Isolated On-Demand Preview Environments
Why isolated on-demand preview environments?
Most teams using Garden use Kubernetes for production. This means they already have their Dockerfiles, manifests and/or Helm charts.
Garden lets them re-use these resources to create isolated preview environments on-demand so that they can:
Review changes for every pull request in a production-like environment
Easily share work in progress, even before pushing their code
Test out their changes as they develop
If your staging environment is a bottleneck where changes get queued up, isolated preview environments might be the solution.
Check out how Slite uses Garden to clear up their once congested staging environment.
How does it work?
Developers run the garden deploy command from their laptops to create a preview environment in their own namespace in the team's remote Kubernetes cluster.
Similarly, Garden can be run from CI pipelines to create isolated preview environments with each pull request, using e.g. the pull request number to isolate the environment. For example, you may have a CI job that runs garden deploy --env preview.
Garden's powerful templating engine ensures that namespaces and hostnames are unique across users and CI runs—and Garden's smart caching ensures creating these environments is blazing fast.
Key features
View URLs, logs, and command history with the Garden dashboard
Accelerate build times with remote image builds and smart caching
Isolate environments with Garden's template syntax
How can my team get on-demand preview environments?
Teams typically adopt Garden in a few phases and setting up on-demand preview environments tends to be the first one.
So with that in mind, these are the recommended next steps:
Go through our Quickstart guide
Check out the First Project tutorial and/or accompanying video
Add actions to build and deploy your project
Follow our guide on environments and namespaces to ensure each preview environment is isolated
Join our Discord community 🌸 for access to Garden's dedicated Community Engineers and our AI chatbot 🤖 trained on our docs.
Further Reading
Examples
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