Best Application Deployment Strategies in 2024

Modern standards for software development and user experience require developers to update their projects constantly. Deployment and integration have become continuous operations — a modern application is deployed daily. This is why having a working deployment technique is now more important than ever.

Today, systems are more flexible and scalable. They often use Cloud infrastructure, serverless approach, or AWS microservices to offer a more adaptive functionality. There are many development teams involved in the process, and such diversity can make deployment tricky. In this article, you’ll find out how to deploy an application using the most viable application deployment strategies.

So without further ado, let's dive into the top software deployment strategies for 2024.

Types of Deployment Strategies

Deployment strategy refers to the process of releasing an application into production. It involves a series of steps that ensure the application is correctly configured and ready to be used by end-users. The deployment process is an essential part of any software development lifecycle, as it determines how quickly and efficiently a new application or feature can be launched.

The importance of selecting the right deployment strategy cannot be overstated. An effective deployment strategy can significantly reduce downtime and minimize the risk of errors or bugs, resulting in a better user experience for your customers. On the other hand, a poor deployment strategy can lead to lost revenue, frustrated users, and damage to your brand reputation.

There are several types of deployment strategies to choose from, and the right one for your business depends on various factors, including the size of your team, the complexity of your application, and the frequency of your releases. We defined the most common software deployment models:

#1 Recreate Deployment

This is a standard deployment strategy example that requires developers to scale the previous version of the software before the app’s deployment. First, the developers shut down the initial version of the app to upload a new update. After the first version is scaled to zero and can be removed, the team uploads and deploys the next version. Every process is handled one at a time — it’s the main pattern requirement.

Pros

The recreate deployment strategy allows developers to deal with one scaling process at a time because it doesn’t allow parallel deployment. Therefore, the codebase is always organized and developers have full visibility of their workflows.

Cons

On the other hand, the app has to stop running temporarily. Active software can’t be deployed with this method. For critical applications that need to be available 24/7 (like medical platforms or server software), this strategy of deploying applications is not a good fit.

Use case

This deployment strategy is a good fit for applications that have a relatively low traffic volume and don't require continuous uptime. It's also suitable for applications that require a complete overhaul of the codebase or architecture during deployment, as it allows developers to focus on one scaling process at a time.

You could schedule the deployment during off-peak hours to minimize downtime, and use the one-at-a-time process to ensure that the codebase is always organized and developers have full visibility of their workflows.

#2 Rolling Deployment

With rolling updates, developers can upload several versions simultaneously — the number of active ones is called a window side. Developers can

• upload one instance at a time (setting up window size to 1)
• deploy apps in simultaneous updates in clusters.

For faster and safer deployment, developers use containers. Containers application deployment include Docker and Kubernetes, which are popular tools for isolating updated versions, enabling and disabling servers, and tracking changes.

Pros

A rolling update deployment pattern allows continuous deployment in the active application. The strategy reduces app downtime, which is crucial for critical applications.

Deployment teams can decide which window sizes provide the highest efficiency and visibility. Developers can redirect traffic from the updated targets before the instance is ready to accept users, avoiding security and performance issues.

Cons

If a team updates too many instances at a time, they risk losing visibility of their deployment workflow. It’s better to start with smaller window sizes, gradually increasing the number of processed updates. Also, it’s important to set up the algorithms in which users are routed to old and updated instances not to cause compatibility issues between old and new versions.

Use case

Rolling Deployment is particularly useful for large applications that require continuous deployment. It is commonly used by online gaming platforms, and other applications that require high availability and minimal downtime.

With Rolling Deployment, the platform can deploy the new feature to a subset of servers, test it with a small number of users, and then gradually roll it out to the rest of the servers. This ensures that the new feature is deployed smoothly and without any interruptions to the gaming experience.

Choosing the right deployment strategy is essential for ensuring a successful software release. Whether it's a blue-green deployment or a canary release, the key is to pick a strategy that aligns with your specific goals and use case. It's also important to test your deployment thoroughly and have a rollback plan in case things don't go as planned. By taking a strategic and measured approach to deployment, you can minimize risk and ensure a smooth rollout of your software.

#3 Blue/Green Deployment Pattern

A blue/green pattern combines a one-at-a-time and simultaneous approach to the Cloud application deployment. Developers run two versions of the application: the existing instance is blue, and green is the updated one. They can choose which version is live, and keep another in a testing mode. Both versions are technically active, but only one is public (usually blue). After developers finish testing the new application’s edition, they redirect the traffic to the green zone.

The main distinction of this application deployment evaluation cycle from other strategies is that you can save the blue environment. The team is always free to execute a rollback or use the previous functionality to work on future updates.

Pros

This software deployment strategy allows switching between old and updated versions seamlessly. Developers use load balancers to redirect traffic streams between the blue and green editions. In this way, the deployment team minimizes the risks of downtime.

If something goes wrong with uploading the green version, developers can always roll back to the blue one. Because both are separated, there are no risks of one version having a negative impact on another.

Cons

Even though downtime risks are low, the deployment team should use compatible data formats and stores to shift between the blue and green environments seamlessly.

Often developers need to run two versions of the application simultaneously, which leads to an increase in storage space, computing power, hardware costs, etc.

Use case

For instance, a banking application that processes financial transactions may choose to deploy updates using the blue/green deployment pattern to minimize the risk of any errors or bugs that could lead to financial losses for their customers. By keeping one version of the application live while testing the updated version, the bank can ensure that their customers can continue to access their accounts and conduct transactions without interruption.

#4 Canary Deployment

Canary deployment and tests allow publishing features one by one, not the full update versions. Developers are keeping the previous version active and comparing the performance of the updated instance to the original one.

The canary application deployment checklist is the following:

• Developers upload the new version (Version B) to the server.
• Most users are still redirected to Version A, while only a small portion interacts with Version B. Thus, developers can monitor performance and make improvements in Version B.
• Having made the necessary changes in Version B, developers redirect most traffic to the updated instance. The team measures the performance and compares it with the original version.
• When Version B is stable enough, developers can stop Version A and fully redirect the traffic to the updated codebase.

For canary deployment to be successful, developers have to set up clear performance metrics. The baseline version (the original one) and the canary version (the updated one) should be deployed simultaneously and under the same conditions (caching, connections, hash) for objective analysis.

Pros

Thanks to live performance testing, developers can reduce deployment risks and create a better user experience. Two app versions are working, so users don’t experience downtimes. Moreover, the canary version receives enough traffic to provide an objective picture of its load-handling capacities and performance speed.

Cons

One of the main drawbacks of the canary method is its time-consuming nature. Canary testing and deployment are done in several stages and require time for thorough monitoring and evaluation. Because of this, not all users will be able to benefit from the new features and upgrades immediately.

As in many deployment strategies with both versions running simultaneously, developers must keep in mind and ensure the compatibility of tech stack and databases.

Use case

For example, a business might use canary deployment as best deployment strategy when introducing a new payment gateway or checkout process. By testing the new feature on a small subset of users, the company can identify and resolve any issues before rolling out the update to the entire user base.

Canary deployment can also be used to test the compatibility of different software versions. For instance, if a company is planning to upgrade its application server or database software, it can use canary deployment to test the compatibility of the new software with the existing application before fully deploying the update.

#5 Shadow Deployment

This is a strategy where both versions are active simultaneously. Version A, the older one, is initially presented to the user and accepts the input. Version B receives traffic not directly from the load balancer but from Version A. This way, the risks of wrong redirects or poor performance are minimized — the operation is partially executed by the older version. Even if Version B fails, A can step in.

However, running operations through two versions prolongs the response time. You need to test the communication between A and B and minimize the delays.

How to execute a shadowing strategy for software deployment:

• Application-level implementations. Developers write functions that send input to a current and a new version of the application. Inputs and outputs can be processed simultaneously or asynchronously in a queue for higher precision. The team can divide inputs on the client side, setting up distinctive API targets in a browser or mobile device.
• Infrastructure level implementations. Developers configure a load balancer to the fork format, supporting endpoints of both versions. Developers need to ensure there is no duplication; the app must never request the same data twice. Version B should receive the user’s information from the first one; otherwise, users will have to enter payment or registration data twice.
• Shadow mode results evaluation. Teams pay attention to the data errors, compare the performance of both versions, and check if the second version accepted the input from the first one correctly.

Pros

Thanks to real-time user and performance testing, it’s easier for developers to achieve higher stability. If the new version performs poorly, the older one will back it. As users interact with both the tried-and-proven old version and the updated one, there is no impact on production by faults in services that process shadow data.

Cons

The fact that both Versions A and B run simultaneously doubles operations, and requests, and uses twice as much computing power, storage space, and servers. Consequently, the communication between the two versions takes a long time to set up and test. As a result, the outcome will not be 100% accurate as there’s still an involvement from the original version.

Use case

By shadowing the new payment method, the business can ensure that the new feature functions correctly and does not negatively impact the user experience before fully rolling it out. Additionally, if any issues arise with the new payment method, the business can easily revert back to the old payment method without any disruption to the user experience.

Final thoughts

Transferring to serverless engineering is an answer for established organizations and new businesses. It’s a method to lessen costs, improve item quality, abbreviate time to market, and spotlight advancement. It is anything but a simple move — going serverless requires examination and mindfulness. This is why we at TechMagic always select a deployment strategy concerning the needs of the product. Small non-critical services can be deployed with a recreate pattern, whereas 24/7 platforms need a complex, no-downtime approach.

Whether you need help deploying a new application or updating an existing one, TechMagic can assist you in selecting the right deployment strategies. Our experienced developers and DevOps engineers can help you deploy your application seamlessly and efficiently, ensuring that your users have a positive experience while using your product. Contact us to determine which deployment strategy works best for your software.

FAQs

1. What are the 3 main steps in the deployment process?

   Development, testing and monitoring are three stages of the deployment process.

2. What are some deployment strategies more?

   There are some types of deployment strategies:
   - Shadow deployment model
   - Canary deployment model
   - Ramped deployment model
   - Recreate deployment model
   - Blue/green deployment model