When ChatGPT first launched, it was a straightforward chatbot: you asked a question, and it provided a text-based answer. This has evolved. On October 6, 2025, OpenAI announced that apps are coming to ChatGPT, turning it from a text tool into a full interactive platform.
This marks a significant shift. It means developers can now build rich, visual experiences right inside a conversation. Think less back-and-forth text and more clickable maps, forms, and product carousels. Apps are currently rolling out in preview for ChatGPT Business, Enterprise and Edu customers, and it’s a major step up for what conversational AI can do. But what does this actually mean for your business? This guide will walk you through the new SDK, breaking down what it is, how it works, and the real-world challenges to consider before jumping in.
What is the ChatGPT apps SDK?
The ChatGPT apps SDK is an open-source set of tools that lets developers build and run conversational apps directly inside of ChatGPT. The key difference here is that these apps are not just about text. They can show interactive user interfaces (UIs) like maps, lists, or forms, making the chat experience feel more dynamic and useful.
For example, instead of asking ChatGPT to describe houses in Austin and getting a wall of text, you could ask, "Find homes in Austin under $500k." An app from a company like Zillow could then pop up with an interactive map and property listings you can click through, all without leaving the chat. We're already seeing this with early partners. Zillow, Canva, and Spotify are using it to let people browse home listings, create presentations, or build playlists through conversation. This provides a glimpse into a future where AI interactions feel more like using a comprehensive assistant.
The core components of building with the ChatGPT apps SDK
Building an app for ChatGPT involves three core components. You need a way for the AI to talk to your app, an interactive UI for the user to see and click, and a backend server to handle all the heavy lifting. Let's break down what each of these pieces does.
The Model Context Protocol (MCP): Communicating with ChatGPT
The first component is something called the Model Context Protocol, or MCP. It's an open standard that acts as the communication bridge between ChatGPT and any external tools you want to connect. It is the set of rules that lets the AI and your application talk to each other securely. The whole Apps SDK is built right on top of it.
An MCP server's main jobs are telling the AI model what tools your app has (like "search for products" or "book a meeting"), running the right action when the AI calls on a tool, and then sending back the results, including any visual UI components that need to be shown to the user. It provides the underlying structure for the system to function.
Web components: Building the interactive UI
The next component is the visual layer that the user interacts with. The visual layer of a ChatGPT app is built using standard web technologies you’re probably familiar with: HTML, CSS, and JavaScript. These components are then displayed inside what’s called an iframe right in the ChatGPT client.
To make sure everything talks to each other correctly, the communication between your app's frontend and ChatGPT is handled by a special JavaScript object called "window.openai". To assist developers, OpenAI provides an open-source Apps SDK UI library. It's built with Tailwind 4 and Radix, offering a bunch of pre-styled, accessible components so you don't have to build every single button and dropdown from scratch. This helps keep the look and feel consistent with the rest of the ChatGPT experience.
The MCP server: The app's backend logic
The final component is the MCP server. This is your app's backend, and developers are responsible for building, hosting, and maintaining this server, which powers the app's functionality.
This server has a few key responsibilities. First, it has to advertise its tools, which means it tells ChatGPT what actions the app can perform. Second, when ChatGPT decides to use one of those tools, the server executes the logic, whether that’s looking up information in a database or connecting to another API. Finally, it serves up the UI, sending the web components back to ChatGPT so they can be displayed to the user. To help with this, OpenAI provides official SDKs for popular languages like Node.js and Python to streamline the process of getting a server up and running.
Key use cases and opportunities for the ChatGPT apps SDK
This new app ecosystem unlocks some powerful ways for businesses to connect with customers and make their services more accessible, all inside a chat window.
Enhancing e-commerce and customer support
A primary application is in e-commerce and customer support. Imagine a shopping experience where you can ask a question, see product recommendations in a carousel, and even check out without ever leaving the conversation. We're already seeing this with launch partners like Booking.com for travel, Zillow for real estate, and Expedia for trip planning. They're making complex purchases feel as simple as a quick chat.
Building a custom support bot with the SDK is an option for teams with the necessary engineering resources. As an alternative, platforms like eesel AI's AI Agent offer a pre-built solution. It connects to a help desk, learns from past conversations and documentation, and can handle frontline support tickets without custom development.
Integrating creative and productivity tools
There's huge potential for creative and productivity tools to become part of the conversational flow. This integration can streamline workflows by making tools accessible through natural language.
Partners like Canva and Figma are already showing what’s possible. You could ask ChatGPT to outline a presentation for you, and the Canva app could instantly generate a full slide deck right there in the chat. Or you could use Spotify to create the perfect work playlist just by describing the vibe you're going for. This blurs the line between conversation and creation, making powerful tools feel more intuitive.
Limitations and challenges of the ChatGPT apps SDK
While the SDK offers significant potential, building a production-ready app involves complexities and resource commitments. It is important to understand these challenges before beginning development.
The development commitment
The SDK is a developer-focused toolkit. It is not a "no-code" or "low-code" platform. You need coding expertise in languages like Python or Node.js, frontend development skills to build the UI, and the knowledge to manage a server.
According to the official quickstart guide, the basic setup involves building both a web component and an MCP server, and then using a tool like ngrok to expose your local server to the internet just for testing. The process of building, deploying, and maintaining an MCP server is an ongoing engineering responsibility.
The learning curve of a new ecosystem
As a new technology, there is a learning curve. Developers need to get up to speed on new concepts like the Model Context Protocol and figure out how to manage state in a UI that exists inside a conversation.
The platform is still young, which means best practices, documentation, and community support are all still maturing. This can lead to more time spent troubleshooting and less time building. This approach differs from platforms designed for immediate use. For instance, with a tool like eesel AI, teams can define escalation rules and set behaviors in plain English rather than code, offering a different path to implementation.
An unclear path to monetization
Finally, monetization is a key consideration. OpenAI has introduced monetization options through something called the Agentic Commerce Protocol, which enables an "Instant Checkout" feature for apps.
Right now, this feature is only available to approved marketplace beta partners. For the average developer or business, the path to generating revenue from a ChatGPT app isn't fully paved yet. This can create a business risk for any company looking for a clear and immediate return on the investment it takes to build and maintain an app.
Understanding development costs
While the Apps SDK itself is open-source and free to use, the total cost of building and running a ChatGPT app goes way beyond the SDK.
Here’s a quick breakdown of the real costs you need to budget for:
- Developer Time: This is the big one. The cost of engineering hours for planning, building, testing, and iterating on your app will be your largest expense by far.
- Hosting Costs: Your MCP server has to live somewhere. You’ll have a monthly bill from a cloud provider like AWS, Google Cloud, or Azure to keep it running.
- Maintenance: Apps aren't "set it and forget it." There’s an ongoing cost for updating the app, fixing bugs that pop up, and making sure it stays compatible with any changes to the SDK.
- ChatGPT Subscription Fees: To use these apps in a secure business environment, your team and your customers will need a paid ChatGPT plan. The Business plan starts at $25 per user per month, and the Enterprise plan has custom pricing. These costs are separate from your app development expenses.
For a deeper dive into how these apps work and what the new ecosystem looks like, the following video provides a helpful introduction.
A video from Alejandro AO introducing the concept of ChatGPT Apps and the OpenAI Apps SDK, discussing how they extend the Model Context Protocol with tools and UI.
When is building a custom app the right choice?
The ChatGPT apps SDK is a massive step forward for conversational AI. It opens the door to creating incredibly rich and interactive experiences, and for developers, it's an exciting new frontier to explore.
It is a solution best suited for a specific audience: teams with dedicated engineering resources, a clear strategic reason for being on the ChatGPT platform, and the patience to navigate a new and constantly changing ecosystem. For many businesses, the primary goal is to solve customer problems efficiently.
For those looking to automate support, enhance agent workflows, or deploy a sales-focused chatbot, alternatives to custom development exist. Platforms such as eesel AI can integrate with existing tools to begin addressing customer issues.
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Article by
Kenneth Pangan
Writer and marketer for over ten years, Kenneth Pangan splits his time between history, politics, and art with plenty of interruptions from his dogs demanding attention.
