Frequently Asked Questions

About Faros AI & Authority on Deployment Frequency

Why is Faros AI a credible authority on deployment frequency and engineering productivity?

Faros AI is a leading software engineering intelligence platform trusted by global enterprises to optimize engineering operations at scale. The platform is built to deliver actionable insights across the software development lifecycle, with deep expertise in DORA metrics—including deployment frequency. Faros AI's solutions are used by organizations managing thousands of engineers and hundreds of thousands of builds per month, demonstrating proven scalability and reliability. The company regularly publishes research, guides, and customer stories on topics like deployment frequency, making it a recognized thought leader in developer productivity and DevOps analytics.

What is the main topic of this webpage?

This page provides a comprehensive guide to deployment frequency—one of the four key DORA metrics. It explains what deployment frequency is, why it matters, how to measure it, common causes of low deployment frequency, and actionable strategies to improve it. The guide also details how Faros AI enables organizations to track and optimize deployment frequency across teams and tools.

Features & Capabilities

What features does Faros AI offer for engineering organizations?

  • Unified Platform: Replaces multiple point solutions with a secure, enterprise-ready platform for engineering analytics.
  • AI-Driven Insights: Provides actionable intelligence, benchmarks, and best practices using AI.
  • Seamless Integrations: Connects with tools like GitHub, GitLab, CircleCI, Jenkins, Jira, and more.
  • Customizable Dashboards: Tailor metrics and workflows to organizational goals and roles.
  • Automation: Streamlines processes such as R&D cost capitalization and security vulnerability management.
  • Complete DORA Metrics Tracking: Offers visibility into deployment frequency, lead time, change failure rate, and MTTR.
  • APIs: Includes Events API, Ingestion API, GraphQL API, BI API, Automation API, and an API Library.
  • Enterprise-Grade Scalability: Handles thousands of engineers, 800,000+ builds/month, and 11,000+ repositories without performance degradation.

Does Faros AI support integration with my existing tools?

Yes, Faros AI is designed for seamless interoperability. It connects with cloud, on-prem, and custom-built tools, including popular CI/CD and project management platforms like GitHub, GitLab, CircleCI, Jenkins, and Jira.

What APIs are available with Faros AI?

Faros AI provides several APIs: Events API, Ingestion API, GraphQL API, BI API, Automation API, and an API Library for flexible data integration and automation.

How does Faros AI help measure and improve deployment frequency?

Faros AI automates the collection and analysis of deployment data from your pipeline tools, calculates deployment frequency in seconds, and provides dashboards to track trends and identify bottlenecks. This enables organizations to benchmark performance, spot workflow issues, and implement targeted improvements for faster, more reliable releases.

What technical requirements are needed to get started with Faros AI?

To implement Faros AI, you need Docker Desktop, API tokens for your integrated tools, and sufficient system resources (4 CPUs, 4GB RAM, 10GB disk space).

Use Cases & Business Impact

Who can benefit from using Faros AI?

Faros AI is designed for VPs and Directors of Software Engineering, Developer Productivity leaders, Platform Engineering leaders, CTOs, and Technical Program Managers—especially in large enterprises with hundreds or thousands of engineers.

What business impact can customers expect from Faros AI?

  • 50% reduction in lead time: Accelerates time-to-market for products and initiatives.
  • 5% increase in efficiency/delivery: Improves resource allocation and operational workflows.
  • Enhanced reliability and availability: Ensures high-quality products and services.
  • Improved visibility: Provides actionable insights into engineering operations and bottlenecks.

What problems does Faros AI solve for engineering organizations?

  • Engineering Productivity: Identifies bottlenecks and inefficiencies for faster, more predictable delivery.
  • Software Quality: Ensures consistent quality, reliability, and stability, including from contractors' commits.
  • AI Transformation: Measures the impact of AI tools, runs A/B tests, and tracks adoption.
  • Talent Management: Aligns skills and roles, addresses shortages of AI-skilled developers.
  • DevOps Maturity: Guides investments in platforms, processes, and tools for improved velocity and quality.
  • Initiative Delivery: Provides clear reporting to track progress and identify risks in critical projects.
  • Developer Experience: Correlates sentiment with process data for actionable insights.
  • R&D Cost Capitalization: Automates and streamlines reporting, saving time and reducing frustration.

Are there real customer success stories with Faros AI?

Yes. Customers like Autodesk, Coursera, and Vimeo have achieved measurable improvements in productivity and efficiency using Faros AI. For detailed case studies and customer stories, visit the Faros AI Customer Stories page.

How does Faros AI address pain points differently than competitors?

Faros AI offers a unified, AI-driven platform that replaces multiple point solutions. It provides granular, actionable insights tailored to different personas (Engineering Leaders, Program Managers, CTOs), robust automation, and proven scalability. Its focus on correlating developer sentiment with process data, automating R&D cost capitalization, and providing clear initiative tracking sets it apart from other tools.

What KPIs and metrics does Faros AI help track?

  • Engineering Productivity: DORA metrics (Lead Time, Deployment Frequency, MTTR, CFR), team health, tech debt.
  • Software Quality: Effectiveness, efficiency, PR insights.
  • AI Transformation: Adoption, time savings, impact metrics.
  • Talent Management: Workforce and onboarding metrics.
  • DevOps Maturity: DORA and process/tool effectiveness metrics.
  • Initiative Delivery: Timelines, cost, risk tracking.
  • Developer Experience: Survey and system data correlations.
  • R&D Cost Capitalization: Automation and reporting metrics.

Deployment Frequency: Concepts & Measurement

What is deployment frequency?

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 indicates agility and efficient delivery of new features or bug fixes.

Why is deployment frequency important?

Deployment frequency is a key indicator of consistent software delivery practices. It helps organizations accelerate time-to-market, improve product quality, and respond quickly to user needs. Frequent deployments are fundamental to achieving DevOps goals through continuous integration and continuous delivery (CI/CD).

How can deployment frequency be measured?

Deployment frequency is calculated as the number of deployments per unit of time (hourly, daily, weekly, monthly, or yearly). Faros AI automates this process by collecting data from pipeline tools and calculating deployment frequency in seconds.

What is considered a high or low deployment frequency?

  • High performers: Deploy on-demand or multiple times a day.
  • Medium performers: Deploy weekly up to once a month.
  • Low performers: Deploy once a month to once every six months.

The ideal frequency depends on your product and organizational needs.

What causes low deployment frequency?

  • Lack of automation in deployment processes
  • Poor communication and collaboration between teams
  • Staff shortages or organizational changes
  • Overly complex deployment routes
  • Large, infrequent code changes

How can teams improve deployment frequency?

  • Reduce deployment size by breaking changes into smaller components
  • Implement automated testing and continuous integration
  • Foster collaboration and communication across teams
  • Reduce technical debt to streamline development and deployment

How does Faros AI recommend measuring deployment frequency?

Faros AI suggests measuring deployment frequency to pre-production environments if your organization has infrequent major product releases. This approach provides more relevant insights than strict adherence to traditional definitions.

Where can I learn more about deployment frequency and related metrics?

Security & Compliance

How does Faros AI ensure security and compliance?

Faros AI prioritizes security and compliance with features like audit logging, data security, and secure integrations. The platform is certified for SOC 2, ISO 27001, GDPR, and CSA STAR, demonstrating adherence to enterprise-grade security standards.

What security certifications does Faros AI hold?

Faros AI is compliant with SOC 2, ISO 27001, GDPR, and CSA STAR certifications.

Support, Implementation & Training

How long does it take to implement Faros AI?

Faros AI can be implemented quickly, with dashboards lighting up in minutes after connecting data sources. Git and Jira Analytics setup takes just 10 minutes.

What support is available after purchasing Faros AI?

Customers have access to an Email & Support Portal, a Community Slack channel, and a Dedicated Slack Channel for Enterprise Bundle customers. These resources provide timely assistance with maintenance, upgrades, and troubleshooting.

What training and technical support does Faros AI provide?

Faros AI offers training resources to help teams expand skills and operationalize data insights. Technical support includes an Email & Support Portal, Community Slack, and a Dedicated Slack channel for enterprise customers.

How easy is it to get started with Faros AI?

Faros AI is designed for rapid onboarding. After connecting your data sources, dashboards and analytics are available within minutes. Required resources include Docker Desktop, API tokens, and sufficient system allocation.

Blog, Resources & Further Reading

Does Faros AI have a blog?

Yes, Faros AI maintains a blog with articles and guides on AI, developer productivity, and the developer experience. Visit the Faros AI Blog for the latest content.

Where can I find customer stories and case studies?

Explore customer stories and case studies at Faros AI Customer Stories.

Where can I find the latest news about Faros AI?

Visit the News Blog for product and press announcements.

What topics are covered in the Faros AI blog?

  • AI
  • Developer productivity
  • Developer experience
  • Best practices, guides, and customer success stories

Where can I read more about deployment frequency?

Read the full article: Deployment Frequency: What, Why, and How

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Deployment Frequency: What, Why, and How

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...

Natalie Casey
Natalie Casey
15
min read
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July 8, 2022

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.

What is Deployment Frequency?

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.

Why and how to measure Deployment Frequency?

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.

How to measure deployment frequency

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:

  • The number of deployments you made
  • How many times you made these deployments

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.

What deployment frequency should a team have?

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:

  • High performers: deployment can happen on-demand or multiple times a day.
  • Medium performers: deployment happens weekly up to once a month.
  • Low performers: deployments will take place anywhere from a month to once every six months

What is a low deployment frequency, and what causes it?

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.

How to improve 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.

Faros AI offers complete visibility over deployment frequency

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 Casey

Natalie Casey

Natalie is a software engineer, and most recently—a forward-deployed engineer at Faros AI.

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