squadbase.yml

Squadbase uses a YAML-formatted file to describe how your app should be built and which cloud settings to apply at deploy time. By defining these parameters, the platform picks up the settings automatically during deployment.

The file is usually saved as squadbase.yml, but you can rename it by changing Setting file path in the dashboard. Required keys vary depending on the runtime and framework you choose.

version: '1'

# Build Settings
build:
  framework: streamlit        # streamlit | nextjs | fastapi | django | morph
  runtime: python3.9          # python3.9, python3.10, python3.11, python3.12, python3.13, node18, node20, node22
  package_manager: pip        # pip, poetry, uv, npm, yarn, pnpm
  entrypoint: src/app.py      # src/app.py
  # Custom Dockerfile settings
  dockerfile: Dockerfile      # Path to custom Dockerfile (optional)
  context: .
  build_args:
    ARG1: value1
    ARG2: value2

# Deployment Settings
deployment:
  provider: gcp
  aws:
    memory: 512
    timeout: 30
    provisioned_concurrency: 0
    ephemeral_storage: 512
  gcp:
    memory: 1Gi
    cpu: 1
    timeout: 300
    concurrency: 80
    min_instances: 0
    max_instances: 0
    ephemeral_storage: 10Gi
    region: us-central1

Build

Define build-time settings under build:.

Squadbase builds your app with Docker. For frameworks listed in framework, you don’t need to write your own Dockerfile—one is provided automatically. If you’re using an unsupported framework or need custom OS packages, you can supply your own Dockerfile.

Using an officially supported framework

Set the following keys when you rely on Squadbase’s built-in images:

Using a custom Dockerfile

If a Dockerfile exists in the project root, Squadbase ignores framework and builds with your Dockerfile instead. You can also specify a custom Dockerfile path using the dockerfile key.

Example:

build:
  dockerfile: docker/Dockerfile.prod
  context: .
  build_args:
    NODE_ENV: production
    API_KEY: ${API_KEY}

Deployment

Deployment settings go under deployment:. Choose the cloud provider with provider (defaults to aws).

AWS

GCP

Pricing model

Runtime charges are calculated on memory, request, and CPU. You pay for resources used according to the spec you define under deployment.