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AI in Your Product
How we add AI to Bubble.io apps — without blowing up costs or leaking keys
Semantic search, summaries, document Q&A and in-app copilots, added to the Bubble app you already run. We've built Bubble apps since 2019; this is how we wire AI into them properly.
What does adding AI to a Bubble.io app involve?
Adding AI to a Bubble.io app means connecting your app to a language model so it can search, summarise, answer and write using your app's data — called from server-side workflows, with keys kept private and usage limits in place. It's different from Bubble's own AI Agent, which helps you build the app in the editor. Here we're talking about AI features your users use inside the finished product.
Do you need AI in your Bubble app?
- "Users can't find records because search only matches exact words."
- "Our users read long threads and notes just to get the gist."
- "Customers upload documents and then email us questions about them."
- "We added an OpenAI call through the API Connector, and now we're worried about the bill and the key."
- "We want an assistant inside the app that knows the user's own data."
Which AI features can we add to a Bubble app?
- Semantic search that finds records by meaning, using embeddings stored in a vector database.
- Summaries of long records, conversations or documents, generated once and stored.
- Document Q&A where users ask questions about files they've uploaded and get answers with references.
- An in-app copilot that answers questions about the user's own data and helps them complete tasks.
- Content generation such as first drafts of descriptions, replies or reports, always editable.
- Classification that tags, routes or prioritises new items automatically.
API Connector, backend workflows or n8n: which architecture?
There are three common ways to wire AI into Bubble. The right one depends on the feature:
| API Connector called directly | Backend workflows | n8n as middleware | |
|---|---|---|---|
| How it works | A workflow calls the model's API and shows the result | Calls run in Bubble's backend, often scheduled or on a list | Bubble sends a request to n8n, which runs the AI steps and returns the result |
| Best for | Simple, fast, one-step features | Processing many records, background jobs | Multi-step AI, RAG, many tools involved |
| Watch out for | Never expose keys or call from the page without limits | Workload units on large lists | One more system to host and maintain |
| Example | "Summarise this note" button | Tag 5,000 existing records overnight | Q&A over uploaded documents |
Most apps end up using two of the three. We pick per feature and explain why in the proposal.
How does an AI feature run inside Bubble?
- Trigger. A user clicks a button, saves a record or asks a question.
- Workflow. A backend workflow collects the data this user is allowed to use.
- Limits. It checks usage limits so one user can't run up your bill.
- Model. The request goes to the model through the API Connector or n8n, with the key kept private.
- Save. The result is stored on the record, so it isn't paid for twice, and shown to the user.
How do you keep AI costs and keys under control in Bubble?
- Keys stay server-side. API keys go in API Connector fields marked Private, so they're never sent to the browser.
- Token limits on each request, and usage limits per user and per day.
- Caching: summaries and embeddings are stored and reused instead of regenerated.
- The right model per task: smaller, cheaper models for tagging and short text; larger ones only where quality needs it.
- Workload awareness: AI workflows are designed so they don't add unnecessary Bubble workload units — see how Bubble.io pricing and workload units work.
On privacy: only the fields a feature needs are sent to the model, Bubble's privacy rules still decide what each user can see, and a privately hosted model can be used when data must not go to a commercial API.
How long does it take and what does it cost?
A first AI feature in an existing Bubble app typically takes 2–4 weeks. It starts at $1,500, and model usage is paid to the provider based on how much your users use it. We estimate running costs before you commit.
After launch, the feature can be included in your Bubble maintenance plan or our monthly AI care. Payments are milestone-based, and the launch includes one month of free bug fixing.
When is adding AI to a Bubble app not a fit?
Good fit when…
- Users search, read or write a lot in your app
- Your data is structured in Bubble's database
- You can name the step the feature saves
- You're fine with model usage as a running cost
Not a fit when…
- The app's database or privacy rules need fixing first
- The feature is for show rather than daily use
- Answers must be exact every time with no review
- You'd rather build the whole product on an AI builder — see our comparison below
Frequently asked questions
Can you add OpenAI or Claude to an existing Bubble.io app?
Yes. Models from OpenAI, Anthropic and others can be connected to an existing Bubble app through the API Connector, backend workflows or n8n as middleware. The app doesn't need to be rebuilt; the AI features are added to the workflows and pages you already have.
How do I keep my OpenAI API key safe in Bubble?
Store the key in an API Connector field marked Private, so Bubble keeps it on the server and never sends it to the browser. Call the model from backend workflows rather than directly from the page, and add usage limits so a single user can't run up costs.
Should I use the API Connector or n8n for AI in Bubble?
Use the API Connector for simple, single-step features such as a summary button. Use backend workflows for processing many records in the background. Use n8n as middleware when a feature needs several AI steps, document search or many connected tools, such as question answering over uploaded files.
How much does it cost to add AI to a Bubble app?
At Webziper, a first AI feature in an existing Bubble app starts at $1,500. Model usage is paid to the provider and depends on how often users trigger the feature, which we estimate before the build. Caching results and choosing the right model keep running costs predictable.
Is Bubble's AI Agent the same as adding AI features to my app?
No. Bubble's AI Agent helps you build and edit your app inside the Bubble editor. Adding AI features means your users get AI inside the finished product, such as semantic search, summaries or document Q&A, powered by a language model your app calls.
Have a Bubble app that could use AI?
Send us your editor link. We'll suggest the feature worth adding first, the right architecture and the running cost.
Book a Free Call →