The Pros and Cons of Auto-Deploy From a DevOps Perspective
Tags: code, continuousdelivery, devops, featureflags
Published: 2023-12-08 14:52:24

---
title: The Pros and Cons of Auto-Deploy From a DevOps Perspective
published: true
date: 2023-12-06 20:19:30 UTC
tags: Code,continuousdelivery,devops,featureflags
canonical_url: https://www.split.io/blog/pros-cons-of-auto-deploy-from-devops-perspective/
---

Travel back in time to the early days of software development when deployment was a laborious, manual process. It required developers to expend considerable time moving code from development to production environments, a method prone to human error and inconsistencies.

## **What Is Auto-Deploy?**

Today, the software development landscape has dramatically changed. The term auto-deploy, once akin to science fiction for old-school developers, now stands as a cornerstone in modern DevOps. Auto-deploy refers to an automated process that ushers the code developers write to the right locales. This automation revolutionizes DevOps teams’ work, enabling focus on crafting flawless code instead of struggling with deployment particulars.

However, auto-deploy carries its own hurdles, including setup costs and potential security risks. Far from a magic bullet, it is a powerful tool that demands careful handling.

Let’s consider both the advantages and challenges of auto-deploy from a DevOps lens, and explore a balanced approach to harness the full potential of this tool.

## **Benefits of Auto-Deploy**

### **Improves Efficiency and Speed**

Auto-deploy significantly boosts efficiency by working round the clock. It eliminates manual hand-offs between team members, reducing wait times. When a developer submits a piece of code, they can move directly onto the next task. Meanwhile, the code seamlessly advances through the deployment pipeline. This automated process accomplishes tasks faster than humans, reducing the overall time-to-market for software products. As a result, you receive faster feedback, quicker iterations, and a superior product for your users. Auto-deploy doesn’t just offer speed, it delivers impactful speed.

### **Reduces Human Errors**

Auto-deploy also decreases the risk of human error. Manual processes can lead to mistakes, such as a developer unintentionally pushing a bug into the production environment. This can result in downtime and user dissatisfaction. With auto-deploy, there’s no room for such errors. The automation doesn’t get tired or distracted. It consistently tests and deploys the code according to predetermined procedures, ensuring a reliable and error-free deployment process.

### **Supports Scalability and Consistency**

Auto-deploy offers exceptional scalability. The process remains consistent whether you’re deploying to a few servers or hundreds. This scalability means you can grow your infrastructure without the deployment process becoming a bottleneck. Auto-deploy also ensures consistent deployments. Manual processes can vary, leading to inconsistent deployment. Automation ensures every deployment is uniform. It strictly follows set procedures, which minimizes surprises in production and simplifies troubleshooting. Auto-deploy supports growth while maintaining consistency, a key foundation for your expanding needs.

## **Drawbacks of Auto-Deploy**

### **Setup Costs and Complexity**

Auto-deploy implementation requires a considerable investment in time, money, and resources. The process of building automated deployment pipelines, integrating them with existing systems, and configuring them accurately can be particularly challenging for large-scale projects. This complexity often requires the expertise of specialized staff or additional training for existing employees, further adding to the costs. Additionally, maintaining these systems is an ongoing expense. As your software evolves, so must your deployment pipelines, which increases the cost. Auto-deploy can streamline your processes and boost productivity, but it’s crucial to understand the substantial initial and recurring investments involved.

### **Security Risks**

Automation, despite reducing human error, can create new security vulnerabilities. If a security flaw exists in the automation system, it can expose your entire codebase as auto-deploy automates code movement. Such vulnerabilities could allow hackers to gain unauthorized access or disrupt your operations. The automation scripts and tools could also be targets for hackers, who, in the worst-case scenario, could alter the deployment scripts to insert malicious code into the production environment. Therefore, it’s essential to have strong security measures, conduct regular security audits, and remain alert to potential threats when using auto-deploy.

### **Over-reliance and Lack of Manual Oversight**

Excessive use of auto-deploy can lead to an over-reliance on automation, resulting in a lack of manual oversight. If teams place too much trust in automated systems, they might neglect the need for manual reviews and checks. This absence of human scrutiny can let errors or issues go unnoticed. Automated systems can’t replace human intuition and critical thinking—they only do what they’re programmed to do. Additionally, over-reliance on automation can lead to skill erosion within your team. If team members aren’t actively involved in the deployment process, they might lose their understanding of it, which could cause problems when automated systems fail or require modifications. Thus, balancing auto-deploy with manual oversight is essential for a robust, efficient, and secure deployment process.

## **Finding the Perfect Equilibrium**

### **Understanding Continuous Integration and Continuous Delivery**

Embracing Continuous Integration (CI) and [Continuous Delivery](https://www.split.io/blog/how-to-achieve-continuous-delivery/) (CD) is key to achieving a balanced auto-deploy process. CI encourages developers to merge code changes into a central repository frequently, facilitating early detection and correction of potential issues. CD complements CI by automating the application delivery to selected environments, enhancing auto-deploy efficiency and effectiveness.

CI ensures harmonious integration of code changes made by various developers, similar to an orchestra conductor maintaining harmony. By running automated tests on each code check-in, CI provides instant feedback on any issues, saving time and resources.

CD, on its part, automates the delivery of integrated code to staging or production environments. CD ensures safe and efficient code delivery and eliminates the need for human intervention, reducing deployment errors. CI and CD together form a symbiotic relationship that continuously integrates, tests, and delivers code. This combination optimizes and streamlines the deployment process, [amplifying auto-deploy’s strengths] (https://www.split.io/blog/continuous-integration-delivery-deployment/) and mitigating its weaknesses.

### **Value of Manual Checks and Reviews**

Despite the automation benefits provided by CI/CD, human intuition and critical thinking remain indispensable. Manual checks and reviews are essential components of a balanced auto-deploy process.

Regular code reviews, conducted by peers or experienced team members, can identify potential issues or improvement areas that automated tests might miss. These reviews promote a culture of collective ownership and learning, allowing team members to share knowledge and improve their skills. They also help teams maintain a solid understanding of the code base and the deployment process.

Manual checks should not be confined to the code. They should also cover the deployment process and the application’s performance in production. Human observation can detect unusual patterns or behaviors that might not trigger automated alerts. Incorporating manual checks and reviews within an auto-deploy workflow ensures a robust and reliable system, combining automation’s benefits with human oversight’s strengths.

## **A Thoughtful Approach to Auto-Deploy in DevOps**

Auto-deploy is a mainstay in software development. It brings efficiency, speed, and precision to DevOps, despite its potential shortcomings.

The initial setup costs may seem expensive and the complexity intimidating, but the returns are worth it. By eliminating manual processes, you reduce human error and increase deployment consistency. This alters your team’s work dynamic, allowing for more concentration on innovative problem-solving instead of repetitive tasks.

While security concerns are legitimate, they’re not exclusive to auto-deploy. Any part of software development could offer hackers an entry point if left unprotected. The solution isn’t avoidance due to fear but investing in meticulous security audits, employing best practices, and maintaining vigilance.

The concern about over-reliance on automation and lack of manual oversight boils down to balance. Like how an orchestra needs a conductor, an automated deployment process requires human supervision. Automation serves as a support system rather than replacing the critical thinking and intuition of team members.

We need to combine the benefits of Continuous Integration and Continuous Delivery (https://www.split.io/blog/using-continuous-delivery-and-pipeline-deployment-to-streamline-collaboration/) with auto-deploy, coupled with manual checks and reviews. This provides an optimal combination of automated processes and skilled human insight.

The goal isn’t simply adopting auto-deploy because it’s trendy. It’s crucial to understand its capabilities and challenges and adopt a balanced approach for its effective use. Properly used, auto-deploy can offer immense benefits to your team.

Incorporating auto-deploy into your practices reminds us of surfing: there will be ups and downs, but maintaining balance lets you ride these waves to success.

## **How Feature Flags Can Support Auto-Deploy**

Feature flags, also known as feature toggles or feature switches, are a powerful tool in software engineering that lets you turn a feature or code block in your application on or off without changing or redeploying the code. Feature flags play an important role in enabling auto-deployment by allowing a means of controlling the release of features separately from the deployment process. Here’s a quick breakdown of how feature flags support auto-deployment:

### **Gradual Rollouts**

Feature flags enable you to gradually roll out features to a percentage of users rather than releasing them to all users at once. This is known as a progressive or canary release.

Auto-deployment systems can use feature flags to enable a feature for a small percentage of users initially. If there are issues, the deployment can be halted or rolled back easily without affecting all users.

### **Rollback Mechanism**

If something unexpected happens, auto-deployment systems can quickly roll back the changes by turning off the feature flag, effectively reverting the application to its previous state.

This rollback mechanism provides a safety net during auto-deployment, allowing teams to respond to issues rapidly without requiring a new deployment.

### **A/B Testing**

Feature flags enable A/B testing, where different versions of a feature can be exposed to different user groups at the same time to collect feedback and measure the impact of new functionality before a full release.

Auto-deployment systems can be configured to enable or disable specific feature variants based on the feature flags, facilitating the A/B testing process.

### **Feature Lifecycle Management**

Feature flags help manage the lifecycle of a feature from development to release and eventual retirement. Auto-deployment systems can use feature flags to turn on or off features at different stages without requiring code changes.

By incorporating feature flags into the deployment process, teams can gain more flexibility, mitigate risk associated with deployments, and ensure a smoother auto-deployment workflow. This approach aligns well with principles of continuous delivery and DevOps practices.

## **Using Version Control Systems to Support Auto-Deploy**

### **Git**

Using Git as part of an auto-deployment workflow involves using Git’s version control features to manage code changes, and integrating it with deployment tools to automate the process.

By combining Git with CI/CD practices and deployment automation, you can create a robust and efficient auto-deployment workflow that streamlines the release process while maintaining control and visibility into your codebase.

### **Github**

Using GitHub for auto-deployment involves leveraging GitHub Actions, which is GitHub’s built-in continuous integration and continuous deployment (CI/CD) platform. GitHub Actions allows you to define workflows using YAML files, automate build and deployment processes, and trigger actions based on events such as pushes, pull requests, or the creation of releases.

## **Deployment Automation Tools**

There are several deployment automation tools available, each with its strengths and capabilities. The choice often depends on things like type of application, infrastructure, and specific requirements of your deployment pipeline. Here’s a quick list of deployment automation tools:

- **Jenkins: ** A widely-used open-source automation server that supports building, deploying, and automating any project. It has a large and active community, and numerous plugins are available for extending its functionality.
- **GitLab CI/CD: ** GitLab provides integrated CI/CD features within its platform. It includes version control, continuous integration, and continuous deployment, all in a single application.
- **Travis CI: ** A cloud-based CI/CD service that integrates with GitHub, GitLab and Bitbucket repositories. It’s easy to set up and configure, and it supports various programming languages.
- **CircleCI: ** A cloud-based CI/CD platform that automates the software development process. It integrates with version control systems like GitHub and Bitbucket.
- **GitHub Actions: ** GitHub’s native CI/CD and automation platform. It allows you to define workflows using YAML files and provides tight integration with GitHub repositories.
- **TeamCity:** A CI/CD server developed by JetBrains. It supports building and deploying projects, and it offers powerful features such as build chains and build configurations.
- **Ansible:** A powerful open-source automation tool that can be used for configuration management, application deployment, and task automation. It uses a simple YAML syntax for defining tasks.
- **Docker: ** A containerization platform that simplifies application deployment by packaging applications and their dependencies into containers. Tools like Docker Compose can be used to define multi-container applications.
- **Kubernetes:** A container orchestration platform that automates the deployment, scaling, and management of containerized applications. It provides a powerful and flexible framework for deploying and managing distributed systems.
- **AWS CodeDeploy:** A fully managed deployment service that automates application deployments to Amazon EC2 instances, on-premises instances, and serverless Lambda functions.

### **Switch It On With Split**

Ready to start with auto-deploy? Don’t hesitate to take that first step. We’ll guide you from manual deployment to setting up automated pipelines to balancing manual checks. Together, we’ll plot a course that combines the best of both human and machine expertise for an efficient and secure software deployment process. Switch on a [trial account](https://www.split.io/signup/), schedule a [demo](https://www.split.io/demo-request/), or [contact us](https://www.split.io/company/contact/) for further questions.

### **Get Split Certified**

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The post [The Pros and Cons of Auto-Deploy From a DevOps Perspective](https://www.split.io/blog/pros-cons-of-auto-deploy-from-devops-perspective/) appeared first on [Split](https://www.split.io).