Why We Built APIMint

Every product team I have ever worked on has hit the same wall. The frontend is a day of work. The design is a day of work. The backend is a week of work. Two weeks if anything goes wrong.

The web has been like this for two decades. You write a controller. You wire up the database. You build the schema validation. You add auth, rate limiting, logging. You stand up an environment. You document the endpoint so the frontend dev (or your future self) can actually call it. By the time you ship, the original idea has cooled off and you have already moved on.

We built APIMint because we are tired of waiting for the backend.

What changed

Two things changed at the same time. First, language models got good enough at writing code that the boilerplate part of building an API endpoint can be expressed as a prompt. Not "generate me a todo app" prompts. Real ones. "Take a Stripe webhook, extract the customer email and amount, return JSON with the next steps." The model understands the intent, picks the right shape, writes the validation, handles the edge cases.

Second, the runtime around an API endpoint has commoditized. Auth, rate limiting, logging, schemas, versioning, docs - none of these are interesting problems anymore. They are infrastructure. You should not have to assemble them by hand for every endpoint you ship.

Put those two things together and the obvious move is to make the entire backend layer something you describe in plain english and ship in seconds.

What APIMint actually does

You describe what you want. APIMint generates the handler code, infers the input and output schemas, writes test cases, and deploys the endpoint to a real HTTPS URL with auth and rate limiting baked in.

You get a dashboard with live request inspection, latency breakdowns, cost tracking, request replay, and version rollback. You get auto-generated docs you can share with anyone. You get an SDK in TypeScript and Python you can install with one command.

Composable APIs let you chain endpoints together into a pipeline and ship the result as a single new endpoint. Data connectors let your handler talk to Supabase, Airtable, or any Postgres database. Every Pro endpoint is automatically MCP-compatible so AI agents can discover and call it without any extra glue code.

The whole thing runs on the same model you would use anyway. We just removed the parts that were not adding any value.

Where we go from here

The first version of APIMint solves the small-and-fast case. Most internal APIs. Most webhook handlers. Most "I just need this one endpoint to exist by tomorrow" cases.

The next version pushes into bigger workflows. Long-running jobs. Streaming responses. Multi-step pipelines with branching. Stateful agents that hold a session across calls. We have most of the primitives in place; we just need to expose them.

If you have ever stared at a sprint board and thought "this would already be live if I could just describe the endpoint and have it exist," try APIMint. We are building the tool we wish we had every single day for the last ten years.