Kubernetes Deployment Strategies: 6 Types to Consider

Deploying applications to Kubernetes can be a nerve-wracking experience. You're managing containers, pods, services, and configurations—and every little piece needs to work right or the whole system could fail.

And failure means downtime, degraded performance, and frustrated users. Not to mention the potential for new bugs to be unleashed upon your entire user base simultaneously.

Fortunately, employing the right Kubernetes deployment strategy can help you roll out new features with confidence. Choosing the right strategy helps you:

Minimize risk when rolling out new features
Maintain service availability during updates
Better manage resources across your cluster
Quickly roll back if something goes wrong
Test new versions of the application with real traffic before full release

Bugs and software problems are inevitable. However, the disruptions and issues they cause don’t have to be. The right Kubernetes deployment strategy will position you to ship better products and features, while also having the capabilities to reverse if (and when) things don’t go according to plan.

This guide will show you everything you need to know about the different Kubernetes deployment strategies at your disposal.

What is a Kubernetes deployment strategy?

A Kubernetes deployment strategy is your plan for updating applications in production without things going sideways. It’s your playbook for swapping out the old version of your application for the new one—while keeping your services running and your users happy.

It’s more than just updating your YAML files and hoping for the best (or at least it should be). A proper deployment strategy defines exactly how your new application version will replace the old one, including:

How many pods you'll replace at once
Whether you'll run old and new versions side by side
What happens if something breaks
How traffic gets routed during the update
How to roll back if needed

The strategy you choose impacts everything from your application's availability to your resource usage to your ability to test new features safely. And while Kubernetes provides several built-in deployment strategies, each has its own tradeoffs.

For example, you might choose a more cautious approach for your payment processing service and gradually shift traffic to new pods while keeping the old ones around for quick rollback. But for an internal dashboard that can handle a few minutes of downtime? A simpler, faster strategy might make more sense.

Ultimately, it’s all about matching your deployment strategy to your specific needs—your application architecture, your DevOps team's capabilities, your users' expectations, and your business requirements.

Kubernetes deployments vs. alternatives

Before we get into the deployment strategies, let's look at what makes deployments different from other Kubernetes services and resources:

Deployments vs. Pods

Pods are the smallest deployable units in Kubernetes clusters—they run your containers
Deployments manage sets of pods, handling updates and scaling automatically
If a pod fails, deployments can automatically replace it
Deployments make it easy to roll back to previous versions

Deployments vs. ReplicaSets

ReplicaSets guarantee a desired number of pod replicas are running
Deployments manage ReplicaSets for you, adding update and rollback capabilities
Think of ReplicaSets as the engine, and Deployments as the driver's controls

Deployments vs. StatefulSets

Deployments work best for stateless applications
StatefulSets maintain pod identity and desired state—critical for databases
StatefulSets update pods in order, while deployments can update in parallel

Deployments vs. DaemonSets

DaemonSets run one pod per node (great for logging or monitoring)
Deployments let you choose how many replicas to run and where
DaemonSets update pods automatically when nodes are added or removed

6 types of Kubernetes deployment strategies

There's no one-size-fits-all approach to deploying applications on Kubernetes. Each strategy provides different tradeoffs between speed, safety, complexity, and resource usage. Some prioritize zero-downtime updates, while others focus on testing in production or managing risk through gradual rollouts.

Let’s look at the different strategies, how they work, and when to use them:

Recreate deployment
Rolling update
Blue/green deployment
Canary deployment
Shadow deployment
A/B testing deployment

1. Recreate deployment

A recreate deployment strategy does exactly what it sounds like—it terminates all the existing pods running your old version before spinning up new ones with your updated application.

It’s like flipping a switch: old version off, new version on.

Here's what it looks like in practice:

All existing pods running version 1.0 are terminated
Kubernetes waits for them to shut down completely
New pods with version 2.0 are created
Service resumes once the new deployment pods are ready

Here's a basic YAML configuration:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-app
spec:
  replicas: 3
  strategy:
    type: Recreate
  selector:
    matchLabels:
      app: my-app
  template:
    metadata:
      labels:
        app: my-app
    spec:
      containers:
      - name: my-app
        image: my-app:2.0

When to use recreate deployment

The recreate strategy makes the most sense when:

Your application can't handle running multiple versions simultaneously
You're working in development environments where downtime is acceptable
You need to run database schema migrations between versions
You want the simplest possible deployment strategy
Resource constraints prevent running both old and new versions at once

Challenges

The biggest tradeoff is downtime. Your application will be completely unavailable during the transition from old to new versions. The duration depends on how long it takes to:

Terminate all old pods
Start up new pods
Complete any initialization processes
Pass readiness checks

Plus, you'll want to consider that if something goes wrong with the new version, you'll face additional downtime while rolling back.

2. Rolling update

Rolling updates are Kubernetes' default deployment strategy—and for good reason. Instead of replacing all pods at once, rolling deployments gradually swap out old pods for new ones. This means your application stays available during the update, and you can catch issues before they affect all users.

Here's what the rolling update process looks like in practice:

A new pod with version 2.0 is created
Once the new pod is healthy and ready, an old pod running version 1.0 is removed
This process repeats until all pods are running the new version of the application
If any new pod fails to start, the deployment process can be paused or rolled back

Here's a basic YAML configuration:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-app
spec:
  replicas: 4
  strategy:
    type: RollingUpdate
    rollingUpdate:
      maxSurge: 1
      maxUnavailable: 1
  selector:
    matchLabels:
      app: my-app
  template:
    metadata:
      labels:
        app: my-app
    spec:
      containers:
      - name: my-app
        image: my-app:2.0

When to use rolling update strategy

Rolling updates are a great default choice when:

You need zero-downtime deployments
Your application can handle running multiple versions of an application simultaneously
You want to catch issues early before affecting all users
You need to control the pace of updates
Resource constraints prevent running double capacity (unlike blue/green)

Challenges

Rolling updates aren't perfect, though:

Both versions of your application need to run simultaneously
Database schema changes can be tricky to coordinate
Users might get inconsistent experiences during the rollout
Network connections may need to handle version differences
Rolling back can take time if issues are discovered late in the update

3. Blue/Green deployment

With blue/green application deployment, you run two identical environments—blue (current) and green (new)—and switch traffic between them. When it's time to update, you deploy the new version to the inactive environment, test it thoroughly, and then flip the switch.

It's a bit more complex than rolling updates, but it gives you a higher level of certainty. You can test your new version in a production environment with real data before any users see it.

Here's what it looks like in practice:

Blue environment serves all production traffic (version 1.0)
Green environment is created and updated (version 2.0)
Green environment is tested with real production data
Once verified, traffic is switched from blue to green
Blue environment stays available for quick rollback

Here's a basic YAML configuration:

# Blue deployment
apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-app-blue
spec:
  replicas: 3
  selector:
    matchLabels:
      app: my-app
      version: blue
  template:
    metadata:
      labels:
        app: my-app
        version: blue
    spec:
      containers:
      - name: my-app
        image: my-app:1.0

---
# Green deployment
apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-app-green
spec:
  replicas: 3
  selector:
    matchLabels:
      app: my-app
      version: green
  template:
    metadata:
      labels:
        app: my-app
        version: green
    spec:
      containers:
      - name: my-app
        image: my-app:2.0

---
# Service for routing traffic
apiVersion: v1
kind: Service
metadata:
  name: my-app
spec:
  selector:
    app: my-app
    version: blue  # Switch to 'green' when ready
  ports:
    - protocol: TCP
      port: 80
      targetPort: 8080

When to use blue/green deployment

Blue/green deployments are a great option when:

You need to test thoroughly in a production environment
Downtime during switchover isn't an issue
You want fast rollbacks (just switch back to blue)
Your application requires extensive warm-up time
You're making significant changes that need validation
Database compatibility isn't a concern between versions

Challenges

It’s not all sunshine and roses, though. The main challenges with blue/green deployments:

You need double the resources during deployment
Database migrations require special handling
Service discovery and DNS caching can complicate the switch
Stateful applications need careful planning
Cost implications of running two environments

4. Canary deployment

You release your new version to a small subset of users or traffic first. This gives you an opportunity to spot any signs of trouble before rolling out to everyone.

Canary deployments are like having a trusted group of beta testers (but in production). If something goes wrong, you've limited the blast radius. And if everything looks good, you can gradually increase traffic to the new version.

Here's what it looks like in practice:

Most traffic (say, 95%) goes to version 1.0
A small portion (5%) gets routed to version 2.0
Monitor for errors, performance issues, and user feedback
Gradually increase traffic to version 2.0 if all looks good
Roll back quickly if any issues appear

Here's a basic YAML configuration:

# Main deployment (version 1.0)
apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-app-stable
spec:
  replicas: 9  # 90% of pods
  selector:
    matchLabels:
      app: my-app
      version: stable
  template:
    metadata:
      labels:
        app: my-app
        version: stable
    spec:
      containers:
      - name: my-app
        image: my-app:1.0

---
# Canary deployment (version 2.0)
apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-app-canary
spec:
  replicas: 1  # 10% of pods
  selector:
    matchLabels:
      app: my-app
      version: canary
  template:
    metadata:
      labels:
        app: my-app
        version: canary
    spec:
      containers:
      - name: my-app
        image: my-app:2.0

When to use canary deployments

Canary releases are perfect when:

You want to test new functionality with real users and traffic
You need to validate performance in production
You're making significant changes that could impact users
You want to gather feedback before full rollout
Risk management is a top priority

Challenges

Here are a few things to keep in mind with canary deployments:

You need good monitoring and metrics to detect issues
Traffic splitting can be complex to set up
Session handling needs careful consideration
Database schema changes require special planning
Testing with a small subset might miss edge cases

Shadow deployments (sometimes called mirror deployments) are equivalent to a "try before you buy" strategy. You run your new version alongside the existing one and send it a copy of all live traffic. However, the responses from the new version are thrown away. Only the current version handles real user traffic.

It’s simply a practice run with real data. Your new version gets battle-tested with production traffic, but if it breaks, no users are affected because they're still being served by the stable version.

Here's what it looks like in practice:

Version 1.0 handles all user traffic
Version 2.0 receives a copy of the traffic
Both versions process requests, but only version 1.0 responds to users
Compare metrics, logs, and performance between versions
Switch to version 2.0 only after thorough validation

Here's a basic configuration approach:

# Main deployment (version 1.0)
apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-app-prod
spec:
  replicas: 3
  selector:
    matchLabels:
      app: my-app
      version: prod
  template:
    metadata:
      labels:
        app: my-app
        version: prod
    spec:
      containers:
      - name: my-app
        image: my-app:1.0

---
# Shadow deployment (version 2.0)
apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-app-shadow
spec:
  replicas: 3
  selector:
    matchLabels:
      app: my-app
      version: shadow
  template:
    metadata:
      labels:
        app: my-app
        version: shadow
    spec:
      containers:
      - name: my-app
        image: my-app:2.0

When to use shadow deployments

Consider shadow testing when:

You're making major architectural changes
Performance testing matters
You need to validate with real production patterns
You're dealing with complex microservice interactions
Failure in production costs are extremely high
You need to test with real data volumes

Challenges

Shadow deployments have a few obstacles, though:

You need double the computing resources
Complex to set up proper traffic mirroring
Handling stateful operations requires careful planning
Difficult to test actual user interactions
Monitoring and comparing metrics can be tricky

6. A/B testing deployment

Instead of assuming your new version is better, you can prove it by running different versions simultaneously and comparing their performance.

You split your traffic between version A and version B, measure everything from user behavior to system performance, and let the data tell you which version wins.

Here's what it looks like in practice:

Version A (control) and Version B (variant) run simultaneously
Traffic is split between versions based on defined rules
Metrics are collected for both versions
Statistical analysis determines which performs better
The winning version is rolled out to all end-users

Here's a basic YAML configuration:

# Version A deployment
apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-app-a
spec:
  replicas: 3
  selector:
    matchLabels:
      app: my-app
      version: A
  template:
    metadata:
      labels:
        app: my-app
        version: A
    spec:
      containers:
      - name: my-app
        image: my-app:1.0

---
# Version B deployment
apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-app-b
spec:
  replicas: 3
  selector:
    matchLabels:
      app: my-app
      version: B
  template:
    metadata:
      labels:
        app: my-app
        version: B
    spec:
      containers:
      - name: my-app
        image: my-app:2.0

When to use A/B testing deployments

A/B testing makes sense when:

You need data to validate changes
You're testing UX improvements
You’re focusing on performance optimization
You want to compare different implementations
You need to validate hypotheses about user behavior

Challenges

A/B testing has a few challenges:

Need clear success metrics before starting
Statistical significance requires sufficient traffic
Session persistence can complicate testing
Multiple variants increase complexity
Analysis needs to account for external factors

Improve your risk management with feature flags

Unfortunately, even the most carefully planned Kubernetes deployment strategy can't eliminate all risks. However, combining your deployment strategy with feature flags gives you an extra layer of control and safety.

Think of it this way: your deployment strategy handles how code gets to production, while feature flags control what happens once it's there. This separation of deployment from release means you can:

Deploy code without exposing features
Test in production safely
Roll back instantly without pod changes
Target specific users or regions
Gradually release features to validate performance

How LaunchDarkly works with Kubernetes

LaunchDarkly integrates seamlessly with any Kubernetes deployment strategy:

With recreate deployments:

Keep new features off during the initial deployment
Enable features gradually after pods are stable
Kill problematic features without redeploying

With rolling updates:

Maintain feature consistency across pod versions
Test new features before starting the rollout
Control feature exposure independently of pod updates

With blue/green:

Test features in green environment before switching
Maintain consistent feature states across environments
Roll back features without switching environments

With canary:

Fine-tune exposure beyond pod-level traffic splitting
Target specific users or segments for testing
Control multiple features independently
Get feature-level metrics to inform rollout decisions

And feature flags aren't just for deployments—they help you manage risk throughout your application lifecycle:

Emergency kill switches for critical features
A/B testing for performance optimization
Regional or segment-specific releases
Capacity management during high-traffic periods
Progressive feature rollouts
Quick incident response without code changes

Don't just ship and hope. Combine smart deployment strategies with feature management to deploy with confidence. Start your free full-access 14-day trial to try for yourself.

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6 Kubernetes Deployment Strategies (with Sample Code)

Like what you read?

What is a Kubernetes deployment strategy?

Kubernetes deployments vs. alternatives

Deployments vs. Pods

Deployments vs. ReplicaSets

Deployments vs. StatefulSets

Deployments vs. DaemonSets

6 types of Kubernetes deployment strategies

1. Recreate deployment

When to use recreate deployment

Challenges

2. Rolling update

When to use rolling update strategy

Challenges

3. Blue/Green deployment

When to use blue/green deployment

Challenges

4. Canary deployment

When to use canary deployments

Challenges

When to use shadow deployments

Challenges

6. A/B testing deployment

When to use A/B testing deployments

Challenges

Improve your risk management with feature flags

How LaunchDarkly works with Kubernetes

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