Divio Cloud overview

The Divio Cloud is a Docker-based platform-as-a-service. See Docker basics for an introduction to Docker and its key components.

The Divio Cloud offers a local development environment that replicates almost perfectly the Cloud environments in which applications run, eliminating many of the pain-points of deployment caused by having to deal with different environments in development and production.

In our architecture, we abstract functionality from configuration so that functional components can be made immutable and stateless wherever possible. This enables them to be replaced, added, moved and so on simply by spinning up new instances, without requiring manual configuration.

Divio Cloud infrastructure

Our Cloud is built on a Python/Django stack. Our client sites run in Docker containers. More information about our infrastructure can be provided on request.

Divio Cloud projects

The three environments

Each Divio Cloud project includes three environments, each of which will create a version of the website.

The three environments are created in Docker containers from the same images.

  • Local, running on your own computer
  • Test, running on our Cloud servers
  • Live, running on our Cloud servers

In our workflow, development is done locally, before being deployed to Test and finally to Live.

Project site stack

The stack running Cloud sites is:

Operating system
Ubuntu Linux
Web server/web application gateway
Divio loadbalancer plus uWSGI (local sites use the Django runserver.)
Postgres (Test and Live sites use an AWS database; Local sites use a database running in another local container.)


Divio Cloud projects represent web projects. Each project requires a frontend, however minimal - at the very least, a basic base.html template. In order to make Divio Cloud projects immediately useful, they each come with frontend files included. These are defined by the site’s Boilerplate, a set of default templates and static file.

Typically, a Boilerplate will define how the Django templates are structured and make opinionated choices about what JavaScript frameworks and CSS tools are used.

Various Boilerplates are provided as defaults, but it’s also possible to define and reuse your own.

Our simplest Boilerplates provide only basic HTML and CSS, but more sophisticated ones include advanced frontend tooling: NPM, webpack, Sass and other components.

Project repository branches

By default, each project’s code is in its develop branch. This is then pushed our Git server, where it can be deployed to the Test or Live servers (our strongly -recommended workflow is always to deploy to Test first),

However, on request different branches can be set for the Test and Live servers - for example, develop and master respectively.

In this workflow you would work on develop before manually merging into master, and then deploying Live.


A number of optimisations have been built into our Cloud deployment process to make deployments faster and more reliable.

Python packaging

We maintain our own Python Package Index, with which has pre-built platform-specific wheels for all Python packages.

Docker layer caching

We don’t use Docker-level layer caching, as certain cases can produce unexpected results:

  • Unpinned installation commands might install cached versions of software, even where the user expects a newer version.
  • Commands such as apt-get upgrade in a Dockerfile could similarly fail to pick up new changes.
  • Our clustered setup means that builds take place on different hosts. As Docker layer caching is local to each host, this could mean that subsequent builds use different versions, depending on what is in each host’s cache.