5 Ways to Deploy Code (Without Making Your Users Mad)

Picture this: You just spent weeks building an awesome new feature. It's fully tested and ready to go. But when you hit "Deploy," your entire application goes down for 5 minutes, and your users are met with a blank loading screen.

Not a good look.

In the world of DevOps and Cloud Infrastructure, how you roll out updates matters just as much as the code itself. AWS Elastic Beanstalk gives us 5 distinct deployment policies to handle this smoothly.

Let's break them down from simplest to most robust so you know exactly which one to pick for your next project.

1. All at Once (The Speed Demon)

This is the simplest method. Elastic Beanstalk takes all your existing servers, shuts them down, deploys the new code, and boots them back up simultaneously.

[ Old App ] ─> SHUTDOWN ALL ─> DEPLOY NEW ─> [ New App ]

• The Good: It’s incredibly fast.
• The Bad: Your app goes completely offline during the deploy.
• When to use it: Only in development environments where downtime doesn't matter. Never use this in production!

2. Rolling (The Line Worker)

Instead of updating everything at once, Beanstalk splits your servers into batches (e.g., 2 at a time). It takes the first batch offline, updates them, brings them back online, and then moves to the next batch.

Batch 1: [ Updating... ] Batch 2: [ Running Old App ]

Batch 1: [ Running New App ] Batch 2: [ Updating... ]

• The Good: No total downtime! Your app stays online.
• The Bad: While a batch is updating, your overall server capacity drops. Plus, users might experience a "mixed state" where refreshing switches them between the old and new version.
• When to use it: Production environments that can handle a temporary dip in bandwidth.

3. Rolling with Additional Batch (The Safe Substitution)

To fix the capacity problem of standard rolling, this policy launches a brand new batch of instances first. Once those new servers are healthy and running the updated code, Beanstalk starts rolling the update through the old servers.

[Old Servers: 100% Capacity ] + [ New Batch Added: +25% Capacity]

│

[Roll updates through old servers safely]

• The Good: Your application maintains 100% of its required capacity the entire time. No slowdowns for users.
• The Bad: It takes a bit longer to complete, and you temporarily pay for a few extra instances during the deployment window.
• When to use it: Great for production apps that experience steady, heavy traffic.

4. Immutable (The Clean Slate)
Instead of messing with your live servers, Beanstalk leaves them alone. It spins up a completely separate, parallel group of brand-new instances running the new code. Once the new group passes all health checks, traffic is instantly swapped over, and the old servers are terminated.

Group A (Live): [ Old App ] ──┐

                             ├───> Swaps traffic instantly!

Group B (New) : [ New App ] ──┘

• The Good: Insanely safe. If the new code crashes, Beanstalk just deletes the new group, and your live users never notice a thing. Rollback is effortless.
• The Bad: It doubles your server count during deployment, which can temporarily spike your cloud bill.
• When to use it: Highly critical production applications where deployment failure cannot be risked.

5. Traffic Splitting / Canary (The Diplomat)

This is the gold standard for testing features live. Beanstalk launches a new set of instances alongside the old ones but only sends a tiny fraction of real traffic (e.g., 10%) to the new version. If no errors pop up after a set amount of time, it rolls out the rest.

Traffic ──► [ 90% to Old Version ]

──► [ 10% to New Version (Testing) ]

• The Good: Low blast radius. If there is a hidden bug, it only impacts 10% of your users before you automatically roll it back.
• The Bad: Complex to configure and manage.
• When to use it: Major version upgrades or introducing massive features where you want real-world validation before a full release.

Summary Cheat Sheet:
Need speed in dev? 👉 All at Once
Need no downtime on a budget? 👉 Rolling
Need full capacity during deployment? 👉 Rolling with Additional Batch
Need the safest rollback strategy? 👉 Immutable
Want to test features on real users safely? 👉 Traffic Splitting

Which deployment policy does your team use at work? Let's talk about the pros and cons in the comments!