---
title: "Sandboxes for code interpreters in AI apps: isolated, ready in milliseconds"
description: "Promigence runs the code your AI app writes in an isolated sandbox that is ready in milliseconds, behind the sandbox API many AI apps already use."
url: "https://www.promigence.ai/for/code-interpreters"
site: "Promigence"
---

# Code interpreters that answer while your user is still looking

Promigence runs the code your AI app writes in an isolated sandbox that is ready in milliseconds. In our benchmark a code-interpreter session took 1.15 seconds from start to result at the median, and Promigence works with the sandbox API many AI apps already use.

**Who this is for:** Teams building AI analysts, chat-with-your-data features and any AI app that runs code for its users.

## What breaks today
### Your user is watching
A code-interpreter answer is interactive. Every second of sandbox start-up is a second your user stares at a spinner in your product.

### Model-written code needs a real boundary
The model writes the code, and anyone can prompt the model. It has to run somewhere isolated, with network access you control.

### Switching providers should not be a rewrite
Code written against one sandbox API should not have to change to move to a faster one.

## What changes with Promigence
### Ready in milliseconds
A sandbox is ready for its first command 8 milliseconds after it is requested.

### Isolated, with network you decide
Each session runs in its own isolated sandbox, which can sit behind an outbound allow-list.

### A drop-in for the API you use
Promigence works as a drop-in replacement for the sandbox API many AI apps already use, so moving over is a small code change.

## A clean sandbox, then the code your model wrote
```bash
# A fresh sandbox from the base image
promigence fork --count 1 --cap 1

# Run the model's code, isolated
promigence exec --sandbox "$SANDBOX" -- python3 analysis.py
```