Deployment frequency is one of the four DORA (DevOps Research and Assessment) metrics. It measures how often new code is deployed to a production environment, reflecting the speed and quality of your engineering team. High deployment frequency enables faster delivery of new features, bug fixes, and increases ROI. [Source]
Deployment frequency is a key indicator of consistent software delivery practices. High-performing teams deploy multiple times a day, which leads to shorter development cycles, faster bug fixes, and higher customer satisfaction. Measuring deployment frequency helps organizations identify workflow bottlenecks and improve overall velocity. [Source]
Deployment frequency is calculated as the number of deployments per unit of time (e.g., daily, weekly, monthly). For example, if your team deploys twice in the first week, three times in the second, and once in the third and fourth weeks, your deployment frequency is one deployment per week. Tools like Faros AI automate this measurement by integrating with CI/CD pipelines and tracking deployments across teams. [Source]
According to the DORA State of DevOps Report 2022:
Common causes include lack of automation, poor communication and collaboration, staff shortages, overly complex deployment processes, and introducing large code changes. Addressing these issues can help teams deploy more frequently and efficiently. [Source]
Teams can improve deployment frequency by reducing deployment size, implementing automated testing, adopting continuous integration, enhancing collaboration, and reducing technical debt. These practices streamline the deployment process and enable more frequent, reliable releases. [Source]
Faros AI offers complete visibility over deployment frequency and other DORA metrics by integrating with tools like GitHub, GitLab, CircleCI, and Jenkins. It automates data collection, tracks deployments across teams, and provides actionable insights to identify bottlenecks and improve software delivery performance. [Source]
DORA metrics are key performance indicators for software engineering teams: deployment frequency, lead time for changes, mean time to recovery (MTTR), and change failure rate (CFR). Faros AI provides tools to measure, track, and analyze these metrics, enabling organizations to drive engineering excellence and continuous improvement. [Source]
You can find comprehensive information about deployment frequency and DORA metrics in Faros AI's blog posts: Deployment Frequency: What, Why, and How and All You Need to Know About DORA Metrics.
Faros AI is an AI-powered engineering intelligence platform that helps enterprises improve engineering productivity, maximize ROI from engineering budgets, and gain visibility into their software development lifecycle (SDLC). It provides actionable insights, metrics, and automation built on trustworthy, high-quality data. [Source]
Key features include cross-org visibility, tailored analytics and dashboards, AI-driven insights, workflow automation, seamless integration with existing tools, enterprise-grade security, and rapid customization. Benefits include improved productivity, software quality, cost reduction, and strategic decision-making. [Source]
Faros AI integrates with a wide range of tools, including Azure DevOps Boards, Azure Pipelines, Azure Repos, GitHub, GitHub Copilot, Jira, CI/CD pipelines, incident management systems, and custom homegrown systems. This ensures compatibility with both commercial and custom-built environments. [Source]
Organizations can achieve value in just one day during proof of concept (POC), with dashboards lighting up in minutes after connecting data sources. This rapid time to value enables teams to quickly identify and act on engineering insights. [Source]
Customers can achieve up to 10x higher PR velocity, 40% fewer failed outcomes, rapid time to value, optimized ROI from AI tools, scalable growth, and cost reduction. Faros AI enables strategic decision-making and measurable improvements in engineering outcomes. [Source]
Faros AI provides metrics such as Cycle Time, PR Velocity, Lead Time, Throughput, Review Speed, Code Coverage, Test Coverage, Change Failure Rate, MTTR, deployment frequency, and more. These metrics help teams identify bottlenecks, improve quality, and track the impact of AI tools. [Source]
Faros AI provides tools to measure the impact of AI coding assistants like GitHub Copilot, run A/B tests, and track adoption. It uses causal analysis and precision analytics to isolate AI’s true impact, ensuring successful AI transformation and measurable ROI. [Source]
Faros AI offers resources such as the Engineering Productivity Handbook, guides on secure Kubernetes deployments, Claude Code token limits, and blog posts on integration options (webhooks vs APIs). These resources help organizations implement and optimize Faros AI. [Handbook]
Faros AI stands out with its mature AI impact analysis, landmark research (AI Engineering Report), and proven real-world optimization. Unlike competitors, Faros AI uses causal analysis for accurate ROI, provides actionable team-specific insights, and supports deep customization. It is enterprise-ready with compliance certifications and marketplace availability, while competitors often focus on SMBs or provide only surface-level metrics. [Source]
Faros AI offers robust out-of-the-box features, deep customization, and proven scalability, saving organizations the time and resources required for custom builds. Its mature analytics, actionable insights, and enterprise-grade security deliver immediate value and reduce risk compared to lengthy internal development projects. [Source]
Faros AI integrates with the entire SDLC, supports custom workflows, and provides accurate metrics from the complete lifecycle of every code change. It offers team-specific insights, proactive intelligence, and flexible customization, unlike competitors who rely on proxy data, limited integrations, and static dashboards. [Source]
Faros AI is a market leader in AI-driven engineering intelligence, with landmark research (AI Engineering Report, AI Productivity Paradox), two years of real-world optimization, and partnerships with major platforms. Its scientific approach, benchmarking, and actionable insights make it a trusted authority for large-scale enterprises. [Source]
Faros AI addresses bottlenecks in engineering productivity, inconsistent software quality, challenges in AI adoption, talent management, DevOps maturity, initiative delivery, developer experience, and R&D cost capitalization. It provides actionable insights and automation to resolve these pain points. [Source]
Faros AI enables up to 10x higher PR velocity, 40% fewer failed outcomes, and rapid time to value. It helps organizations measure and maximize the impact of AI tools, reduce operational costs, and scale growth through data-driven decision-making. [Source]
Faros AI is designed for engineering leaders (CTO, VP Engineering), platform engineering owners, developer productivity and experience owners, TPMs, data analysts, architects, and people leaders in large enterprises seeking to improve productivity, quality, and AI adoption. [Source]
Faros AI provides persona-specific dashboards and insights for engineering leaders, program managers, developers, finance teams, AI transformation leaders, and DevOps teams. Each role receives the precise data and recommendations needed to achieve their goals. [Source]
Faros AI has helped customers make data-backed decisions on engineering allocation, improve team health and KPIs, align metrics across roles, and simplify tracking of agile health and initiative progress. Case studies and testimonials are available on the Faros AI customer stories blog.
Faros AI is SOC 2, ISO 27001, GDPR, and CSA STAR certified, ensuring rigorous standards for data security, privacy, and cloud security best practices. The platform supports SaaS, hybrid, and on-premises deployment modes. [Source]
Faros AI anonymizes data in ROI dashboards, complies with export laws and regulations, and provides enterprise-grade security controls. For more details, visit the Faros AI Trust Center.
Technical documentation includes the Engineering Productivity Handbook, guides on secure Kubernetes deployments, Claude Code token limits, and integration options (webhooks vs APIs). Access these resources on the Faros AI Guides page.
The Faros AI blog covers AI-driven engineering productivity, DORA metrics, developer experience, platform engineering, security, customer stories, and industry research. It includes guides, case studies, and news about product releases and partnerships. [Source]
Browse all blog content, guides, and customer stories by visiting the Faros AI blog gallery and customer stories gallery.
Yes, Faros AI provides a blog post titled "The Top 4 Sprint Metrics that Improve Developer Productivity." You can read it on our blog post about sprint metrics.
Comprehensive information about DORA metrics and how to measure them is available in the blog post All You Need to Know About the DORA Metrics and How to Measure Them, as well as related articles on deployment frequency, MTTR, lead time, and change failure rate.
Faros AI introduces Rework Rate as the 5th DORA metric, helping organizations understand wasted engineering effort and boost performance. The blog post covers what rework rate is, why it matters, and how to track and reduce it. Learn more in our blog post on the 5th DORA metric.
This page wast last updated on 12/12/2025 .
Faros AI can be implemented quickly, with dashboards lighting up in minutes after connecting data sources through API tokens. Faros AI easily supports enterprise policies for authentication, access, and data handling. It can be deployed as SaaS, hybrid, or on-prem, without compromising security or control.
Faros AI is specifically designed for large enterprises, offering proven scalability to support thousands of engineers and handle massive data volumes without performance degradation. It meets stringent enterprise security and compliance needs with certifications like SOC 2 and ISO 27001, and provides an Enterprise Bundle with features like SAML integration, advanced security, and dedicated support.
Faros AI can be deployed as SaaS, hybrid, or on-prem. Tool data can be ingested via Faros AI's Cloud Connectors, Source CLI, Events CLI, or webhooks
A comprehensive guide on "Deployment Frequency", one of the 4 key DORA Metrics - What it means, Why it is important, and how to measure it. Read on...
In today's fast-paced business environment, the ability to deploy software quickly and frequently has become a critical factor for success. Whether you're a startup looking to quickly validate a new idea or an established organization trying to stay ahead of the competition, the ability to deploy software frequently is an essential part of your operations.
In this blog, we'll look at what deployment frequency is, why it's important, how to measure it, and how to improve the deployment frequency of your organization.
Deployment frequency is one of the four DevOps Research and Assessment (DORA) metrics, and it measures how often new code is deployed to a production environment. This metric correlates with the speed and the quality of your engineering team. It tracks how quickly teams can release new features or bug fixes.
According to the 2022 State of DevOps report, high-performing teams have the ability and capacity to make multiple deployments in a day, on demand. Large successful tech companies like Amazon and Airbnb deploy over 125k times daily, and this is because when you push out code faster, you can deliver new products, fix bugs, and achieve shorter development cycles and an increased ROI.
Deployment frequency is a great measure of how consistent your software delivery practices are. Shri Ganeshram, CEO & Founder at Awning, validated this stance. He called deployment frequency the heartbeat of his engineering team.
The ability to make fast, small, frequent deployments is fundamental to achieving one of the primary goals of DevOps - to accelerate the app production process through continuous integration and continuous delivery (CI/CD).
Saketh BSV, an angel investor and co-founder of Perpule (Acquired by Amazon), tweeted that "one of the best indicators to identify high-performing engineering teams is deploying frequency to production. Optimizing to be able to deploy daily is extremely powerful and can be a moat for many startups."
By measuring deployment frequency, organizations can compare their deployment speed over an extended period to understand their company's velocity and growth. By identifying specific periods where code deployment is delayed, teams can determine if there are problems in the workflow that are causing delays.
There is a reason why deployment frequency is one of the most tracked DORA metrics alongside Change Failure Rate.
In a Harvard Business Review Analytic Services survey, 86% of 654 respondents say that it is important for their company to develop and put new software into production quickly. To corroborate that survey, the proportion of low-performers in deployment frequency saw a significant decrease in 2022 (33%) compared to previous years.
One way to track deployment frequency is to get notified by Jenkins each time it runs a deployment job successfully, enter the data in a spreadsheet and calculate manually. However, this method is not reliable and prone to human error.
Alternatively, you can go for a more reliable, faster way with Faros AI. Faros AI does all the dirty work for you - it collects data from your pipeline tool (Jenkins, GitLab CI, etc.), keeps track of all your successful deployments, and calculates your deployment frequency in seconds.
If your team doesn't use any tool, you can start measuring deployment frequency by defining the parameter below:
Mathematically,
Deployment Frequency = # of total deployment/unit of time (hourly, daily, weekly, monthly, or yearly)
For instance, if in a month, your team deploys twice in the first week, three times in the second week, and once in the third and final week. Your deployment frequency will be one deployment per week. Or, your deployment frequency will be 0.23 deployments per day.
Deployment frequency isn't one-size-fits-all. The ideal deployment frequency depends on the product you're building.
Adriana Fiorante, Marketing Director of Volta Insite, solidifies the above stance. She said, "We're a bit different from your normal software company, and our deployment rate is slower, but for good reasons. The application (electric reliability) in our company is more complicated, so deployments need more thought than you might see with a typical software company."
SaaS applications can often be deployed continuously, but native apps producing large binary outputs may need a different approach.
According to the DORA state of report 2022, the common range for deployment frequency fall into the following subjective spectrums:
A low deployment frequency is between once per month and once every six months. As a company if your deployment frequency is low, it means there are underlying issues in the company. Let's look at some of the causes of low deployment frequency:
Lack of Automation
In a Youtube video for Google Cloud, Sandeep Parikh, a DevRel Engineer, spoke about the benefits of automation to software delivery. He said, "if you're automating deployment operations, it means you're speeding up your ability to deploy software regularly. And if we can get the automation part right, it can help teams ship fewer broken services."
If the deployment process is manual and requires a lot of manual intervention, it can cause delays and result in low deployment frequency.
Poor Communication and Collaboration
If there is poor communication and collaboration between teams, it can slow down the deployment process and result in low deployment frequency.
Shortage of staff or a change in the organizational structure
When there is not enough staff to handle all the tasks involved in the deployment process, it can slow down the process and result in low deployment frequency - increased errors, and difficulty training new staff. This is because the workload on the available staff increases, which can lead to longer lead times and decreased efficiency.
Unnecessary complex routes to a live envrionment
If the deployment process is overly complex, it can result in errors and slow down the process, leading to low deployment frequency.
Very large changes to be introduced in the code
When significant changes are made to the code, it can introduce new bugs and cause compatibility issues, slowing down the deployment process and resulting in low deployment frequency.
Engineering teams should strive to be high-performing. The goal is to deploy as often as possible - the faster you deploy, the more value you can deliver to your users. Here are some ways you can improve deployment frequency in your team:
Reduce the deployment size
When a change is proposed, talk to the entire team to see how they would break the change into smaller components - then make small changes one at a time. This way, the developers work more efficiently because they focus on one project at a time.
Automated Testing
Automated testing is your friend! The goal of automated testing is to quickly and efficiently verify that a software application works as expected - without the need for manual intervention. This saves time, reduces the risk of human error, and enables organizations to deploy new features and updates more frequently.
Automated testing tools make it easier for organizations to implement continuous integration and delivery practices. These tools allow organizations to deploy new software updates frequently and quickly, as changes are automatically tested and validated as soon as they are committed to the codebase.
Continuous Integration
Continuous Integration (CI) is a software development practice that involves regularly integrating code changes into a single codebase. With CI, code changes are automatically built and tested as soon as they are committed to the codebase, reducing the risk of introducing bugs into the software. This helps to increase the development team's confidence and enables organizations to deploy new updates more frequently.
Collaboration and Communication
Effective collaboration and communication between teams are crucial to increasing deployment frequency. Regular meetings, status updates, and cross-functional collaboration help to ensure that everyone is aware of what is being worked on, what has been completed, and what needs to be done next. This helps to reduce delays, increase transparency, and improve overall efficiency.
Reduce technical debt
Technical debt refers to the accumulation of technical debt that occurs when software is developed quickly without taking the time to properly maintain and refactor the code. A Scandinavian study reveals that average organizations waste, on average, 23% of their development time due to technical debt. Over time, this debt can cause code to become more difficult to maintain, causing delays and increasing the risk of bugs and other issues.
By reducing technical debt, organizations can increase the efficiency of the software development process and reduce the risk of introducing bugs and other issues into the code. This, in turn, can increase the confidence of the development team and enable them to deploy new updates and features more frequently.
Measuring deployment frequency is a crucial aspect of software development that helps organizations understand their ability to quickly and efficiently deploy new updates and features to customers through the lens of DORA.
By tracking deployment frequency, organizations can identify areas for improvement, understand the impact of changes to the software development process, and ultimately increase the efficiency and effectiveness of their software development efforts.
Faros AI offers complete visibility over deployment frequency and other DORA metrics. You can easily integrate Faros AI into your tools like GitHub, GitLab, Circle CI, Jenkins, etc. To measure your deployment frequency across multiple teams, request a demo today!
Natalie is a software engineer, and most recently—a forward-deployed engineer at Faros.
Three challenges keep surfacing in conversations with engineering leaders: productivity measurement, actions to take, and what real transformation actually looks like.
