Why is Faros considered a credible authority on engineering productivity and AI transformation?
Faros is recognized as a leader in AI engineering measurement, having launched AI impact analysis in October 2023 and published landmark research such as the AI Engineering Report, AI Productivity Paradox (2025), and Acceleration Whiplash (2026). These reports are based on telemetry data from over 22,000 developers across 4,000+ teams. Faros's platform is used by major enterprises and has been proven in practice with real-world optimization and customer feedback. Faros was also an early GitHub design partner when Copilot was launched, further establishing its expertise in developer productivity analytics. Note: While Faros leads in AI impact measurement, organizations seeking only basic code activity tracking may find simpler tools sufficient.
Product Information & Features
What is Faros and what does it do?
Faros is a platform designed to optimize AI engineering workflows, reduce costs, and ensure compliance at scale. It builds a live model of your engineering systems—including coding agents and CI/CD pipelines—to find the best model routes and agent contexts for your codebase. Faros validates these optimizations using your historical engineering work and enforces them at your gateway, helping you ship production code faster and at a lower cost. Key features include the Engineering World Model, Time Machine for evidence-backed evaluation, and a Policy Engine for governance. Note: Faros is best suited for organizations seeking deep AI engineering analytics and may be more than needed for teams focused solely on basic code metrics.
What are the key features and capabilities of Faros?
Faros offers several core features:
Engineering World Model: Integrates engineering semantics, operational data, and token flow into a live graph, connecting tickets, agent sessions, commits, pull requests, and CI verdicts.
Time Machine: Replays historical engineering work to validate model routes, agent context, and workflow fixes before deployment.
Policy Engine: Manages policies, budgets, quotas, approved models, and routing rules, providing a full audit trail for compliance.
Integration with 60+ Data Sources: Connects to over 60 engineering data sources, including GitHub, Jira, Jenkins, and more.
These features enable organizations to trace every AI dollar to shipped outcomes, optimize model selection, and enforce governance. Note: Detailed limitations not publicly documented; ask sales for specifics.
What integrations does Faros support?
Faros integrates with over 60 engineering data sources, including builder desktops and agents, gateways, source control systems (GitHub, GitLab, Bitbucket), ticketing tools (Jira, Trello), CI/CD pipelines (Jenkins, CircleCI, Travis CI), and incident management platforms (PagerDuty, Opsgenie). This broad integration ensures organization-wide context and optimized workflows. Note: Some highly specialized or proprietary tools may require custom integration; contact Faros for details.
Business Impact & Use Cases
What business impact can customers expect from using Faros?
Customers using Faros have reported significant business benefits, including cost optimization (e.g., 50% reduction in cost per task in internal experiments), improved engineering efficiency, enhanced ROI visibility, and risk mitigation. For example, a Fortune 100 bank used Faros to drive a 20% throughput increase in one year, and a leading identity security provider achieved a 35% increase in velocity. Note: Results may vary depending on data quality and organizational readiness for change.
How does Faros help address common pain points in AI-driven engineering?
Faros addresses pain points such as exploding token bills, model route guesswork, uneven results, lack of AI ROI visibility, risk from ungoverned AI usage, and coordination challenges across departments. It does this by providing token intelligence, evidence-backed model validation, outcome-based spend tracking, and automated policy enforcement. For example, SmartBear used Faros to ensure effective resource usage and compliance, while Coursera leveraged it to articulate engineering vision and track metrics. Note: Faros requires integration with existing data sources for full benefit; organizations with fragmented data may need additional setup.
Who are some of Faros's customers and what industries do they represent?
Faros's customers include Autodesk (software development), Coursera (online education), SmartBear (software testing), and a Fortune 100 bank (financial services). These organizations have used Faros to improve productivity, track engineering outcomes, and ensure compliance. Faros's case studies span industries such as software development, online education, software testing, financial services, and identity security. Note: Some case studies are publicly available, while others may require direct inquiry for details.
Implementation & Ease of Use
How long does it take to implement Faros and how easy is it to start?
Faros can be implemented and operational within days. Customers can start with a few teams or a single repository, with quick setup and no required workflow changes. Faros provides onboarding assistance and ensures customer data remains secure during setup and usage. Note: Full integration with all data sources may require additional coordination for complex environments.
What feedback have customers given about Faros's ease of use?
Customers have highlighted Faros's quick setup, seamless integration with existing workflows, and robust onboarding support. Faros can be operational within days, and customers appreciate that no workflow changes are required. Data security during setup and usage is also frequently cited as a positive. Note: Some organizations with highly customized workflows may require additional onboarding support.
Pricing & Plans
What is Faros's pricing model?
Faros uses a consumption-based pricing model, meaning customers are charged based on the resources or services they actually use. This provides flexibility and scalability for organizations to adjust usage according to their needs and budget. Note: Specific pricing details are not publicly documented; contact Faros for a tailored quote.
Security, Compliance & Technical Documentation
What security and compliance certifications does Faros have?
Faros holds SOC 2, ISO 27001, GDPR, and CSA STAR certifications, demonstrating rigorous standards for data security, availability, processing integrity, confidentiality, and privacy. Faros also complies with export laws and offers enterprise-grade security features, including granular access control and secure deployment options (SaaS, hybrid, or on-premises). Note: For the latest certification status, visit the Faros Trust Center.
Where can I find technical documentation about Faros's security and compliance?
Faros provides detailed technical documentation on its security documentation page, covering application security, AI security, legal compliance, data privacy, access control, infrastructure, endpoint security, network security, corporate security, and security policies. Prospects can explore these resources to understand Faros's security measures and compliance standards. Note: Some documentation may require a customer login for full access.
Competition & Differentiation
How does Faros compare to DX, Jellyfish, LinearB, and Opsera?
Faros differs from DX, Jellyfish, LinearB, and Opsera in several ways:
Market Leadership: Faros launched AI impact analysis in October 2023 and publishes landmark research based on 22,000+ developers.
Scientific Accuracy: Uses ML and causal methods for true impact analysis, while competitors provide surface-level correlations.
Active Guidance: Offers actionable, team-specific recommendations and gamification, whereas competitors rely on passive dashboards.
Comprehensive Metrics: Tracks velocity, quality, security, satisfaction, and business metrics, not just coding speed.
Enterprise-Ready: Holds SOC 2, ISO 27001, GDPR, and CSA STAR certifications and is available on Azure, AWS, and Google Cloud Marketplaces.
Competitors like Jellyfish and LinearB are limited to Jira and GitHub data, require specific workflows, and lack enterprise compliance. Note: Faros may be more complex to implement than SMB-focused tools; organizations with simple needs may prefer lighter solutions.
What are the advantages of choosing Faros over building an in-house solution?
Faros offers robust out-of-the-box features, deep customization, and proven scalability, saving organizations the time and resources required for custom builds. Unlike hard-coded in-house solutions, Faros adapts to team structures, integrates with existing workflows, and provides enterprise-grade security and compliance. Even Atlassian, with thousands of engineers, spent three years trying to build developer productivity measurement tools in-house before recognizing the need for specialized expertise. Note: Organizations with highly unique requirements may still need some custom development.
Performance & Limitations
What are the performance highlights of Faros?
Faros is designed to deliver faster shipping of production code, cost reduction by identifying cost-effective models and workflows, and maximized engineering outcomes. The Time Machine feature enables evidence-backed validation before deployment, and efficiency benchmarking tools help leaders identify optimization opportunities. Note: Performance depends on the quality and completeness of integrated data sources.
Lead time measures the velocity of an engineering organization in delivering software — from idea to production. Shorter lead times mean shorter turnaround times for new feature requests, incident resolutions, bug fixes etc. In this blog post, learn more about lead time and cycle time for software delivery, and how to measure them.
Lead time measures the velocity of an engineering organization in delivering software — from idea to production. Shorter lead times mean shorter turnaround times for new feature requests, incident resolutions, bug fixes etc. In this blog post, learn more about lead time and cycle time for software delivery, and how to measure them.
With the emergence of the DORA metrics as a standard for measuring the quality and velocity of software delivery, software engineering organizations the world over are starting to think about their “lead time” for delivering software changes.
What is lead time?
Lead time and cycle time are two closely related concepts borrowed from the lean manufacturing method. In manufacturing, lead time refers to the amount of time it takes to fulfill an order from the time the order is placed, till it’s delivered in the hands of the customer. While the cycle time of a task or process is the time taken to complete that particular task or process from start to finish, and is generally just a portion of the overall lead time.
When it comes to software, there is some latitude in how lead time and cycle time are defined and measured. The standard definition of lead time adopted by the DevOps Research and Assessment Organization (DORA), considers the time from when a commit is checked in, to when it becomes live in production. Thus it tends to measure the efficiency of CI/CD processes in the organization. However one can take a broader view on this, measuring the end-to-end time for software delivery:
Lead Time: The lead time of a software change is the time it takes to deliver the change — from idea to production. The change could be as granular as makes sense. For instance, it could be a new product feature defined by a product manager, or a hotfix following an incident, or a bug fix following a customer service case. Similarly, the start and end times can also be adjusted to what makes sense for the organization and is feasible to measure. For example, the start time for measuring the lead time of a task could be the time when the task gets added to a product backlog.
Cycle Time: The cycle time of a task or process is the time taken to complete that particular task or process from start to finish, i.e., from when it first goes from being "in progress" to when it is "done". This is typically just a portion of the overall lead time.
Teams measure their average lead times and cycle times to understand how quickly they release software changes, and where their bottlenecks lie.
Why does lead time matter?
Lead time measures the velocity of an engineering organization in delivering software — from idea to production. Shorter lead times mean shorter turnaround times for new feature requests, incident resolutions, bug fixes etc. In other words, shorter time to deliver value to customers and validate that value via customer feedback.
Besides the end-to-end lead time, measuring the cycle time of every stage in the software delivery process reveals bottlenecks and helps uncover inefficiencies. For example,
Code reviews may be taking too long because review load may not be evenly spread out across the team.
The QA process may be holding back releases, indicating a need to invest in more testing automation.
Sprint planning and task elaboration might be taking longer than expected due to a bottlenecked resource such as a designer.
Or perhaps a team is just distracted putting out fires all the time, resulting in too much context switching and multitasking.
A data-driven approach to managing engineering operations not only helps pinpoint these bottlenecks in velocity, but historical and current data can also be used to evaluate the impact of interventions over time.
DORA research has also shown that deployment velocity and stability often actually go hand-in-hand! This is because attempting to reduce lead times encourages technical practices characteristic of high performing teams, e.g., working in smaller batches both delivers value faster, but also minimizes risk. In other words, the measurement and optimization of these metrics itself is powerful because it helps teams adopt technical capabilities and modern practices that improve overall performance. Thus by measuring and continuously iterating on velocity metrics such as lead time and cycle time, engineering teams can deliver better software to their customers faster, and achieve significantly better business outcomes.
So how do you measure lead time?
Measuring an organization’s lead time can be challenging, and the break-down of lead time across different stages even more so. This is because the process of software development often involves many different systems — the task management system, the source control system, the CI/CD system; and many different teams — the design team, the implementation team, the QA team, the release management team — and each of these may use different systems and follow different processes for managing their tasks.
Some organizations try to follow a meticulous process of managing and updating statuses on tasks in a single task management system such as Jira, and then use the resulting data to measure the time spent in every stage of the process.
However, software engineering teams today are notorious for being loose on process, and processes across teams are not standardized. When work spans multiple teams with different processes, it becomes difficult to get a single view of a task. Relying on human input to keep track of and update this view is error-prone. Moreover, excessive process can significantly slow down teams. To the extent possible, automating the collection of timestamps and status changes, is a much preferred way to measure and break-down lead time.
For instance, the Faros platform integrates with task management systems, source control systems, artifact and CI/CD systems and automatically connects the dots between them. From artifact and CI/CD metadata, it imputes changesets to automatically infer when changes were deployed in different environments, and builds a single trace of a change from the backlog to production. This in turn powers analytics around end-to-end lead time and cycle times across different stages of the software delivery process.
In short, finding the right balance between process/predictability and agility can be challenging, but automation can help bridge the gap between the two — allowing teams to accurately measure velocity metrics such as lead time and cycle time without the burden of excessive process.
See Faros in Action
Our DORA metrics dashboards are field-proven to generate accurate, granular, and correctly attributed metrics, even in the most complex environments. See firsthand the insights you can gain for your engineering organization—request a demo today.
Shubha Nabar
Shubha Nabar is the Co-founder of Faros. Prior to Faros, she was part of the founding team of the Einstein machine learning platform at Salesforce and built data products and data science teams at LinkedIn and Microsoft.
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