Overview

LaunchFlow provides tools that make it easy to build and deploy Python APIs in your own GCP / AWS account. You can import Postgres and other cloud resources in your Python code, then deploy everything to dedicated Environments that run in your own cloud account.

You can create and connect to cloud Resources in an Environment by simply importing them in your code. Our SDK provides ready-to-use client APIs for every Resource so you can immediately start using them in your application both locally and when deployed.

You can build and deploy Services in the same Environment as your Resources with a single command. LaunchFlow deploys your APIs to Cloud Run on GCP and ECS Fargate on AWS - your network, service accounts, and other configuration are automatically setup for you.

Everything you build with LaunchFlow deploys to your own GCP / AWS account, giving you full control over your infrastructure and the most cost savings possible.

TL;DR: LaunchFlow automates your GCP and AWS configuration / deployments so you can focus on writing your application code.

Define Resources

First you define your resources anywhere in your python code. We recommend putting your resources in a file called infra.py. But you are free to put them wherever.

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import launchflow as lf
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postgres = lf.gcp.CloudSQLPostgres("my-pg-database")
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This tells LaunchFlow that you would like a Postgres database named my-pg-database to be created in your GCP account. To trigger this creation you can simply run

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launchflow create
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This will prompt you to select the project and environment you would like a database to be created in, and LaunchFlow will do all the configuration for where the database lives and how to connect to it.

After this command the connection info is stored securely in your cloud account. This ensure sensitive information like database passwords are never stored in your code or in your git history.

To download the connection info you can call resource.connect() or await resource.connect_async(). This will update your resource with the connection info allowing your application to connect to the resource. For our database above we would do:

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# Connect synchronously
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postgres.connect()
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# Connect asynchronously
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await postgres.connect_async()
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Deploy Services

To deploy a service add a service to your launchflow.yaml code. Like:

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project: my-project
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environment: dev
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services:
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  - name: my-service
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    product: gcp_cloud_run
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This tells LaunchFlow the my-service should be deployed to GCP cloud run. When you run:

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launchflow deploy
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From the directory containing your launchflow.yaml file, LaunchFlow will deploy your service to the environment you specify. This will upload your code to an artifact bucket in your account and trigger a build in your cloud account (powered by CloudBuild on GCP and CodeBuild on AWS). Once the build is complete LaunchFlow will create a new revision of your service and deploy it for you.

GCP Projects and Environments

For each LaunchFlow environment we will create a new GCP project inside of this folder. This allows you to keep all of your resources and services organized and isolated from each other, helping ensure SOC2 compliance by having each environment have a dedicated network.

When you first sign up for LaunchFlow we create a GCP service account that will be used for admin operations. You will need to grant this service account access to your organization in order to use LaunchFlow with GCP. The following permissions are needed:

AWS Projects and Environments

For each LaunchFlow environment you create in an AWS project we will provision a VPC to contain and connect all resources. This allows you to keep all of your resources and services organized and isolated from each other, helping ensure SOC2 compliance by having each environment have a dedicated network.