python-configuration

You can load environment variables into typed Python settings by defining a Settings class with pydantic_settings.BaseSettings and instantiating it. This externalizes configuration from code, enabling the same code to run in any environment without modification.

Pydantic configuration validation provides a fail-fast startup mechanism that catches misconfigurations early. By validating typed settings before your application runs, it prevents configuration drift and ensures environment-specific behavior is correct across development, staging, and production.

Pydantic-settings supports optional nested configurations and secrets management for environment-specific settings. It loads environment-based values into typed settings, allowing you to manage project-wide configuration and secrets across different environments securely.

The best way to manage environment-specific Python configuration is externalizing values into environment variables loaded through typed settings. Using pydantic-settings solves configuration drift by validating these environment-based values at startup, ensuring consistent behavior across development, staging, and production.

To prevent configuration drift between environments, externalize configuration from code using environment variables loaded into typed settings. This approach ensures the same code runs unmodified in development, staging, and production, with validation catching any misconfigurations at startup.