What’s New in Continuous Delivery? Trends and Predictions for 2025

Continuous delivery is a software development practice where code changes are automatically prepared for a release to production. It expands upon continuous integration by deploying all code changes to a testing environment and then to a production environment after the build stage. When properly implemented, it ensures developers always have a deployment-ready build artifact that has passed through a standardized test process. 

Continuous delivery encourages developers to automate testing beyond simple unit tests so they can verify application updates across multiple dimensions before deploying to customers. These tests may include UI testing, load testing, integration testing, API reliability testing, etc. This helps developers more thoroughly validate updates and pre-emptively discover issues. 

With cloud environments, it is easy and cost-effective to automate the creation and replication of multiple environments for testing, which was previously difficult to do on-premises.

Benefits of Continuous Delivery 

Continuous delivery has several advantages over traditional waterfall-style development:

• Simpler releases: Development teams spend less time preparing codebases for release and don’t combine multiple changes into a large, complex release. Developers can update and release code in small increments.
• Easier maintenance: Minor releases quickly reveal bugs in new code. When software is frequently deployed to production, it is easy to identify production issues, isolate a recent change that caused the issue, fix it, test, and redeploy. 
• Improved development velocity: In a continuous delivery environment, developers can iterate on software rapidly and deliver value to customers faster. New features can be accessed by customers much more quickly, and when customers have new requirements, development teams can rapidly respond to them.
• Improved quality: Continuous delivery makes releases more predictable, more reliable, and of higher quality. CD does not prevent bugs, but can catch them earlier in the development lifecycle and reduce their impact on customers.
• Less downtime: Removal of manual steps minimized the amount of human errors

Emerging Trends and Predictions in Continuous Delivery for 2025 

Evolution of the Continuous Delivery Market

The global market size for continuous delivery technology was calculated to be around USD 3.67 billion in 2023 and was projected to grow at a compound annual growth rate (CAGR) of 19.2% from 2024 to 2030. A major reason for this growth is the increasing need for faster software development. 

Organizations are often under pressure to release new features, updates, and improvements frequently to meet their customers’ expectations. Continuous delivery enables organizations to automate software development and deployment processes, which allows for faster and more reliable releases. This is especially important for markets like technology, finance, and retail.

Organizations across various industries have adopted digital transformation initiatives to help make their operations more efficient and improve customer experiences. Continuous delivery supports these efforts by enabling faster, more frequent software updates. 

Continuous Delivery Around the World

In North America, the share of the continuous delivery market was 36.0% in 2023. One of the main trends in this region is the widespread adoption of cloud-native technologies. Organizations across industries have been shifting to cloud platforms like AWS, Microsoft Azure, and Google Cloud, which support continuous delivery practices. 

In Europe, the market is growing at a significant CAGR of 18.8% from 2024 to 2030. European enterprises have been increasingly integrating AI and machine learning into CD pipelines to improve their operations, reduce manual errors, and make decision-making processes faster. AI-driven analytics are also used for predictive maintenance, helping identifying issues in advance, and to optimize workflows.

Asia Pacific is expected to grow at a CAGR of 21.1% from 2024 to 2030 due to an emphasis on mobile-first development, especially in countries with large mobile user bases, such as India, Indonesia, and the Philippines. Continuous delivery is necessary for mobile application developers who need to release frequent updates and maintain a consistent user experience across different devices. 

The Emergence of Low-Code Platforms

Low-code platforms are likely to become the next major trend in software development. These platforms allow developers to build applications without having to write code manually. Through a visual interface, users can drag and drop components to define workflows.

These platforms reduce the time required to develop applications, but they’re also going to have an impact on continuous delivery. Teams can use low-code platforms to create, deploy, and manage a range of applications much faster.

The Shift to GitOps

GitOps is a new approach to continuous delivery that’s gaining popularity. In GitOps, the entire delivery pipeline is defined in code and stored in a Git repository. This means that teams can version control the delivery process and roll back to previous versions if anything goes wrong.

GitOps also provides a way to automate the delivery process using Git as the control interface. This means that developers can trigger deployments automatically when new code is pushed to a repository, and roll back to previous versions automatically if there are issues.

AI-Driven CD Pipelines to Enable Continuous Integration Testing

Artificial Intelligence is expected to overhaul CI/CD pipelines as well as further simplify the SDLC. AI-driven CI/CD pipelines enable faster issue detection. They identify bottlenecks and provide potential solutions before developers become aware of the problem.

AI-powered pipelines help development and testing teams reduce the time taken to troubleshoot and debug. AI automates routine tasks such as setting up continuous integration testing environment setups. It also enables intelligent automation within continuous delivery pipelines, automating code reviews and testing.

Cloud-Native CI/CD Gains Steam

Cloud-native CI/CD approaches are increasing to simplify and automate the software delivery pipeline. The popularity of container orchestration tools such as Kubernetes is also responsible for the rise of cloud-native CI/CD. These tools allow easy management and deployment of containerized applications across different environments.

Cloud-native CI/CD pipelines improve agility and responsiveness to market conditions and ensure faster delivery of new features and bug fixes. They also help improve cloud resource utilization and reduce software development, deployment, and delivery costs.

5 Best Practices for Adopting Continuous Delivery 

Here are some best practices to consider for automating delivery effectively.

1. Develop Service Level Objectives (SLOs)

Service level objectives (SLOs) are a set of criteria that software must meet to satisfy stakeholder requirements. They are defined within a service level agreement (SLA) based on service level indicators (SLIs). Organizations can deliver higher-quality releases faster once SLOs are established and continuously tested at every stage of the development lifecycle.

Establish a multi-stage environment with built-in SLO-based quality gates that can orchestrate the CD workflow. Integrate tools for performance testing, chaos testing, and other types of testing into the pipeline. When the code is fully tested and can stand up to SLO-based quality evaluations, it is ready to deploy.

2. Automate SLO Evaluation with Quality Gates

With SLOs in place, there’s a framework to begin automating test evaluation. To enable automation, start by establishing quality gates to set out the criteria the software must meet before continuing to the next stage of the delivery pipeline. 

Quality gates take in data from multiple test tools, such as performance testing, integration testing, and observability data, and evaluate it against the criteria specified by the SLOs. This creates a consistent process that can be repeated and tuned. AI-assistance helps quickly pinpoint why a test may have failed and how to fix it.

3. Automate Every Repeatable Process

DevOps practitioners often overlook a manual step or two when automating the delivery pipeline, requiring additional action either before, during or after a release is deployed. This occurs for many reasons, including perceived difficulty or up-front cost, or the habit of involving a key member of the staff to make a decision. 

For CD processes to continuously improve and scale, it’s important to automate every repeatable process throughout software development, testing and deployment. While evaluating processes, make sure that the pipeline automates all tests, configuration changes, quality checks, and dependency gathering. Pay special attention to repeatable processes, even if they traditionally involve multiple manual steps or approvals.

4. Reduce Complexity Where Possible

The complexity of a pipeline grows with the number of tools used. Managing the process requires simplifying the multitude of tasks involved in checking the final product. In some cases, automation is built gradually and as needed, resulting in automation code spread out across many tools. This makes it harder to use or monitor these tools. 

Choose a solution that offers an SLO-based orchestration framework. These frameworks organizations simplify and automate the continuous delivery process. Having a central control plane for CD pipelines enables teams to collaborate more easily with all automation code in the same place.

5. Establish Observability and Continuous Monitoring

Having end-to-end observability is essential for managing a dynamic, cloud-native CD pipeline. It allows DevOps and SRE teams to ensure applications meet the relevant SLOs. The goal is to eliminate blind spots and enable efficient root-cause analysis of issues at any stage in the pipeline, which requires reliable telemetry, including metrics, logs, and traces.

Additional information to monitor includes user experience data and the context of processes, both upstream and downstream. Capturing code-level detail is important for debugging and troubleshooting accurately. Given that applications and services are often distributed and based on open-source technologies, this telemetry may come from disparate sources.