The 12-factor principles serve as a powerful framework, enabling us to engineer efficient, secure, and scalable software apps. But now, you can use it to make software environments - revolutionizing pipeline modularity and cloud automation. Why?

  • 12-Factor Principles are best practices widely adopted by developers since 2016
  • Operations as Code has been demonstrated in the case of Mobile Credential
  • Software Driven Automation is an emerging practice
  • Workflow Pipeline Modularity is essential in compliant environments

In this interactive guide, we will walk you through each factor adopted for the modern challenges of automating the cloud. Whether you are a software engineer or you run a Dev Team, this is what you need to know.

The 12-factor principles advocate for modularity in the development process. In the context of workflow pipelines, this modularity allows for the creation of self-contained and reusable steps that can be easily managed and updated.

The Twelve-Factor principles is a blueprint for designing robust, platform-independent environments that streamline Development, Operational, and Runtime processes.

This approach not only enhances the efficiency and reliability of the CI/CD pipeline but also ensures the consistent operation of containers across different stages of the deployment process.

By maintaining this consistency, we can ensure that our software environment remains stable and predictable, regardless of the scale or complexity of the build or deployment.

The journey begins with the first two factors of the 12-factor principles: "Codebase" & "Dependencies".

I. Codebase

The Codebase factor principle is the foundation of the pipeline. Every code change is continuously integrated, tested, and released from this single source of truth. This approach simplifies reviews and tracking changes, rolling back deployments, and maintaining consistency across all environments, making the CI/CD process more efficient and reliable.

This diagram illustrates the principle of using a single codebase tracked in revision control, leading to multiple deployments, a strategy that ensures continuous integration, consistency across deployments, and a unified source of truth for enhanced process reliability.

II. Dependencies

Next, we encounter Dependencies. This factor emphasizes the importance of explicitly declaring all dependencies. By managing dependencies with dedicated tools, we ensure that the application has everything it needs to run.

This schema demonstrates the principle of explicitly declaring and isolating dependencies, a critical practice for ensuring application integrity and operational predictability.

Continuous Integration Advantages

  • A Software Bill of Materials (SBOM) provides a comprehensive inventory of all dependencies, enhancing transparency.
  • Utilizing vulnerability scanning tooling to detect and address potential security issues promptly.
  • Automated tools, like Renovate or Dependabot keep dependencies updated, mitigating the risk of security vulnerabilities.
  • Treating Docker images or repositories as dependencies ensures consistency across all environments.

Together, the Codebase and Dependencies factors set the stage for a robust, secure, and efficient CI/CD pipeline, laying the groundwork for the automation of the software environment.

As we progress along the CI/CD pipeline, we encounter the next two factors: "Config" & "Backing Services".

III. Config

The Config factor emphasizes the importance of separating configuration from the application code. This principle is key to maintaining continuous compliance and ensuring the secure configuration of infrastructure components. By managing environment-specific configurations through pipeline variables, secrets, or YAML configuration files, sensitive data is kept out of the codebase, enhancing security.

This visualization emphasizes the practice of storing configuration in the environment, a key strategy that enhances security and scalability by separating it from the code.

In practical terms, the Config factor encourages the design of applications, scripts, logic, Docker images, and other components as modular entities with parameters. This modular design allows for repeated initialization and customization through parameter values that come from an external configuration.

For instance, consider a Docker image designed to deploy a web server. Instead of hardcoding the server's port number or database credentials into the image, these values can be passed as environment variables when the container is started.

This not only enhances the reusability and flexibility of the code but also improves security by keeping sensitive data out of the codebase. It also aligns with the principles of Infrastructure as Code (IaC), where infrastructure setup is managed using code, allowing for easy versioning, sharing, and reuse.

Such a separation of concerns between code and configuration ultimately leads to more secure, maintainable, and scalable software solutions. This approach also enables efficient orchestration of deployment releases, ensuring that each environment is correctly configured for its specific purpose that allows the same image to be used in different contexts - for example, in development, testing, and production environments - simply by changing the configuration parameters without modifying the code, facilitating compliance automation.

IV. Backing Services

Following the Config factor, we encounter the Backing Services factor which is a perfect example of why configurations should be managed separately.

This schema highlights the principle of treating backing services as attached resources, a practice that streamlines connectivity and enhances modularity.

This factor treats all services that the application interacts with as attached resources, which can be easily connected or disconnected without any changes to the application's code.

For instance, your application might rely on a database, a message queue, and a caching system. Instead of hardcoding the details of these services into your application, you would store them as part of your external configuration. Each service would be addressed via a URL or other locator stored in the configuration. This approach allows you to switch out a backing service without any code changes. For example, you could switch from a PostgreSQL database to a MySQL database just by changing the database URL in your configuration.

This principle extends beyond databases and similar services. It applies to any kind of service your application might consume, including other cloud services, third-party APIs, and even functions or microservices within your own application.

By treating these backing services as attached resources, your system becomes more modular and easier to manage. You can swap out, upgrade, or reconfigure services with minimal disruption to your application. This not only makes your system more resilient and adaptable but also promotes a clean separation of concerns, where each part of your system does one thing and does it well.

The journey continues with the "Build, Release, Run", and "Processes" factors.

V. Build, Release, Run

This illustration emphasizes the principle of maintaining a strict separation between build, release and run stages, a key strategy for bolstering reliability throughout the software lifecycle management process.

The "Build, Release, Run" factor emphasizes the need for clear delineation between the software factory stages. This principle ensures that each stage has its distinct responsibilities and processes.


The "Build" stage is dedicated to compiling, testing, building, and tagging artifacts. The application is compiled from code into a versioned artifact. This could be a binary, a Docker image, or any other package that represents your application. This stage often includes running unit tests, static code analysis, and other checks to ensure the code's quality. The output of this stage is an immutable artifact that can be promoted to the next stages.


The "Release" stage manages the configuration of app lifecycle environments and deployment releases, ensuring that the application is ready and compliant for deployment. It takes the build artifact and combines it with the appropriate configuration for the target environment. This could involve injecting environment variables, adjusting configuration files, or performing other setup tasks. The result is a release, a versioned package that is ready to be run in a specific environment. This stage is also where any final checks or compliance audits would take place.


The "Run" stage is responsible for executing the application in the target environment and monitoring its availability and performance. It's where the release is deployed and run in the target environment. The application is started, and any necessary services (like databases or message queues) are connected. The application's performance is monitored, and any issues are logged for analysis.

This structured approach enhances the overall process's reliability, enabling rigorous testing and integration before release, and facilitating better tracking and management of each stage.

VI. Processes

This visualization underscores the principle of treating processes as first-class citizens, an approach that significantly contributes to scalability and resilience, thereby enhancing the robustness of the system design.

The "Processes" factor underscores the importance of executing the application as one or more stateless processes. This principle is particularly crucial during the Run stage.

This stateless design has several advantages. It allows your application to scale horizontally, by adding more instances, to handle increased load. It also makes your application more resilient to failures. If an instance fails, it can be replaced without any loss of data or service continuity.

For example, consider a web application that serves requests from users. Each request could be handled by any instance of your application. If the application is stateless, it doesn't matter which instance handles a request because none of them rely on any local state to process requests. Instead, they fetch any necessary data from a backing service, process the request, and then return the response.

This approach ensures that the application remains responsive and resilient, regardless of the load or any potential disruptions, thereby maintaining a high level of service availability.

As we near the end of the CI/CD pipeline, we encounter the "Port Binding", "Concurrency", and "Disposability" factors.

VII. Port Binding

This visual illustrates the practice of exporting services via port binding, a strategy that facilitates modular communication and interoperability.

The "Port Binding" factor allows your application to be accessible to other applications or services over the network, effectively making it a backing service itself.

This approach is particularly useful in a microservices architecture, where each service is a self-contained application that communicates with other services over the network. Each service can be developed, deployed, and scaled independently, enhancing the modularity and flexibility of your system.

In a CI/CD pipeline, this principle enables applications to communicate with each other through defined ports, enhancing the modularity of the system. By adhering to the "Port Binding" factor, you can build applications that are self-contained, modular, scalable, and resilient, making your system more robust and easier to manage

VIII. Concurrency

This diagram contrasts the inefficient use of heavyweight OS threads with the efficient strategy of scaling through a process model.

The Concurrency factor promotes the use of the process model for scaling, as opposed to relying on heavyweight OS threads. This principle is particularly important in the context of scaling, where applications need to manage increased load.

By leveraging containerization technologies, each process can be packaged into an isolated unit as Docker container allowing for efficient scaling. This approach enables applications to handle increased load by simply increasing the number of running containers, rather traditional multi-threading, as containers are lightweight and can be started and stopped quickly.

This model not only enhances the scalability of applications but also improves their resilience and availability. By isolating processes in containers, failures are contained and do not affect the entire system, ensuring that the application remains available even under high load or during component failures.

IX. Disposability

This illustration highlights the importance of fast startup and graceful shutdown processes, key factors in achieving agility and stability in application disposability.

The Disposability factor emphasizes the need for applications to be disposable, meaning they can start or stop at a moment's notice.

For instance, consider a web application that needs to handle a sudden surge in traffic. If your application's processes are disposable, you can quickly start more instances to handle the increased load. Conversely, when the load decreases, you can just as quickly stop the extra instances to save resources.

This disposability also applies when deploying a new version of your application. You can start new instances of the new version alongside the old ones, and once they are up and running, stop the old instances. This allows for zero-downtime deployments, where your application remains available throughout the deployment process.

Containerization technologies like Docker and Kubernetes enhance this disposability by allowing you to package your application into lightweight, isolated containers that can be started and stopped quickly and reliably. This makes your application more robust, scalable, and maintainable, and allows you to respond quickly and effectively to changes in load or failures

Finally, we reach the end of the CI/CD pipeline with the "Dev/Prod Parity", "Logs", and "Admin Processes factors".

X. Dev/Prod Parity

This visualization emphasizes the practice of maintaining similarity across development, staging, and production environments, a strategy that ensures predictable deployments and minimizes 'works on my machine' issues.

The Dev/Prod Parity factor champions the idea of maintaining consistency across development, staging, and production environments. This principle is pivotal in a CI/CD pipeline, particularly for continuous delivery, where frequent deployments necessitate a high degree of trust in the deployment process.

For example, consider an application that uses a database. In development, you might be tempted to use a lightweight, in-memory database for ease of use. However, if your production environment uses a different, more robust database system, this could lead to unexpected behavior when you deploy your application. Differences in the behavior, performance, or capabilities of the two database systems could cause your application to work in development but fail in production.

To avoid this, you should use the same database system in development, staging, and production. The data might be different - you might use a small sample of data in development, a larger dataset for staging, and the full dataset in production - but the database system itself should be the same.

This principle extends to all aspects of your environment: the operating system, the network topology, the configuration, the backing services, and so on. This approach minimizes the "it works on my machine" syndrome and reduces the risk of unexpected behavior when moving from development to production.

By adhering to this factor, we ensure that configurations are sourced and managed in a standardized manner across all environments.

XI. Logs

The Logs factor advocates for treating logs as event streams and directing them to a standard output. This approach transforms logs from static files into dynamic streams of information, enhancing real-time monitoring and analysis capabilities.

For example, consider a web application that logs each request it receives. Instead of writing these logs to a file, the application would write them to stdout. If the application is running in a Docker container, Docker can capture these logs and direct them to a specified location. This could be a log file on the host system, a log management service like Logstash or Fluentd, or a cloud-based log analysis tool like Google Stackdriver or AWS CloudWatch.

In any pipeline, this principle is vital for observability and debugging. By treating logs as event streams, we can capture and analyze data in real-time, providing immediate insights into application performance and potential issues. It can also help measure key performance indicators (KPIs) like error rates, response times, and throughput.

This diagram underscores the approach of treating logs as event streams, a practice that facilitates centralized management, improved monitoring, and in-depth analysis.

Moreover, the collected data can be utilized to analyze development DORA (Deployment Frequency, Lead Time for Changes, Time to Restore Service, and Change Failure Rate) metrics. This approach not only aids in identifying and resolving issues promptly but also contributes to continuous improvement by providing valuable insights into the development process.

XII. Admin processes

This visualization illustrates the practice of executing administrative tasks as one-off processes, a strategy that enhances operational efficiency by streamlining workflows.

The Admin Processes factor emphasizes the importance of executing administrative tasks as one-off processes in an environment identical to the regular long-running processes of the app. This principle is crucial in a CI/CD pipeline, ensuring uniform treatment of all processes, which enhances maintainability and predictability.

These tasks could include database migrations, console sessions, maintenance tasks, and other occasional tasks that need to be performed as part of managing your application.

For example, consider a web application that needs to perform a database migration to update the schema. Instead of running this migration on a developer's local machine or a dedicated admin server, you would run it as a one-off process in the same environment as your application. This ensures that the migration has the same configuration, the same backing services, and the same code as your application, reducing the risk of inconsistencies or errors.

By adopting a Pipeline-as-Code (PaC) approach, we can manage these administrative tasks using source-coded YAML configurations. This approach ensures that these tasks are version-controlled, peer-reviewed, and treated with the same level of rigor as application code.

Moreover, by incorporating compliance gates into these processes, we can ensure that each task meets the necessary security and compliance standards before execution. This approach not only enhances the security and reliability of these tasks but also ensures that they are fully auditable and traceable.

In essence, this factor transforms administrative tasks from potential points of inconsistency into well-defined, controlled, and predictable processes, thereby enhancing the overall reliability and integrity of the system.

Understanding the key concepts in software environment automation is pivotal, as it forms the backbone of efficient, scalable, and secure software development in the cloud era. The integration of everything-as-code principles and containerization has transformed traditional development practices, leading to significant improvements in operational efficiency and application deployment. Here are some core concepts to grasp:

  • 12-Factor Principles: These are a collection of best practices for building applications that thrive in a SaaS environment. They cover everything from codebase management to administrative processes, ensuring that applications are inherently robust and suited for cloud platforms. Key practices include separating configuration from code, embracing a microservices architecture for consistency, treating backing services as attached resources, and running applications as stateless processes.
  • DevOps Workflow Integration: The 12-factor principles can be extended beyond application development to include the entire DevOps workflow. Treating the workflow as an application means it can be source-controlled, tested, packaged, and delivered with the same rigor applied to software development, thereby enhancing the overall process's efficiency and security.
  • Environmental Consistency: A central tenet of the 12-factor methodology is maintaining uniformity across all environments. This ensures that from development through to production, configurations are consistent which leads to predictable outcomes at each stage of the lifecycle.
  • Modularity and Pipeline Efficiency: The principles advocate for a modular approach in both development and operational workflows. This results in creating self-contained, reusable components that streamline continuous integration/continuous deployment (CI/CD) pipelines. Such modularity also guarantees that container operations remain consistent irrespective of build complexity or deployment scale.

The above concepts form an essential framework for those venturing into automating software environments and aim to provide clarity on how modern DevOps practices can be structured for optimal performance.

  • CI/CD: GitHub Actions, Azure DevOps, Bitbucket Pipelines - automates the software development lifecycle, ensures integration, testing, and release, increases the speed of delivery, and ensures adherence to software security and quality best practices.
  • Containers & Orchestration: Docker, Kubernetes, Azure Container Instances, Google Cloud Run - provides a robust mechanism for the deployment, scaling, and operations of application containers across clusters of hosts, providing a consistent application environment.
  • Deployment Releases & Configurations: Octopus Deploy, Ansible, Terraform - orchestrates the rollout of infrastructure, applications, services, and database updates while managing configurations-as-code across various lifecycle environments and applications.
  • Logging and Monitoring: Log Analytics Workspace, ELK Stack, Container Insights - collects, stores, and analyzes log data, providing real-time insights into operational performance and security, helping to identify issues quickly and improve overall performance.
  • Cloud Services: Azure App Service, Google Cloud Functions - fully managed platforms that are used for building, deploying, and scaling web apps and APIs, simplifying the process of managing and scaling applications.
  • Programming Languages: Node.js, Python - programming languages are used for developing server-side and networking applications.
  • Scripting Languages: Bash, PowerShell - scripting languages for task automation and system management.
  • Package Management: NPM, NuGet - Tools for managing and sharing software packages across teams and environments.