Dockerfile Studio
Point Dockerfile Studio at a repository and AI writes a production-ready Dockerfile and .dockerignore, explains every line, checks it, and opens a pull request.
Dockerfile Studio reads a repository in a sandbox, works out how the app builds and runs, and writes a production-ready Dockerfile and .dockerignore (and, if you want, a docker-compose.yml for local development). It explains every line, runs best-practice checks, and opens the files as a pull request for your team to review.
The result is an AI suggestion: it's labeled Review before merging, and nothing is ever merged or pushed to your default branch for you.
How your code is handled
- Each analysis clones the repository into a single-use sandbox with no network, deleted as soon as the analysis ends. Infraista keeps only the generated files and the detection summary.
- From
.env.examplefiles, only variable names are read, never values. Secret values never go into generated files. - Source code isn't sent to the AI, and your code, prompts and generated files are never used to train AI models.
- Infraista writes to your repository only when someone opens a pull request, and never to the base branch.
Also in
- Code Security: a Dockerfile finding can be fixed with AI, which improves your existing Dockerfile and opens a pull request.
- Pipelines: when a new pipeline's template builds an image and the repository has no Dockerfile, Dockerfile Studio can write one and include it in the same pull request.
Where to go next
Generate a Dockerfile
Refine and open a pull request
Settings and limits
Dockerfile Studio API
Storage and billing
How Container Registry storage is measured and billed per GB each month above the included amount, and how to keep your storage down with retention rules.
Generate a Dockerfile
Pick a repository and branch, check what Dockerfile Studio detects about how your app builds and runs, choose a base image and options, and generate.