Connect a Git repository, pick the branch, and every push becomes a built, health checked, atomic release on your own servers. No shell scripts to babysit, no half finished deploy left behind when a build step fails.
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Deployment automation is the practice of turning a code change into a running release without manual steps: the tool checks out the commit, installs dependencies, builds assets, runs migrations, then switches traffic to the new release only after it passes a health check. Automated deployment removes the two things that break production most often, a forgotten step and a partial deploy. DeployManage runs that pipeline on servers you own, on any cloud, with the previous release kept on disk so a rollback is one click rather than a restore from backup.
Last updated August 2026
A webhook on the branch you nominate starts a deploy the moment code lands. If you would rather gate production, keep the trigger manual and let staging deploy automatically, which is the difference between continuous delivery and continuous deployment. Both models are one setting.
Composer, npm, asset bundling, container image pulls, whatever your stack needs, defined once and run identically on every release. A build step that exits non zero stops the deploy before it can touch live traffic.
Each deploy is assembled in its own timestamped directory while the current release keeps serving. Only when the build finishes and the health check passes does the symlink move. Users never see a half copied file tree.
Point the check at a URL that exercises the parts that actually break, the database connection, the cache, a queue ping. If it does not return healthy inside the timeout, the release is abandoned and the old one keeps serving.
Previous releases stay on disk, so reverting is a symlink switch measured in milliseconds rather than a redeploy. A failed health check rolls back on its own, and you can roll back by hand from the deploy log at any time.
Run migrations, warm caches, reload PHP FPM or restart queue workers at the right point in the release, before or after the switch. Ordering matters more than people expect, and getting it wrong is what turns a routine deploy into an outage.
Spin up a new box on Hetzner, DigitalOcean, AWS, Vultr, Linode or OVHcloud with your provider API key, or point DeployManage at a VPS you already run. The server gets a hardened base install, a firewall and SSL before anything deploys.
Authorize GitHub, GitLab or Bitbucket, choose the repository and the branch each environment tracks. Staging can follow develop while production follows main, so promotion is a merge instead of a checklist.
List the commands that turn a checkout into something runnable, then give the health check a URL and a timeout. This is the whole configuration for most applications, and it lives with the site rather than in someone's shell history.
The deploy log streams every command with its exit code. A green release means the health check passed and traffic moved. A red one means the previous release never stopped serving, which is exactly what you want from a failure.
The deployment automation category covers tools built for very different jobs, which is why roundups that rank them in one list are so unhelpful. Here is what each kind is genuinely good at, including where DeployManage is the wrong choice.
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| What you need | DeployManage | Jenkins or GitHub Actions | Ansible or Capistrano | Kubernetes tooling |
|---|---|---|---|---|
| Primary job | Provision servers and deploy releases to them | Run CI pipelines: build, test, then whatever you script | Configuration management and scripted deploys | Orchestrate containers across a cluster |
| Setup before your first deploy | Connect a cloud API key and a repository | Write a workflow file, add deploy keys and secrets | Write playbooks or a deploy recipe, keep them current | Build images, write manifests, run a cluster |
| Zero downtime atomic releases | Built in, releases plus a symlink switch | You script it yourself | Capistrano does this, Ansible is what you make it | Built in via rolling updates |
| Rollback | One click, previous releases kept on disk | Rerun an older workflow, if it still builds | Capistrano keeps releases, Ansible depends on your playbook | kubectl rollout undo |
| Server provisioning and hardening | Built in on any cloud | Out of scope | Ansible is strong here | Out of scope, the cluster is assumed |
| Runs your test suite | No, this is not a CI server | Yes, this is what they are for | No | No |
| Best for | Teams running apps on VPS instances they own | Teams who want full pipeline control and will maintain it | Sysadmin heavy shops with fleet configuration needs | Teams already committed to containers at scale |
| Wrong choice when | You want managed containers or a full CI test runner | Nobody on the team wants to own pipeline YAML | You just want push to deploy without writing recipes | You run three sites and do not need a cluster |
Twenty client sites on a handful of servers, each with its own repository and release history. Automated deployment means a junior developer can ship a content fix safely on a Friday, because the health check and the rollback are doing the worrying, not the person clicking deploy.
Nobody wants to own a Jenkins install or debug a workflow file at 2am. Connecting a repository and defining four build commands gets you the automation that matters, and the time saved goes back into the product.
If the current process is git pull on the server followed by a few commands from memory, deployment automation is the highest leverage change available. Every step becomes recorded, repeatable and reversible, and the person who wrote the script stops being a single point of failure.
A deploy log that records what shipped, from which commit, at what time and triggered by whom is usually what an auditor is asking for. Automation produces that record as a by product rather than as a document somebody maintains by hand.
The phrase covers more ground than it first appears. In a real pipeline there are five distinct jobs, and tools in this category differ enormously in how many of them they take on.
The trigger. Something has to decide that a deploy should happen. A webhook on a Git push is the common answer, but a scheduled window or a manual button are both legitimate, and the right choice depends on how much you trust your test suite.
The build. Turning a checkout into something runnable: dependencies installed, assets compiled, caches warmed. This should happen in isolation, away from the code currently serving traffic, so a failure costs you nothing.
The switch. Moving live traffic from the old release to the new one. Done properly this is atomic, a single filesystem operation, not a file copy that users can catch halfway through.
The verification. Deciding whether the new release actually works before it owns all the traffic. A process that started is not the same as an application that responds correctly, and the gap between those two facts is where most bad deploys live.
The record. What shipped, when, from which commit, and how to undo it. This is the part teams skip and then miss badly during an incident. It overlaps with what deployment management software is responsible for, and in practice you want both halves in the same tool.
These get used interchangeably and they should not be. The distinction is exactly one thing: whether a human approves the production release.
Continuous delivery means every change that passes the pipeline is automatically deployed to staging and is ready to ship, but a person decides when it goes to production. Continuous deployment removes that gate. Every change that passes the pipeline goes straight to customers.
Continuous deployment is the more impressive sounding option and the wrong default for most teams. It moves your entire safety budget onto your automated tests, which means it only works if those tests are genuinely trustworthy. If you cannot say with a straight face that a green pipeline means the application works, keep the manual gate on production and let staging deploy automatically. That is continuous delivery, and it captures most of the benefit at a fraction of the risk.
A good middle position, and the one most DeployManage users land on: automatic deploys to staging on every push, one click deploys to production, and an automatic rollback if the production health check fails. The human decides when, the automation handles how.
Most deployment automation that goes wrong fails in one of four predictable ways. All four are avoidable.
The build runs against live code. If your script does a git pull in the directory nginx is serving, there is a window where half the application is new and half is old. Composer or npm running mid request produces errors that are almost impossible to reproduce afterwards. Build in a separate directory and switch at the end.
Migrations and code go out in the wrong order. A migration that drops a column the current release still selects from will take the site down between the migration and the switch. Ship schema changes that are compatible with both the old and new code, deploy, then clean up in a later release. Our guide to zero downtime database migrations covers the expand and contract pattern in detail.
Success is measured by exit code alone. A deploy script that finishes without error has proved that the commands ran, not that the application works. An application can boot cleanly and still fail every request because an environment variable is missing. Verify with an HTTP request that touches the database.
There is no way back. If rolling back means redeploying an older commit and waiting for a full build, your recovery time is the length of your build. Keeping the previous release on disk turns that into a symlink switch. See how to plan a deployment rollback for the full strategy.
Category roundups tend to list features. These are the questions that actually separate the tools once you are using one daily.
Does it own the server, or assume one exists? This is the biggest fork in the category. Tools like Ansible and Capistrano deploy to infrastructure you provisioned some other way. DeployManage provisions the machine as well, which matters more than it sounds like when you need a second server in a hurry.
How much configuration lives outside the tool? If your pipeline depends on three secrets, a deploy key and a YAML file that only one person understands, you have moved the fragility rather than removed it.
What happens on failure, by default? Not what you can configure, what happens if you configure nothing. The safe default is that a failed release never receives traffic.
Can you leave? Deploying to servers you own on a cloud account in your name means the exit path is short. Your application runs on a normal Linux box with normal nginx and normal systemd, so worst case you keep the server and deploy some other way.
Is it a CI server, a deploy tool, or both? Running your test suite and deploying releases are different jobs. Many teams are best served by GitHub Actions for tests and a dedicated deploy tool for releases, and the two hand off cleanly over a webhook.
The pipeline is deliberately boring, because deploys are the wrong place to be clever.
A push to the branch you nominated fires a webhook. DeployManage clones the commit into a fresh timestamped release directory, links the shared paths that must survive a deploy such as your uploads directory and environment file, then runs your build commands in order. If any command exits non zero, the release is discarded and the currently live release is untouched.
With a clean build, migrations run, then the health check fires against the new release. Only on a healthy response does the symlink move and PHP FPM reload, so the process holding the old code path in its opcache picks up the new one. Queue workers restart last, after the code they will run is already live.
Older releases stay on disk for as many deploys back as you configure. Rolling back moves the symlink to a known good release and reloads, which takes about as long as a page refresh. Every step, its output and its exit code land in a deploy log tied to the commit, so the question of what changed has an answer rather than a guess.
The same pipeline runs whatever the stack. It is the atomic release model rather than anything framework specific, so a Laravel application, a Magento store or a Node service all go out the same way, with the build commands and the reload step adjusted to suit.
Deployment automation is the use of tooling to move a code change into a running environment without manual steps. The tool checks out the commit, installs dependencies, builds assets, runs migrations and switches traffic to the new release, usually only after a health check passes. It replaces hand run SSH commands with a repeatable, recorded and reversible process.
Deployment automation tools fall into four groups: CI servers that build and test then hand off (Jenkins, GitHub Actions, GitLab CI), configuration management and scripted deploy tools (Ansible, Capistrano), container orchestration (Kubernetes and its ecosystem), and server plus deploy platforms like DeployManage, Laravel Forge, Ploi and DeployHQ that provision machines and ship releases to them.
The only difference is the manual approval step before production. Continuous delivery automatically builds, tests and deploys every change to a staging environment, but a person decides when it reaches customers. Continuous deployment removes that gate, so every change that passes the pipeline goes live automatically. Continuous deployment demands a test suite you genuinely trust.
A trigger, usually a Git push webhook, starts the pipeline. The tool clones the commit into a fresh directory, links shared paths like uploads and config, runs the build commands, then runs a health check against the new release. If the check passes, an atomic symlink switch moves live traffic and services reload. If anything fails, the previous release keeps serving.
Not quite. CI/CD covers the whole path from commit to production, and continuous integration is specifically about building and testing every change. Deployment automation is the last stretch of that path, the part that puts a verified build onto servers and moves traffic to it. Many teams use a CI service for tests and a separate tool for deployment.
You already have the build and test half. The question is what your workflow does after the tests pass. If it SSHes in and runs git pull, you have automated the trigger but not the release, so you still get partial deploys and no rollback path. A deploy tool handles atomic releases, health checks and rollback, and GitHub Actions can trigger it over a webhook.
Fewer outages caused by forgotten steps, since the process runs identically every time. Faster recovery, because rollback is a symlink switch rather than a rebuild. More frequent shipping, since a deploy stops being an event. And a deploy log that answers what changed and when, which turns incident response from archaeology into reading.
Yes, if the release is atomic. Building the new release in a separate directory and switching a symlink means there is no moment where the application is half updated. The parts that still need care are database migrations, which must be compatible with both the old and new code, and reloading processes like PHP FPM that cache the resolved code path.
Deployment automation is the mechanics of getting a build onto servers. Release automation is the wider process around it: approvals, environment promotion from staging to production, change records and coordination between teams. Smaller teams need one tool that does both. Large enterprises often run a separate release management layer above their deploy tooling.
For most teams, automatic to staging and approved for production is the right balance. Fully automatic production deploys only make sense when your test suite is trustworthy enough that a green pipeline genuinely means the application works. An automatic rollback on a failed health check gives you much of the safety of a manual gate without the wait.
Connect a repository, provision a server on the cloud you already use, and let every push ship a health checked release with a rollback waiting behind it.
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