token request.

For instance, in the free version, if you ask ChatGPT to formulate a very long essay, that might stop at a certain point, whereas in the premium one, it should have no specific limits.


ChatGPT was also launched as an API endpoint.

Meaning it can be integrated via its APIs into any web application.

As OpenAI explained:

The ChatGPT model family we are releasing today, gpt-3.5-turbo, is the same model used in the ChatGPT product. It is priced at $0.002 per 1k tokens, which is 10x cheaper than our existing GPT-3.5 models. It’s also our best model for many non-chat use cases—we’ve seen early testers migrate from text-davinci-003 to gpt-3.5-turbo with only a small amount of adjustment needed to their prompts.

As OpenAI explains in its documentation, ChatGPT is powered by gpt-3.5-turbo, OpenAI’s most advanced language model.

Using the OpenAI API, you can build your applications with gpt-3.5-turbo to do things like:

  • Draft an email or other piece of writing
  • Write Python code
  • Answer questions about a set of documents
  • Create conversational agents
  • Give your software a natural language interface
  • Tutor in a range of subjects
  • Translate languages
  • Simulate characters for video games and much more

Read Next: History of OpenAI, AI Business Models, AI Economy.

ChatGPT APIs within Microsoft Azure

Another way ChatGPT will be monetized is through Microsoft’s Azure platform. Indeed, Azure is one of the most successful cloud providers in the world.


And it’s also the underlying infrastructure for OpenAI’s pre-training, as Azure has built an AI Supercomputer supporting the development of large language models from OpenAI.


The interesting part is that each time OpenAI releases a new product via APIs, this will get integrated also within the Azure cloud platform, thanks to the exclusive commercial partnership between Microsoft and OpenAI.

In this way, Microsoft Azure can leverage the success of the OpenAI APIs to expand the adoption of its cloud infrastructure further, as developers will be incentivized to host their applications on top of Azure, thanks to the integrations of ChatGPT’s APIs.

Connected Business Model Analyses


Generalized AI consists of devices or systems that can handle all sorts of tasks on their own. The extension of generalized AI eventually led to the development of Machine learning. As an extension to AI, Machine Learning (ML) analyzes a series of computer algorithms to create a program that automates actions. Without explicitly programming actions, systems can learn and improve the overall experience. It explores large sets of data to find common patterns and formulate analytical models through learning.

Deep Learning vs. Machine Learning

Machine learning is a subset of artificial intelligence where algorithms parse data, learn from experience, and make better decisions in the future. Deep learning is a subset of machine learning where numerous algorithms are structured into layers to create artificial neural networks (ANNs). These networks can solve complex problems and allow the machine to train itself to perform a task.


DevOps refers to a series of practices performed to perform automated software development processes. It is a conjugation of the term “development” and “operations” to emphasize how functions integrate across IT teams. DevOps strategies promote seamless building, testing, and deployment of products. It aims to bridge a gap between development and operations teams to streamline the development altogether.


AIOps is the application of artificial intelligence to IT operations. It has become particularly useful for modern IT management in hybridized, distributed, and dynamic environments. AIOps has become a key operational component of modern digital-based organizations, built around software and algorithms.

Machine Learning Ops

Machine Learning Ops (MLOps) describes a suite of best practices that successfully help a business run artificial intelligence. It consists of the skills, workflows, and processes to create, run, and maintain machine learning models to help various operational processes within organizations.

OpenAI Organizational Structure

OpenAI is an artificial intelligence research laboratory that transitioned into a for-profit organization in 2019. The corporate structure is organized around two entities: OpenAI, Inc., which is a single-member Delaware LLC controlled by OpenAI non-profit, And OpenAI LP, which is a capped, for-profit organization. The OpenAI LP is governed by the board of OpenAI, Inc (the foundation), which acts as a General Partner. At the same time, Limited Partners comprise employees of the LP, some of the board members, and other investors like Reid Hoffman’s charitable foundation, Khosla Ventures, and Microsoft, the leading investor in the LP.

OpenAI Business Model

OpenAI has built the foundational layer of the AI industry. With large generative models like GPT-3 and DALL-E, OpenAI offers API access to businesses that want to develop applications on top of its foundational models while being able to plug these models into their products and customize these models with proprietary data and additional AI features. On the other hand, OpenAI also released ChatGPT, developing around a freemium model. Microsoft also commercializes opener products through its commercial partnership.


OpenAI and Microsoft partnered up from a commercial standpoint. The history of the partnership started in 2016 and consolidated in 2019, with Microsoft investing a billion dollars into the partnership. It’s now taking a leap forward, with Microsoft in talks to put $10 billion into this partnership. Microsoft, through OpenAI, is developing its Azure AI Supercomputer while enhancing its Azure Enterprise Platform and integrating OpenAI’s models into its business and consumer products (GitHub, Office, Bing).

Stability AI Business Model

Stability AI is the entity behind Stable Diffusion. Stability makes money from our AI products and from providing AI consulting services to businesses. Stability AI monetizes Stable Diffusion via DreamStudio’s APIs. While it also releases it open-source for anyone to download and use. Stability AI also makes money via enterprise services, where its core development team offers the chance to enterprise customers to service, scale, and customize Stable Diffusion or other large generative models to their needs.

Stability AI Ecosystem


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