Create Mock APIs from CSV in Seconds
Stop building fake JSON by hand. Turn spreadsheets and CSV files into realistic REST APIs instantly—for frontend development, AI-generated apps, prototypes, and internal tools.
- Frontend development
- AI-generated apps
- Prototypes
- Internal tools
The problem
Why would you build fake JSON by hand ever again?
To unblock the UI, developers hand-write JSON, lean on outdated mock APIs and JSONPlaceholder, spin up temporary backends, or maintain fake data manually.
Hand-written JSON
You invent fields and rows by hand, then babysit them every time the shape of the data changes.
Outdated mock APIs
Old mock servers and fixtures drift away from the data your product actually returns.
JSONPlaceholder & generic mocks
Generic services hand everyone the same posts and users—never your schema, enums, or edge cases.
Throwaway backends
Spinning up a temporary server or database just to return read-only rows is wasted effort.
Most mock APIs become technical debt almost immediately.
How it works
From spreadsheet to live endpoint in five steps.
Export data as CSV
A real export or representative sample—headers in row one.
Upload to API Butler
Hosted parsing—no mock-server repo to clone or maintain.
API generated instantly
Copy your live GET URL from the dashboard in seconds.
Connect your application
Point fetch calls at the endpoint—optional API key rules.
Develop against real data
Build UI and flows on rows that resemble production.
Connect your application
const res = await fetch(
"https://api.getapibutler.com/v1/apis/products?limit=25",
{ headers: { "X-API-Key": process.env.API_BUTLER_KEY } }
);
const { data, meta } = await res.json();
Why real data wins
Real data creates better software than placeholder JSON.
Building against responses that resemble production means fewer surprises later and a UI that already handles the messy reality of your data.
Realistic responses
Payloads carry your column names, value ranges, and volumes—not invented placeholders.
Real-world edge cases
Long strings, nulls, duplicates, and empty results surface in development instead of production.
Better testing
Exercise loading, empty, and error states over real HTTP with pagination and filtering metadata.
Faster development
No fixture maintenance—upload a new CSV and your mock reflects the latest data instantly.
AI app workflows
AI generates applications. API Butler provides realistic data behind them.
Cursor, Claude Code, and other AI tools scaffold full apps in minutes—but they still need something real to talk to.
AI tools can generate
- Dashboards
- Admin panels
- CRM systems
- Internal tools
But they still need
- Realistic APIs
- Structured data
- Testable endpoints
Use cases
Mock APIs that match how you build.
Frontend
Frontend Development
Wire lists, tables, and detail views against believable responses before the backend exists.
React
React Applications
Feed typed fetch hooks and components with stable JSON shaped like your real data.
AI
AI-generated Apps
Give coding agents a single endpoint instead of pasting giant blobs of fake JSON.
Internal
Internal Tools
Back lightweight ops tools with CSV exports your team already trusts.
Dashboards
Admin Dashboards
Populate charts and tables with realistic rows for review and sign-off.
CRM
CRM Prototypes
Prototype contact, deal, and pipeline views on representative customer data.
Catalog
Product Catalogs
Serve SKUs, prices, and categories from a spreadsheet as a queryable API.
Inventory
Inventory Systems
Stand in for a stock service with real quantities, locations, and statuses.
Prompt for Cursor
Build a dashboard on your mock API in one prompt.
Paste your API Butler endpoint and drop this into Cursor, Claude Code, or Codex. For guardrailed production variants, see the full prompt library.
Create a React dashboard that consumes this API Butler endpoint. Generate typed fetch hooks, loading states, filtering and error handling.
Endpoint (paste yours):
[PASTE_API_BUTLER_ENDPOINT_HERE]
More prompts: AI agent prompts
Comparison
API Butler vs JSONPlaceholder.
JSONPlaceholder
- × Generic fake data
- × Unrealistic structures
- × Same canned posts and users for everyone
API Butler
- Real data from your CSV
- Realistic responses with pagination metadata
- Project-specific APIs shaped like your domain
Technical example
Request and response shape.
GET /v1/apis/products200 OK {
"data": [
{
"id": 1,
"sku": "SKU-1042",
"name": "Wireless Charger",
"price": 34.99,
"stock": 89
}
],
"meta": {
"limit": 25,
"offset": 0,
"count": 1,
"total": 412
}
}
Paths and fields follow your dashboard and CSV headers — API usage.
FAQ
Mock APIs from CSV.
What is a mock API?
A mock API is an HTTP endpoint that stands in for a backend during development—used to unblock frontend work, run demos, and exercise loading and error states before production services exist.
How do I create a mock API with API Butler?
Upload a CSV with headers as the first row. API Butler generates a hosted GET endpoint that returns your rows as JSON, ready for fetch calls from your app or tests.
Can I use my own data?
Yes. Any CSV you provide becomes the source for responses—schemas and sample values match your export instead of a generic catalog baked into someone else's mock server.
Is this better than JSONPlaceholder?
JSONPlaceholder is useful for quick demos with fixed canned payloads. API Butler fits when you need payloads shaped like your domain—with your columns, filters, and pagination—without maintaining bespoke fixture files.
Can I update the mock data later?
Yes. Upload a new CSV in the dashboard to refresh rows while keeping the same endpoint identifier where configured.
Upload CSV. Get Mock API. Done.
Build against real APIs, not placeholder JSON.
Upload a CSV—get a live GET endpoint your frontend, AI app, or prototype can trust in seconds.