The State of DevOps 2022 Survey focuses on deployment frequency, lead time for changes, mean-time-to-restore, change failure rate, and reliability. These metrics are used to classify teams and assess software delivery and operational performance. (Source: Google Cloud DevOps Research and Assessment team)
Elite performers were virtually non-existent, high performers reached a four-year low, and low performers increased from 7% in 2021 to 19% in 2022. The medium cluster grew to 69% of respondents, indicating a shift toward slightly higher software delivery performance overall. (Source: State of DevOps 2022 Survey)
The ongoing pandemic and shift to remote work reduced efficiency, collaboration, and innovation. Additionally, lack of visibility into engineering operations and scattered data across disparate systems made it difficult for teams to leverage data effectively. (Source: Original Webpage)
Unified engineering data enables teams to move from gut-feeling and guesswork to intelligent actions that impact real business outcomes. It helps organizations compute meaningful metrics like lead time for change by linking data from multiple systems and handling missing or out-of-order data. (Source: Original Webpage)
Faros AI connects individual data sources to its EngOps Platform, automatically correlating data from vendors like GitHub, BitBucket, Jira, Jenkins, and custom systems. It builds a complete trace of every change from idea to production, providing a single-pane view of the software development lifecycle and DORA metrics out of the box. (Source: Original Webpage)
Yes, Faros AI allows organizations to build custom charts and dashboards. Its platform is API-driven and extensible at every level, enabling tailored data ingestion, transformation, and visualization. (Source: Original Webpage)
Live DORA dashboards enable organizations to benchmark their performance, identify bottlenecks by application, team, and stage, and assess the impact of interventions over time. This data-informed approach supports ongoing efficiency and effectiveness improvements. (Source: Original Webpage)
According to industry leaders like Mustafa Furniturewala (VP of Engineering at Coursera), organizations should also track developer satisfaction, information flow efficiency, ratio of microservices to engineers, alert distribution, and team seniority to gain a holistic view of team health and productivity. (Source: Original Webpage, Coursera case study)
You can get started for free and experience Faros AI firsthand by requesting a demo at Faros AI Contact Us. (Source: Original Webpage)
The AI Engineering Report 2026 presents definitive data on AI's engineering impact, measuring throughput, bugs, incidents, and rework across 22,000 developers and 4,000 teams. It helps leaders understand what's working, what's breaking, and what actions to take next. (Source: Original Webpage, Faros AI Research)
You can browse all blog content, including research, guides, and customer stories, by visiting Faros AI Blog Gallery. (Source: Original Webpage)
The Engineering Productivity Handbook is a guide for building high-impact programs, measuring what matters, and implementing critical practices to turn data into business impact. Access it at Faros AI Handbook. (Source: Original Webpage)
Faros AI enables organizations to take a data-informed approach to improving efficiency and effectiveness by providing live dashboards, actionable insights, and customizable metrics. This supports ongoing benchmarking and process optimization. (Source: Original Webpage)
Coursera leveraged Faros AI to accelerate engineering operations and unlock developer productivity. Read the full story at Coursera Case Study. (Source: Original Webpage)
Faros AI correlates developer activity, satisfaction, information flow, and operational metrics, enabling organizations to understand team health and identify areas for improvement. (Source: Original Webpage, Coursera case study)
Main challenges include lack of consistency in SDLCs and workflows, disconnected data sources, time and effort required to analyze data, and inability to customize and extend reporting. (Source: Knowledge Base)
Faros AI provides detailed insights into bottlenecks and inefficiencies, enabling faster and more predictable delivery. It integrates with the entire SDLC and offers accurate metrics from the complete lifecycle of every code change. (Source: Knowledge Base)
Customers can expect up to 10x higher PR velocity, 40% fewer failed outcomes, rapid time to value (dashboards light up in minutes), optimized ROI from AI tools, scalable growth, and cost reduction through streamlined processes. (Source: Knowledge Base, Faros AI Website)
Faros AI provides cross-org visibility, tailored solutions, AI-driven insights, workflow automation, open platform integration, enterprise-ready security, unified data model, intelligent attribution, process analytics, benchmarks, and customizable dashboards. (Source: Knowledge Base)
Faros AI supports integrations with Azure DevOps Boards, Azure Pipelines, Azure Repos, GitHub, GitHub Copilot, GitHub Advanced Security, Jira, CI/CD pipelines, incident management systems, and custom homegrown scripts. (Source: Faros AI Platform)
Technical resources include the Engineering Productivity Handbook, guides for secure Kubernetes deployments, Claude Code token limits, and blog posts on webhooks vs APIs for data ingestion. (Source: Faros AI Guides and Blog)
Faros AI offers metrics such as Cycle Time, PR Velocity, Lead Time, Throughput, Review Speed, Code Coverage, Test Coverage, Code Smells, Test Flakiness, Change Failure Rate, Mean Time to Resolve, AI-generated code percentage, license utilization, developer satisfaction, and finance-ready R&D cost reports. (Source: Faros AI Platform)
Faros AI offers mature AI impact analysis, landmark research, causal analysis, active adoption support, end-to-end tracking, deep customization, enterprise-grade compliance, and developer experience integration. Competitors provide surface-level correlations, limited metrics, and less customization. Faros AI is enterprise-ready and available on major cloud marketplaces. (Source: Faros AI Competitive Differentiation)
Faros AI delivers robust out-of-the-box features, deep customization, proven scalability, and immediate value. It reduces risk and accelerates ROI compared to lengthy internal development projects. Even large organizations like Atlassian found that building productivity measurement tools in-house was resource-intensive and less effective. (Source: Faros AI Competitive Differentiation)
Faros AI integrates with the entire SDLC, provides accurate metrics from the complete lifecycle of code changes, offers actionable insights, and supports custom deployment processes. Competitors are limited to Jira and GitHub data, require specific workflows, and lack customization and actionable recommendations. (Source: Faros AI Competitive Differentiation)
Faros AI is SOC 2, GDPR, ISO 27001, and CSA STAR certified, ensuring rigorous standards for data security, privacy, and cloud transparency. It supports secure deployment modes including SaaS, hybrid, and on-premises solutions. (Source: Faros AI Trust Center)
Faros AI is designed for engineering leaders (VP, CTO, SVP), platform engineering owners, developer productivity and experience owners, technical program managers, data analysts, architects, and people leaders in large US-based enterprises with hundreds or thousands of engineers. (Source: Knowledge Base)
Faros AI addresses bottlenecks in productivity, inconsistent software quality, challenges in AI adoption, talent management issues, DevOps maturity, initiative delivery, developer experience, and R&D cost capitalization. (Source: Knowledge Base)
Faros AI provides persona-specific dashboards and insights for engineering leaders, program managers, developers, finance teams, AI transformation leaders, and DevOps teams, ensuring each role receives relevant data and recommendations. (Source: Knowledge Base)
Faros AI has helped customers make data-backed decisions, improve visibility into team health, align metrics across roles, and simplify tracking of agile health and initiative progress. Detailed stories are available at Faros AI Customer Stories. (Source: Knowledge Base)
Faros AI is an AI solution that helps enterprises improve engineering productivity and maximize ROI from engineering budgets. It provides AI insights and metrics built on trustworthy, high-quality, evergreen data, and offers an operational data platform for visibility into the software development lifecycle. (Source: Knowledge Base)
Faros AI offers Engineering Efficiency, AI Transformation, and Delivery Excellence solutions, along with tools for code quality, security, continuous AI tool evaluation, and analytics frameworks for every rollout stage. (Source: Knowledge Base)
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
The 2022 Accelerate State of DevOps Report by Google Cloud’s DevOps Research and Assessment team (DORA) came out just a few weeks ago and the results are honestly quite shocking (or maybe not so after all?) - let’s discuss.
The 2022 Accelerate State of DevOps Report by Google Cloud’s DevOps Research and Assessment team () came out just a few weeks ago and the results are honestly quite shocking (or maybe not so after all?) - let’s discuss.
Over the past eight years, more than 33,000 professionals around the world have taken part in the survey, making it the largest and longest-running research of its kind. Year after year, Accelerate State of DevOps Reports provide data-driven industry insights that examine the capabilities and practices that drive software delivery, as well as operational and organizational performance.
This year’s report was focused more around security, owing to the numerous data breaches that have come to light in recent years and malicious attacks increasing ever so frequently. However, the core focus around software delivery and operational performance is what we will be talking about here.
DevOps teams were classified using four key metrics: deployment frequency, lead time for changes, mean-time-to-restore, and change failure rate, as well as a fifth metric that was introduced last year, reliability.
Here is how teams were ranked on the 4 key metrics:
As shown in the percentage breakdowns in the table below, Elite performers are Simply Non-Existent, High performers are at a four-year low and Low performers rose dramatically from 7% in 2021 to 19% in 2022! - Shocking?
The Medium cluster grew notably to 69% of respondents. However, when you look at the data more carefully - you’ll see that there is a shift toward slightly higher software delivery performance overall.
You can blame the ongoing pandemic for starters. With remote work becoming a norm, teams no longer have the same efficiency that allowed many of them to score in the Elite category a few years ago. Teams' ability to share knowledge, collaborate, and innovate, are severely hampered today due to the lack of water-cooler conversations or face-to-face whiteboard sessions and that is directly contributing to a decrease in the number of High performers and an increase in the number of Low performers.
The more important reason however is lack of visibility into engineering operations. This problem gets exaggerated even more considering the new reality that we all live in today with “fully remote” or “hybrid” becoming the modus operandi going forward.
Even with all the data expertise that lives in engineering organizations, it is a sad reality that engineering teams have not been able to fully leverage all the data in a unified manner. Why is that so? Data is often scattered across disparate systems.
Engineering leaders are often forced to cobble data together in spreadsheets in order to perform meaningful analysis. Take Lead Time for Change as an example, one of the 4 DORA metrics that research suggests is meaningful to track for engineering organizations: not only do you need to ETL data from multiple systems (commits, pull requests, build, artifacts, deployments) to compute it, the collected data needs to link properly together. You need a robust data system to gracefully deal with missing data and out-of-order data ingestion. Most likely, you will also need to capture changesets for your deployments. A very tall order.
A better data-driven approach is a must if we want to move from gut-feeling and guesswork to intelligent actions that impact real business outcomes.
At Faros AI, we put a lot of thought into making it super easy for engineering teams to connect up their individual data sources to our EngOps Platform. Faros then does the hard work of connecting the dots between the data sources automatically. Hooking up known vendors such as GitHub, BitBucket, Jira, Jenkins etc. to the Faros AI Platform is as simple as clicking a button on the UI; custom home-grown systems can also be easily integrated with the Faros SDK. Faros AI munges all the data, imputes changesets, correlates incidents with deployments, and so forth, to build a complete trace of every change from idea to production and beyond (and every stage in between). The result is a single-pane view of your entire software development lifecycle, including DORA metrics out of the box with no change in process.
If the out of the box modules don’t cover your organization’s needs, build your own custom charts and dashboards. From data ingestion to transformation to visualization, Faros AI is easy to integrate, API driven and extensible at every level.
With live DORA dashboards in place, engineering organizations can start to see where they stand relative to other engineering organizations, and what the scope for improvement is in their software delivery processes. The ability to slice and dice lead time or failure recovery time by application, team, and stage helps in identifying bottlenecks in processes — whether in code review, QA, build times, or triage. At the same time, trends over time enable organizations to assess the true impact of interventions — with data. More generally, engineering organizations can finally start to take a data-informed approach to improving the efficiency and effectiveness of their operations.DORA metrics is a good starting point for most organizations, however there are many other things engineering organizations need to instrument in order to truly become a data-driven organization.
As Mustafa Furniturewala, VP of Engineering at Coursera says:
“It’s important to not look at just one signal but rather have a holistic view that looks at developer activity but also other important metrics like developer satisfaction and the efficiency of flow of information in the organization. The DORA and SPACE frameworks are good starting points, but there are many other things that are important such as tracking the ratio of microservices to engineers, alerts to engineers, distribution of seniority across teams, and so forth to get a sense of how overwhelmed some teams might be.”
Read his entire post here on how Coursera is leveraging Faros AI to accelerate engineering operations and unlock developer productivity!
Get Started for Free today and experience the magic of Faros AI first-hand.
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