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.
at 200 concurrent, all served
forks in six hours, zero failures
warm fork on the box
Three things that are true of almost every team doing this
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.
With Promigence underneath
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 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
Run your own workload on it, free
Send a repo and the command you run against it, whatever that is: an eval suite, an RL rollout, a CI job, a queue of coding tasks. We build the environment once, run it a thousand times, and send back the timings, the failures and an exact price. Free, once, on your real workload.
- 1,000 runs of your own command, on your own repo
- What each one cost in wall clock, and anything that failed
- Whether a failure was your code or the environment
- An exact price for your real volume
Not ready to hand over a repo? Read the quickstart or check the numbers first.