DevOps: The complete guide to culture, technology, and tools

What is the DevOps approach?

DevOps is an information technology (IT) approach that encourages collaboration, communication, and integration between software developers and IT operations staff. The primary purpose of DevOps is to improve the pace and reliability of software delivery, enabling continuous, frequent updates that deliver value to customers.

The DevOps team works together to create a consistent development, testing, and production environment and automates the development pipeline to make software delivery efficient, predictable, sustainable, and secure.

DevOps gives developers better control over their infrastructure and a clearer understanding of the production environment. It also encourages operations specialists to be involved from the onset of the development process. It creates a culture of shared ownership and responsibility for software that runs and delivers value in production.

What is a DevOps culture?

DevOps is not just a process or a set of practices; it’s also an organizational culture. DevOps culture focuses on small interdisciplinary teams that can work independently and are jointly responsible for the user experience delivered by a software product. The DevOps team lives in production and focuses on improving the product’s live usage.

A DevOps culture has the following essential elements:

DevOps teams adopt agile practices and integrate development and operations into each team’s responsibilities. Teams work in small iterations, striving to improve the end-to-end delivery of customer value and removing waste and obstacles from the process. Teams are jointly responsible, eliminating silos or finger-pointing.
DevOps teams apply a growth mindset — they use monitoring and telemetry to collect evidence in production and observe results in real time. They experiment with new features in production, using techniques like canary or blue/green deployments, to quickly collect data, test features, and use the results to drive continuous improvement.
DevOps teams focus on time to mitigate (TTM) and time to remediate (TTR) rather than the mean time between failures (MTBF). In contrast to traditional waterfall teams that made significant efforts to prevent problems in the field, the DevOps team recognizes that failures will happen, so their ability to detect and respond to an issue quickly is crucial.
DevOps teams think in terms of competencies, not roles — this includes development and operational skills. All team members share responsibility for running services. Both developers and operations are responsible for live services, and all of them may share a rotating on-call schedule. If you built it, you’re responsible for running it.

DevOps vs Agile

DevOps, Agile, and Lead Software Development are wholly compatible with each other. You can draw on aspects of all three approaches to create a high-performing development team and process.

	Agile	DevOps
Process	Communication between the team and customers is continuous, and frequent changes are made to the software to ensure quality. Better suited for complex projects.	Focuses on frequent testing and delivery, but communication is primarily between developers and IT operations. Better suited for end-to-end processes.
Teams	Allows small teams to complete tasks faster. Agile methods encourage all members to share responsibilities equally instead of assigning specific responsibilities to team members. So, every agile team member should be able to handle or assign any part of the project at any time.	Suitable for large teams. Skill sets are distributed among operations and development team members: Each team member has a specific set of tasks they need to do at each stage of the SDLC.
Focus and feedback	Typically works in sprints, each sprint lasting less than a month. The idea of a sprint is to complete the project step by step, starting each sprint right after delivery of the previous sprint’s deliverables.	Focuses on operations and business readiness. Most of the feedback comes from internal team members and metrics collected from production environments. Deadlines and goals may recur on a daily basis.

DevOps vs Platform Engineering

While DevOps and Platform Engineering share similarities in their goals of improving software delivery and operations, they differ in scope, focus, and implementation.

Scope and focus

DevOps aims to break down the barriers between development and operations teams, fostering a culture of collaboration and shared responsibility. It emphasizes Continuous Integration, Continuous Delivery (CI/CD), and continuous feedback loops to improve the software development process.

Platform Engineering focuses on building and maintaining the underlying infrastructure and platforms development teams use. Platform engineers create self-service tools and environments that streamline the deployment and management of applications. Their goal is to provide a reliable, scalable, and efficient platform that supports the needs of the development teams.

Implementation

In a DevOps culture, development and operations teams work closely together throughout the entire software lifecycle. This includes planning, coding, building, testing, releasing, deploying, operating, and monitoring. DevOps practices encourage automation, continuous monitoring, and proactive incident management to ensure high availability and performance.

Platform Engineering involves designing, building, and maintaining the infrastructure that supports application development and deployment. This includes creating automated pipelines, managing cloud resources, ensuring security and compliance, and optimizing performance. Platform engineers often use infrastructure-as-code (IaC) tools and practices to manage and provision resources consistently and efficiently.

Team structure

DevOps teams are typically cross-functional, with members possessing both development and operational skills. They work in small, autonomous units responsible for specific features or services. This approach fosters a sense of ownership and accountability, as team members are involved in the entire process, from development to production.

Platform Engineering teams, however, are usually specialized and focus on providing infrastructure and platform services to other teams within the organization. They work closely with DevOps and development teams to understand their requirements and provide the necessary tools and environments to support their work.

Key metrics

DevOps teams often measure success using metrics such as deployment frequency, lead time for changes, change failure rate, and time to recover (TTR). These metrics help teams understand their efficiency and effectiveness in delivering high-quality software.

Platform Engineering teams, on the other hand, may focus on metrics related to infrastructure performance, resource use, uptime, and scalability. They aim to ensure that the platform is reliable, secure, and able to support the demands of the development and operations teams.

Learn more in the in-depth guide to platform engineering.

DevOps vs. MLOps

While DevOps and MLOps share foundational principles—automation, continuous integration and delivery, and cross-functional collaboration—they differ significantly in their application, focus areas, and lifecycle requirements.

Application focus

DevOps focuses on standard software development and delivery pipelines. Its core objective is to simplify the development, testing, deployment, and monitoring of applications to improve speed, quality, and reliability.

MLOps, or machine learning operations, extends these practices to machine learning workflows. It supports the end-to-end lifecycle of machine learning models, including data collection, feature engineering, model training, validation, deployment, and monitoring. MLOps addresses the unique challenges of managing data, model drift, and reproducibility in ML systems.

Lifecycle and complexity

DevOps typically deals with deterministic code that behaves consistently across environments. Versioning and testing are straightforward, as application logic is mostly static once deployed.

MLOps must manage non-deterministic artifacts like datasets and models. The system needs to track changes in data sources, monitor model performance in production, and retrain models when performance degrades. This introduces more complexity, especially around automating model retraining and rollback.

Team composition

DevOps teams include software engineers and operations professionals who collaborate on the same codebase and deployment infrastructure.

MLOps teams involve a broader set of roles, including data scientists, ML engineers, data engineers, and DevOps engineers. Each contributes to different phases of the ML pipeline, requiring more coordination and tooling to bridge the gap between experimentation and production.

Tooling and infrastructure

DevOps tools focus on CI/CD pipelines, configuration management, and infrastructure provisioning (e.g., Jenkins, GitHub Actions, Terraform).

MLOps adds specialized tools for experiment tracking, model versioning, and pipeline orchestration (e.g., MLflow, Kubeflow, TFX). It often involves GPU management, scalable data processing, and model monitoring frameworks that are not part of standard DevOps stacks.

Key metrics

DevOps tracks metrics like deployment frequency, change failure rate, and MTTR. MLOps monitors metrics like model accuracy, training time, inference latency, and data drift. These metrics ensure that deployed models remain accurate and performant over time.

Learn more in the in-depth guide to MLOps

DevOps and CI/CD

DevOps closes the gap between development, operations and IT service teams. One of the primary ways it does this is by Implementing DevOps tools. These tools are implemented as pipelines, typically using continuous integration, continuous deployment, and continuous delivery (collectively known as CI/CD) workflows. CI/CD pipelines serve as structured environments that enable and promote fast delivery of high quality software.

The CI/CD implementation process uses a daily or weekly release cycle, allowing the DevOps team to get quick feedback from customers on new versions of the product.

Continuous Integration (CI)

CI is a process that ensures new code is introduced daily. This is typically done by integrating the new code into shared repositories, accessed daily by all collaborators. This is an improvement over the traditional way, in which developers would write new code and check it into the repository only at the end of the project lifecycle.

The goal is to be able to detect integration errors early and correct them quickly. In a CI workflow, a new build is triggered every time new code is merged with the main branch of the code repository. Automatic tests are run for each new version to see if the latest changes broke the build.

Learn more in the in-depth guide to Continuous Integration

Continuous Delivery (CD)

CD begins where CI ends. Continuous Delivery pipelines automate the software delivery process, pushing integrated code to production without errors or delays. While implementing DevOps, CD helps developers merge new code consistently with the master branch, allowing them to automatically build new market-ready versions of the product. CD automation runs tests to ensure that each new build can be released into production.

Learn more in the detailed guide to Continuous Delivery

Related product offering: Codefresh | GitOps software delivery platform

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Continuous Deployment

Continuous Deployment is an extreme form of continuous delivery. Continuous Deployment means that for each major code change, the pipeline generates a new build and immediately deploys it to the production environment, with no manual intervention. This is the “holy grail” of DevOps teams, because it ensures every code change is immediately running in production and the entire team can instantly see its quality and operational properties.

Learn more in the detailed guide to Continuous Deployment

Related product offering: Octopus Deploy | Continuous Delivery and Deployment platform

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Shift Left in DevOps

Shift left is a key principle in DevOps that emphasizes moving testing, security, and quality assurance earlier in the software development lifecycle. Traditionally, many of these tasks occurred late in the pipeline—just before deployment. The shift-left approach ensures these activities are integrated as early as possible, often during the design and coding phases.

In DevOps, shifting left is implemented by embedding testing, security scanning, and code quality checks directly into CI/CD pipelines. Automated unit tests, static code analysis, and security vulnerability scanning are triggered every time code is committed. This reduces the risk of defects propagating downstream and helps teams address issues while they are easier and cheaper to fix.

Benefits of shift left include:

Earlier detection of bugs and vulnerabilities: Catching issues before integration or deployment.
Faster feedback loops: Developers get real-time insights into the quality of their changes.
Reduced rework: Fixing problems early prevents time-consuming debugging later in the lifecycle.
Improved collaboration: Developers, testers, and security professionals work closely from the start.

By shifting left, DevOps teams ensure that quality and security are integral parts of the development process, not post-hoc concerns.

Learn more in the in-depth guide to shift left

What are DevOps pipelines?

A DevOps pipeline is a series of automated steps that support building, testing, and deploying new software versions. A DevOps pipeline aims to enable Continuous Integration and Continuous Delivery (CI/CD), ensuring that software updates can be deployed quickly, reliably, and with minimal human intervention.

Key stages of a DevOps pipeline:

Source code management: Developers commit code changes to a version control system (such as Git). This triggers automated workflows that start the pipeline process.
Build: The code is compiled and transformed into an executable format. Dependencies are resolved, and artifacts are created for further testing and deployment.
Testing: Automated tests (unit, integration, functional, and security tests) run to detect issues early. This ensures that new code does not introduce regressions or vulnerabilities.
Release and deployment: If the code passes all tests, it moves to the staging or production environment. This can be done via Continuous Delivery (manual approval before deployment) or Continuous Deployment (fully automated release).
Monitoring and feedback: Once deployed, the application is monitored for performance, availability, and security. Logs and metrics are collected to identify issues and improve future iterations.

Learn more in the in-depth guide to software deployment

Related product offering: Octopus Deploy | Continuous Delivery and Deployment Platform

DevOps and DevEx

Developer experience (DevEx) refers to the overall experience developers have while building, testing, and deploying software. It includes the tools, workflows, documentation, and internal platforms they interact with daily. A strong DevEx reduces friction, boosts productivity, and helps teams deliver better software faster.

DevOps and DevEx are closely linked. DevOps practices aim to simplify software delivery through automation, collaboration, and shared responsibility. These same principles improve DevEx by eliminating repetitive tasks, reducing cognitive load, and giving developers more control over infrastructure and deployment processes.

Key aspects of DevOps that improve DevEx include:

Self-service environments: Developers can provision infrastructure and deploy services without waiting on operations, thanks to internal platforms and infrastructure-as-code tools.
Integrated feedback loops: With continuous monitoring and automated alerts, developers get immediate insights into how their code performs in production.
Automated pipelines: CI/CD workflows handle builds, tests, and deployments, allowing developers to focus on writing code instead of managing releases.
Unified tooling: Standardized tools and practices reduce context switching and speed up onboarding.

Improving DevEx is a natural outcome of adopting DevOps. When friction is removed from the development process, developers are more empowered, incidents are resolved faster, and innovation happens more frequently. Organizations that prioritize both DevOps and DevEx can achieve higher team satisfaction, faster delivery cycles, and better software quality.

Learn more in the in-depth guide to developer experience

The transition to DevSecOps

The term DevSecOps stands for development, security, and operations. DevSecOps explicitly automates security processes across all phases of the software development lifecycle, including the initial design, testing, deployment in production, and software delivery. DevSecOps improves traditional software development practices by integrating security seamlessly into the pipeline.

Past development paradigms tacked security on at the end of the cycle, assigning this task to a separate, often siloed, security team. When done, the security team passed the product to an independent quality assurance (QA) team for testing. It was possible when teams released software updates only once or twice annually. However, as teams adopted agile and DevOps practices to reduce development cycles to weeks or days, this traditional approach to security resulted in unacceptable bottlenecks.

Here are the core benefits of DevSecOps:

Early remediation: DevSecOps aims to integrate infrastructure and application security seamlessly into agile and DevOps processes. It enables teams to address security issues as they emerge while it is easier, faster, and cost-effective to fix them - and teams can handle many security issues before releasing the product into production.
A security culture: DevSecOps ensures all teams, including development, security, and IT operations, share the responsibility over application and infrastructure security, eliminating the security silo.
Automation: DevSecOps pipelines enable faster release of secure software by using automation. It allows teams to automate secure software delivery without slowing the development cycle.

Learn more in the in-depth guide to DevSecOps

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SRE and DevOps

Software reliability engineering (SRE), also called site reliability engineering, is a practice that is being adopted at many organizations alongside DevOps, and can complement and benefit DevOps teams.

The SRE approach is to apply software engineering ideas to operations topics. Instead of treating operations as an ongoing maintenance effort, software reliability engineers build systems that make software services more reliable and easier to operate.

SRE vs DevOps

Most practitioners agree that SRE is not a competitor to DevOps but a complementary approach. In Google’s book defining SRE, DevOps is compared to an interface, while SRE is a class that implements it. The DevOps philosophy defines the overall behavior of the service management framework, while SRE is a concrete way to implement DevOps and measure its success in improving product reliability.

There are four key ways SRE complements DevOps:

Accept failure as normal

Like DevOps, SRE encourages shared responsibility between IT and development. SRE makes it possible to innovate and make drastic product changes, which may result in faults. It introduces the concept of a “risk budget”, which SREs to sets measurable risk limits while encouraging innovation. SRE also assumes that aiming for 100% availability is incompatible with growth and ongoing development.

Implement gradual changes

SRE, like DevOps, uses gradual change to promote continuous improvement. SRE encourages small changes and frequent deployments, meaning that negative impact will be smaller and easier to test and remediate. SREs implement automated testing of production changes, which can operate alongside CI/CD pipelines used by DevOps teams.

Use tools and automation consistently

DevOps pipelines rely heavily on automation and the adoption of new tools. SRE workflows, on the other hand, prioritize standardizing technology and information across the organization. To achieve this, SRE implementations require collaborators to use the same stacks. This can be beneficial for DevOps, because even in a complete DevOps environment, the use of different technologies and tools may cause teams to split into silos unintentionally.

Measuring everything

Measurements are crucial in both DevOps and SRE. DevOps focuses primarily on process performance and achieves continuous improvement via the CI/CD feedback loop. SRE treats operations issues as software engineering issues, so it measures service level objectives (SLO) as its key indicator. By combining process goals with production SLOs, teams can achieve faster delivery and software that becomes more robust and reliable with each release.

Learn more in the in-depth guide to software documentation

DevOps and performance optimization

Performance monitoring and testing are critical components of DevOps, ensuring that applications meet performance expectations and can handle real-world conditions. These practices help identify and resolve performance bottlenecks early in development, improving user experience and system reliability.

Capacity planning and scalability testing

Capacity planning and scalability testing are crucial for understanding how an application performs under different load conditions and ensuring it can scale to meet future demands.

Key Aspects of Capacity Planning and Scalability Testing:

Load testing: Simulate increasing load on the application to identify its maximum capacity and detect performance bottlenecks.
Stress testing: Push the application beyond its limits to see how it behaves under extreme conditions and identify failure points.
Auto-scaling: Test the effectiveness of auto-scaling mechanisms to ensure the application can scale up or down based on demand.
Resource allocation: Analyze the application’s resource usage patterns to optimize resource allocation and improve cost efficiency.

Application performance monitoring

Application performance monitoring (APM) involves tracking the performance and availability of software applications. APM tools provide insights into application behavior, helping teams detect and diagnose performance issues before they impact users.

Key Components of APM:

Real user monitoring (RUM): Measures actual user interactions with the application, providing insights into user experience.
Synthetic monitoring: Simulates user transactions to test application performance and availability.
Application metrics: Tracks metrics like response time, error rates, and throughput to assess application health.
Distributed tracing: Provides visibility into the flow of requests across different services, helping identify performance bottlenecks.

Learn more in the in-depth guide to application performance monitoring.

Kubernetes autoscaling

Kubernetes autoscaling is a key feature that ensures applications can dynamically adjust to changes in demand by automatically modifying the number of running pods or the resources allocated to them. This capability helps maintain performance while optimizing resource usage and cost.

Key components of Kubernetes autoscaling:

Horizontal Pod Autoscaler (HPA): Automatically increases or decreases the number of pod replicas based on observed CPU/memory usage or custom metrics. HPA is suitable for applications with variable workloads.
Vertical Pod Autoscaler (VPA): Adjusts the CPU and memory requests/limits of individual pods to match their actual usage. VPA helps prevent over- or under-provisioning of resources but is typically used for workloads with more predictable behavior.
Cluster autoscaler: Scales the number of nodes in a cluster based on pending pods and resource requirements. When pods cannot be scheduled due to insufficient resources, Cluster Autoscaler adds nodes; it removes nodes when they are underutilized.
Custom metrics and scaling policies: Kubernetes supports autoscaling based on external or custom metrics using the Metrics Server or Prometheus Adapter. Teams can define thresholds and scaling behaviors tailored to their applications.

Learn more in the in-depth guides to:

Kubernetes monitoring and troubleshooting

Kubernetes monitoring and troubleshooting are essential for maintaining the health and performance of containerized applications. Given Kubernetes’s complexity and dynamic nature, continuous monitoring and effective troubleshooting practices help ensure that the clusters and applications running on them remain reliable, efficient, and scalable.

Key aspects of Kubernetes monitoring and troubleshooting:

Cluster health monitoring: Track the overall health of the Kubernetes cluster by monitoring key metrics such as CPU, memory usage, and disk I/O across nodes. This helps identify resource constraints and potential failures before they impact the applications.
Pod and container monitoring: Monitor individual pods and containers’ status, resource usage, and lifecycle events. This provides insights into container crashes, resource contention, and improper scheduling.
Log aggregation and analysis: Centralize and analyze logs from Kubernetes components, applications, and containers. Aggregated logging facilitates quick identification of issues such as failed deployments, network errors, and application crashes.
Network performance monitoring: Observe network traffic between services and pods to detect latency, packet loss, or misconfigurations. Tools like service mesh observability can help identify bottlenecks and ensure communication reliability across microservices.
Alerting and incident response: Set up alerts for anomalies in cluster and application performance, such as high pod eviction rates or API server errors. Automated alerts enable rapid incident response and minimize downtime.
Troubleshooting with metrics and traces: Use detailed metrics and distributed traces to diagnose issues within the Kubernetes environment. For example, tracing requests across services can help pinpoint latency issues or failures in the application’s request flow.
Resource optimization: Continuously analyze resource usage to optimize Kubernetes deployments. This includes adjusting resource requests and limits, tuning autoscaling policies, and ensuring that workloads are efficiently distributed across nodes.

Learn more in the in-depth guides to Kubernetes monitoring

Mainframe Modernization

Mainframe modernization involves updating legacy mainframe systems to integrate with modern infrastructure, improve agility, and reduce operational costs. As many organizations continue to rely on mainframes for mission-critical workloads, modernization strategies are essential to maintain performance, improve scalability, and support cloud-native development practices.

Key approaches to mainframe modernization:

Replatforming: Move mainframe applications to modern platforms like cloud or distributed systems without rewriting the core logic. This often involves containerizing workloads or using emulation environments that replicate mainframe behavior.
Refactoring: Rewrite or restructure legacy applications to improve maintainability and integration with modern services. This can include breaking monolithic COBOL applications into microservices or converting mainframe-specific code into more modern languages.
Integration with DevOps: Extend DevOps practices to mainframe environments by incorporating automated testing, CI/CD pipelines, and infrastructure as code (IaC). Tools that support mainframe pipelines enable consistent deployment processes across hybrid environments.
Data Modernization: Migrate data from mainframe-based databases to modern data platforms to support analytics, machine learning, and real-time processing. This includes transforming data formats and ensuring compatibility with modern data lakes and warehouses.
API Enablement: Expose mainframe functionality through APIs to enable integration with modern applications, mobile interfaces, and cloud services. API gateways can bridge the gap between legacy systems and digital platforms without full application rewrites.
Monitoring and Performance Optimization: Apply APM and infrastructure monitoring tools to mainframe environments to track system health, detect performance bottlenecks, and forecast resource needs. Monitoring tools that integrate with hybrid systems ensure visibility across the entire application stack.

Mainframe modernization enables enterprises to leverage their existing investments while aligning with modern IT strategies. By adopting a phased approach, organizations can reduce risk, maintain continuity, and gradually evolve their technology landscape.

Learn more in the in-depth guide to mainframe modernization

API testing

API testing ensures that application programming interfaces (APIs) function correctly, reliably, and securely. It involves sending requests to API endpoints and validating responses, ensuring the API performs as expected under various conditions.

Key aspects of API testing include:

Functional Testing: Validates the correctness of API operations, such as data retrieval or submission.
Load Testing: Assesses how the API handles high traffic volumes, identifying potential performance issues.
Security Testing: Ensures APIs are secure from vulnerabilities like SQL injection or unauthorized access.
Integration Testing: Verifies that APIs integrate seamlessly with other system components.

Learn more in the in-depth guide to API testing.

Related product offering: Pynt | Offensive API security testing platform

Synthetic transaction monitoring

Synthetic transaction monitoring uses automated scripts to simulate user interactions with an application. This technique helps DevOps teams measure application performance and availability from the end-user’s perspective.

Key aspects of synthetic transaction monitoring:

Scripted User Journeys: Define common user paths and automate these transactions to monitor application performance continuously.
Geographic Testing: Simulate transactions from various geographical locations to identify region-specific performance issues.
Benchmarking: Compare performance metrics over time to detect regressions and improvements.
Alerting: Set up alerts for deviations from expected performance metrics, enabling rapid response to potential issues.

DevOps and digital transformation

Digital transformation is a global transition of businesses to processes and strategies using digital technology. It’s widely recognized that companies will find it challenging to compete in the modern economy without digital transformation.

A digital transformation requires constant efforts from development and operations teams to create and integrate new technologies supporting business processes, employees, and customers. One of the key elements of a successful digital transformation is a DevOps mindset.

DevOps ensures that organizations build technologies that are useful, easy to maintain, and capable of evolving to support changing requirements. While traditional IT is associated with legacy technology that supports old-school business, DevOps will gradually become synonymous with digital transformation.

DevOps and cloud cost management

Cost management plays a pivotal role as organizations transition infrastructure to the cloud. DevOps can significantly impact cloud costs by introducing efficiency, automation, and better resource management.

DevOps’ inherent emphasis on automation and efficient deployment of resources plays a crucial role in keeping cloud costs under control. Automated provisioning and de-provisioning of resources in response to demand spikes, load balancing, and continuous monitoring for idle or underused resources are ways DevOps can contribute to the efficient use of cloud resources, ultimately leading to cost savings.

For instance, Infrastructure as Code (IaC) can help ensure efficient resource use by standardizing environments, eliminating “environmental drift”, and reducing the need for over-provisioning. Similarly, Continuous Integration and Continuous Delivery (CI/CD) pipelines help minimize wastage and streamline processes by enabling teams to identify and fix issues early in the development lifecycle, which can prevent costly failures in production.

Learn more in the in-depth guides to:

Related product offering: N2WS | Cloud backup and restore

AWS cost management

AWS offers a variety of tools and practices to help organizations manage and optimize their cloud costs effectively. Key AWS cost management tools include:

AWS Cost Explorer: This tool provides detailed insights into your AWS spending patterns. You can visualize your costs and usage data with custom reports, helping to identify trends and detect anomalies. Cost Explorer allows you to forecast future costs based on historical data, making budgeting more accurate.
AWS Budgets: With AWS Budgets, you can set custom cost and usage budgets that alert you when your spending exceeds predefined thresholds. This proactive approach helps prevent unexpected expenses and enables better financial planning.
AWS Trusted Advisor: This service recommends cost optimization, performance improvements, security enhancements, and fault tolerance. Trusted Advisor highlights money-saving opportunities, such as underutilized resources and idle instances.
Reserved Instances and Savings Plans: AWS offers pricing models like Reserved Instances and Savings Plans that provide significant discounts compared to on-demand pricing. By committing to use AWS services for a one- or three-year term, organizations can achieve substantial cost savings.

Learn more in the in-depth guides to: AWS cost management

Related product offering: Finout | Enterprise-Grade FinOps Platform

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Azure cost management

Azure provides comprehensive tools and practices to help manage and optimize cloud spending:

Azure cost management and billing: This service offers detailed cost analysis, budgeting, and forecasting capabilities. You can track spending patterns, create budgets, and receive alerts when costs exceed predefined limits. The service also provides recommendations for optimizing resource usage and reducing costs.
Azure Advisor: Similar to AWS Trusted Advisor, Azure Advisor provides personalized recommendations for cost optimization, high availability, performance, and security. It identifies underutilized resources and suggests ways to reduce expenses.
Azure reserved instances: By purchasing reserved instances, organizations can save up to 72% compared to pay-as-you-go pricing. Azure also offers a hybrid benefit, allowing you to use existing on-premises licenses for further cost savings.
Azure cost alerts: These alerts notify you when your spending approaches or exceeds your budget, helping you take corrective actions promptly. You can set up alerts based on various metrics, such as total cost, resource usage, and specific service expenses.

_**Learn more in the in-depth guides to Azure cost management.

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Learn more in the detailed guide to Azure pricing

Related product offering: Umbrellacost | Intelligent Cost Optimization Platform

Related technology update: [Blog] State of Cloud Cost Report 2024

Google Cloud cost management

Google Cloud provides robust tools and practices to help organizations control and optimize their cloud expenditures:

Google Cloud billing reports and cost management: This tool offers detailed insights into your cloud spending, enabling you to analyze and visualize costs. You can create budgets, set up alerts, and receive notifications when spending exceeds predefined thresholds.
Google Cloud recommender: This service provides actionable recommendations to optimize costs and improve resource use. It identifies idle resources, suggests rightsizing opportunities, and offers guidance on purchasing committed use contracts for cost savings.
Committed use contracts: Google Cloud offers committed use contracts that provide significant discounts in exchange for committing to use a specific amount of resources for a one- or three-year term. This model helps organizations achieve predictable cost savings.
Resource labeling and budget tracking: Google Cloud allows you to assign labels to resources, making it easier to track and allocate costs accurately. You can create detailed budgets and track spending against them, ensuring better financial control.

DevOps and multi-cloud

As organizations adopt multi-cloud strategies, DevOps ensures efficient resource management, automation, and cost optimization across multiple cloud providers. Multi-cloud environments offer flexibility, reduce vendor lock-in, and improve resilience, but they also introduce complexities in managing infrastructure, security, and costs.

DevOps helps address these challenges by implementing standardized workflows, automation, and monitoring across cloud platforms. Infrastructure as Code (IaC) tools like Terraform and Pulumi enable teams to define and deploy infrastructure consistently across AWS, Azure, and Google Cloud. This ensures better governance and cost control, preventing resource sprawl.

Continuous Integration and Continuous Delivery (CI/CD) pipelines simplify deployments across multiple clouds, reducing redundancy and improving efficiency. Multi-cloud monitoring and observability tools like Datadog and Prometheus provide visibility into cloud usage and costs, allowing teams to optimize workloads dynamically.

Learn more in the in-depth guides to multi-cloud.

DevOps vs FinOps

DevOps vs FinOps, short for Financial Operations, is a framework that brings financial accountability to the variable spend model of cloud computing. It enables organizations to manage their cloud costs efficiently by fostering a culture of collaboration between finance, operations, and engineering teams. The goal of FinOps is to ensure that organizations get the most value from their cloud investments by making informed decisions about spending and usage. Here are some of the key differences between DevOps and FinOps:

Focus areas

DevOps concentrates on software integration and delivery through automation, Continuous Integration/Continuous Deployment (CI/CD), and collaboration between development and operations teams. The primary aim is to improve the speed and quality of software delivery.

FinOps centers on the financial management of cloud resources. It aims to optimize cloud spending and ensure that every dollar spent on cloud services is used effectively.

Metrics and goals

In DevOps, success is measured by metrics such as deployment frequency, lead time for changes, mean time to recovery, and change failure rate. The focus is on improving the efficiency and reliability of the software development lifecycle.

In FinOps, success is measured by financial metrics, including cost savings, budget adherence, and cost allocation accuracy. The focus is on managing and optimizing cloud expenditures.

Teams involved

DevOps primarily involves development and operations teams working to streamline the software delivery process.

FinOps involves finance, operations, and engineering teams. Collaboration is crucial to ensure that cloud resources are used efficiently and cost-effectively.

Processes

DevOps emphasizes automation and continuous improvement in software development and deployment processes. It uses tools like CI/CD pipelines, configuration management, and infrastructure as code.

FinOps emphasizes continuous monitoring and optimization of cloud spending. It involves processes such as cost allocation, forecasting, budgeting, and real-time spending visibility._

Learn more in the in-depth guide to FinOps.

The DevOps technology stack

The DevOps development pipeline relies on an entire technology stack that enables automation, efficiency, and collaboration. Below, we describe several elements of this stack that may be used in different combinations by different teams.

Cloud automation

Cloud automation allows IT teams and developers to automatically create, modify, and delete environments in the cloud. DevOps has leveraged cloud computing since its early days to enable complete end-to-end automation of development and delivery pipelines.

However, automation is not built into the cloud. It requires specialized knowledge and uses specialized tools, some of them offered by public cloud providers, some as part of private cloud platforms, and some third-party tools, notably configuration management, infrastructure as code (IaC) tools, and orchestration tools like Kubernetes. These skills and tools are an essential part of any DevOps team.

Feature flags

Feature flags, also known as feature toggles, are a powerful tool in the DevOps toolkit. They allow developers to switch a feature on or off at runtime. This means that developers can deliver new features to production, even if they aren’t complete or thoroughly tested, without affecting the end users.

Feature flags can be used to perform A/B testing, canary releases, or phased rollouts. For example, a new feature can be released to a small percentage of users, and if everything works as expected, the feature can be gradually released to all users.

This approach reduces the risk of deploying new features and gives developers more control over the release process. Furthermore, it allows for quicker feedback, as features can be tested in the production environment early in development.

Learn more in the in-depth guide to feature flags.

Unit testing

Unit testing is a fundamental practice in DevOps that involves testing individual code modules to ensure they function correctly. By isolating modules from dependencies, unit testing enables developers to validate behavior without setting up multiple inter-dependent components.

In DevOps, unit tests are automated and run continuously throughout the development cycle, providing fast feedback to developers when a new code change breaks the software’s behavior. Common unit testing frameworks include JUnit for Java, NUnit for .NET, and Jest for JavaScript.

Automated unit testing reduces bugs, enhances code reliability, and supports faster development cycles by catching issues early. This helps ensure that code pushed to staging or production has already met basic quality standards, contributing to the overall stability of the development pipeline.

Learn more in the in-depth guide to unit testing.

Infrastructure as Code for DevOps

Infrastructure as Code (IaC) uses the same descriptive model that the DevOps team uses for code—version control—to manage infrastructure, including virtual machines, networks, and storage. Just like the same source code always produces the same binary code, an environment configuration should be able to reproduce an environment every time consistently.

IaC solves the age-old problem of environmental drift. Without IaC, IT teams had to maintain the setup of each deployment environment. Over time, each environment becomes a “snowflake”, a unique creation that cannot be replicated automatically. Inconsistencies between dev, test, and production environments can cause problems during deployment, introduce errors, and involve complex manual processes.

Infrastructure as code allows DevOps teams to test applications in a production-like environment early in the development cycle. They use it to set up multiple test environments as needed reliably. Environment configuration code can be checked into version control, tested, and modified as needed until a stable environment configuration is found.

DevOps teams implementing IaC can provision stable environments quickly and at scale. They ensure consistency by expressing the required environment state in code without manually configuring it. Infrastructure becomes repeatable and reliable.

Infrastructure as Code (IaC) on Amazon Web Services (AWS)

The primary IaC service on AWS is CloudFormation. It uses templates, which are simple configuration files using YAML or JSON syntax. Templates are easy to read and edit, and can be used to define resources for deployment. CloudFormation reads templates and then creates a set of ready-to-use resources for AWS.

CloudFormation allows DevOps teams to use templates to automatically build anything, including basic resources and complex applications using many resources. You can fine-tune configuration and repeat the process to create reliable environments, which can then be replicated for the DevOps pipeline’s development, testing, and production stages.

Infrastructure as Code on Azure

Microsoft Azure allows DevOps teams to define and deploy infrastructure as code using Azure Resource Manager (ARM) templates. ARM templates provide declarative definitions of any cloud resources within an environment. Azure automatically sets up resources reliably and consistently from the template.

Azure also provides “blueprints” that package ARM templates with policy and RBAC definitions—giving DevOps teams everything they need to set up cloud resources end-to-end for dev, test, and production environments.

GitOps

GitOps enables you to implement Continuous Deployment (CD) for cloud-native applications. It provides a developer-centric experience for operating infrastructure, letting developers use tools they are already familiar with, such as Git, to operate and automate the infrastructure.

You can implement GitOps by setting up a version control repository (often Git) containing IaC templates representing the desired production environment. GitOps creates an automated process that matches the production environment with the desired state described in the repository.

Once you have configured GitOps for your environment, you can deploy new applications or update existing ones by updating the repository. The automated process you already have in place handles the rest.

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Cloud-native and DevOps

“Cloud-native” is a way to build and run applications that take advantage of the cloud computing delivery model. Cloud-native is not about where you deploy your application but about how applications are built and deployed. Cloud-native applications can live both in the public cloud and on-premises, assuming that the local data center has cloud automation capabilities.

DevOps is one of the primary use cases for cloud-native techniques. The DevOps approach naturally complements cloud-native concepts like containerization, serverless, and microservices architectures:

Containerization and serverless frameworks make applications environment independent, eliminating conflicts between developers and operations teams and improving collaboration between developers and testers.
Microservices architectures split large applications into smaller, functional elements, each of which can be iteratively developed and maintained by a DevOps team. This improves agility and reliability, making collaboration easier, because each element in a microservices architecture is simple and well understood.

DevOps and Kubernetes

Most, if not all, DevOps teams are developing and running applications in containers. Orchestration engines like Kubernetes are required to keep containers running at scale.

Kubernetes helps DevOps teams meet customer needs without worrying about the infrastructure details. Kubernetes replaces the old manual task of deploying, scaling, and building a resilient application. Instead, it dynamically provisions applications on available resources.

Kubernetes is essential for DevOps teams looking to automatically scale and ensure the resiliency of applications while minimizing the operations burden. For example, it allows teams to manage the scalability and elasticity of an application based on load metrics. Developers can focus on building new functionality without worrying whether the application can serve users during peak times.

However, Kubernetes makes certain aspects of the IT environment more complex. These include security, storage management, and management of CI/CD pipelines.

Learn more in these detailed guides to Kubernetes cost optimization

Related technology updates: [Blog] Kubernetes health checks [Blog] Kubernetes deployment anti-patterns

Why is Kubernetes essential for modern DevOps teams?

Portability—Kubernetes makes it possible to deploy applications anywhere without tight coupling to infrastructure. Making it easier to replicate dev, test, and production environments.
Infrastructure as code—everything in Kubernetes works in an IaC paradigm, so infrastructure and applications are fully declarative and can be provisioned automatically.
Hybrid—Kubernetes can run locally, in any public cloud, or at the edge, providing additional flexibility for DevOps teams.
Open—Kubernetes is an open-source platform supported by a large ecosystem of innovative services and tools.
Deployments with no downtime—Kubernetes provides multiple deployment strategies, enabling DevOps teams to test and conduct experiments in production, for example, using blue/green or canary deployments.
Immutability—containers can be stopped, deleted, or re-deployed with minimal impact on the application. Servers become “cattle, not pets”.

Learn more in this kubectl cheat sheet

Related product offering: Komodor | Kubernetes Management and Troubleshooting

4 Kubernetes deployment strategies for DevOps teams

Here are several ways DevOps teams can leverage Kubernetes to deploy dev, test, and production environments.

Recreate deployment All replicas of an existing deployment are deleted and replaced with new ones. There is some downtime between the shutdown and restart of each container. This method is suitable for infrequently used applications and for applications that users do not need 24/7 availability.
Rolling update By default, Kubernetes is updated in stages. After running the deploy command, Kubernetes starts replacing existing containers with new updates, deploying each new container at a time. This can be used both for software updates and for rolling back an update and reverting to a previous version.
Blue/green deployment Blue/green deployments are not built into Kubernetes, but are easy to set up. The “blue” copy is the existing version and is replaced by the “green” copy with a new software version. To achieve blue/green deployment, first, create a deployment and roll out green replicas alongside the existing blue ones. While the green replicas are being deployed, the system will use additional resources, but this is only temporary. After deploying and testing green replicas, you need to route traffic from the “blue” to the “green” replicas. You can do that by using an external load balancer. Linkerd and similar tools can help you define how much traffic you want to route to the blue vs. the green deployment.
Canary Releases This technique enables you to deploy a basic new application version for a fraction of its users. For example, 10% of users get the new version while the rest continue seeing the existing one. Once the version is tested on the initial 10%, it is released to a larger subset and eventually pushed to the entire user base. The main advantage of a canary release is that you can test your applications on real users in a production environment. However, to ensure the release maintains a positive user experience, you need to plan in advance carefully.

DevOps and serverless

Serverless computing is an innovative way to deliver backend services. Serverless architecture allows users to write and deploy code without worrying about the infrastructure. There are still physical servers, but developers do not know about or interact with them.

Serverless architecture is commonly used to implement DevOps. Serverless makes building an efficient CI/CD pipeline possible simply by declaring application requirements with an on-demand pricing model. DevOps teams can implement an entire build, test, and deployment pipeline by writing code and deploying it as serverless functions with no hosted solutions.

Another advantage of serverless technology is that it makes updates easier. DevOps teams can introduce new versions of serverless services while keeping existing instances running and switching between services very easily. This makes using canary and blue/green deployments even easier than a containerized approach.

Learn more in the in-depth guides:

The DevOps toolset

DevOps teams see tooling as a means, not an end. DevOps unifies teams by automating and tracking functional processes from the initial check-in of code to a repository, all the way to production deployment. To fully improve these processes, all stages of the DevOps pipeline must be automated, controlled, and monitored.

DevOps tools enable automation and control for planning, development, testing, deployment, operations, and monitoring. In addition, some tools have a view of the entire DevOps pipeline and can help orchestrate the whole process.

DevOps tools map

A wide range of tools are available for DevOps implementations and new tools are consistently being developed. Below are the most common types of tools that are included by DevOps teams and some examples of the specific tools in use.

Function	Examples of tools
Automated deployment: Automate deployment to staging or production environments.	Spinnaker, ArgoCD, Octopus Deploy
Public cloud platforms: Provide scalable computing resources with rich automation capabilities.	Amazon Web Services, Microsoft Azure, Google Cloud Platform, DigitalOcean, IBM Cloud
Containerization: Enables you to deploy services in a consistent way on any platform, with orchestration tools to manage, scale, and deploy a large number of containerized resources.	Docker, Kubernetes
Collaboration: Enable teams to communicate on tasks transparently with full accountability.	Slack, Jira
Infrastructure as code: Automates system configurations and standardizes resource provisioning.	Puppet, Chef, Ansible, Terraform, CloudFormation, Azure ARM
Logging: Enables you to collect and analyze event and operational data for troubleshooting and optimization.	Fluentd, Logstash, Filebit, Elasticsearch, Splunk
Observability, monitoring, and alerting: Enables the collection, analysis, and visualization of system metrics, logs, and traces to optimize performance, detect anomalies, and respond to production issues as they arise. Learn more in the detailed guide to observability	Logstash, Elasticsearch, Splunk, Prometheus, Sensu, Datadog, NewRelic, Dynatrace, AppDynamics, PagerDuty
Security: Scans and secures applications and resources to remediate vulnerabilities and prevent cyber attacks.	Snyk, WhiteSource, Snort, Veracode, Sonatype
CI/CD systems: Enables automatically building and testing code in a CI/CD pipeline. Related product offering: Codefresh: GitOps software delivery platform. Related technology updates in The pains of GitOps 1.0 and Jenkins Pipeline Generator.	Codefresh, Jenkins, Gitlab, CircleCI, TravisCI, Weaveworks, Azure DevOps
Application mapping: Application mapping is a process that involves identifying and documenting the interactions between various software applications within an organization. Learn more in the detailed guide to application mapping. Related product offering: Faddom: Instant application dependency mapping tool	AppDynamics, Dynatrace, New Relic, Faddom
Documentation tools: These tools can create, manage, and update technical documentation, including system overviews, developer guides, API documentation, and more. Learn more in the detailed guides to documentation tools, and code documentation. Related product offering: Swimm: Document your codebase. Related technology updates: Automating legacy code documentation and Code documentation handbook	Confluence, ReadTheDocs, Swimm, Sphinx
Configuration management Configuration management is the process of systematically handling changes to IT systems to ensure consistency, efficiency, and reliability. It involves defining, maintaining, and auditing system configurations to prevent configuration drift and enable automated provisioning, deployment, and rollback. Learn more in the detailed guides to configuration management software. Related product offering: Configu: Configuration Management Platform	SaltStack, CFEngine, Otter, Configu
Caching and in-memory data stores Enable low-latency data access by storing frequently-used data in memory. Suitable for reducing load on databases, speeding up API responses, and handling high-throughput systems. Learn more in the detailed guide to Redis alternatives	Memcached, Redis, Upstash, Apache Ignite, Hazelcast
AI code generation AI code generation tools leverage machine learning to automate code writing, review, and even refactoring. These tools help speed up development, reduce errors, and improve code quality by providing suggestions or generating code snippets based on input prompts or patterns in existing code. Learn more in the detailed guides to AI coding tools, and AI code generation.	Tabnine, GitHub Copilot, Codeium, CodeT5

DevOps with Octopus

Octopus Deploy is a sophisticated, best-of-breed Continuous Delivery (CD) platform engineered for high-performing DevOps teams. Octopus accelerates your CI/CD pipeline with powerful release orchestration, infrastructure as code deployment automation, and operational runbook automation. The platform seamlessly integrates into your DevOps toolchain while handling the scale, complexity, and governance expectations of even the largest organizations with the most complex multi-environment deployment challenges.

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Steve Fenton
Sunday, June 15, 2025

Steve Fenton is a Principal DevEx Researcher at Octopus Deploy and a 8-time Microsoft MVP with more than two decades of experience in software delivery.

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