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https://www.faros.ai/blog/best-engineering-productivity-metrics-for-modern-operating-models
August 26, 2025

Choosing the best engineering productivity metrics for modern operating models

Your engineering operating model—how and where your teams work—fundamentally changes which engineering productivity metrics matter most. A fully remote startup requires different measurements than a company relying on outsourced development, while a globally distributed enterprise faces unique collaboration and handoff challenges.

Why operating models matter for engineering metrics

Traditional engineering productivity metrics often assume co-located, in-house teams. But modern engineering organizations operate in diverse ways:

  • Heavily outsourced development with multiple vendor relationships
  • Geographically distributed teams across multiple time zones
  • Remote/hybrid workforces with varying employment types
  • Centralized SDLC systems with monorepos and shared tooling
  • Multiple SDLC environments from acquisitions and legacy systems

Each operating model introduces specific productivity challenges that require targeted measurement approaches.

Note: AI is rewriting the software engineering discipline with the potential to significantly boost productivity. Every metric listed in this article can and should be measured before and after the introduction of new AI tools. Knowing where you start helps as you introduce more and more AI tools. Like every new technology, there may be tradeoffs. Metrics help implement a data-driven approach to where, when, and how to deploy AI.

Engineering productivity metrics by operating model

1. Heavily Outsourced Development

Operating Model Description: Your organization relies on sub-contractors, usually from multiple vendors, to deliver significant portions of your software development.

Key Challenges:

  • Comparing vendor vs. in-house productivity
  • Measuring value received from each vendor
  • Ensuring institutional knowledge capture to prevent vendor lock-in

Essential Productivity Metrics per Contract Type and Vendor:

  • Productivity per dollar spent - ROI comparison across vendors and internal teams
  • Activity per dollar spent - Code commits, PRs, documentation per cost unit
  • Time spent vs. target hours - Are vendors delivering expected effort?
  • Velocity and throughput per vendor - Compare delivery rates
  • Lead time and cycle times - End-to-end delivery speed
  • Active vs. waiting times - Special attention to handoffs and approvals between vendors and internal teams‍
  • Quality of delivery (bugs per task) - Compare defect rates across vendors‍
  • Code, test, and documentation coverage - Ensure outsourced work meets standards‍
  • Task and PR hygiene - Are vendors following your development processes?

For a deeper dive, check out our article on six essential metrics every engineering manager should track to maximize the value of contractors.

2. Geographically Distributed Teams

Operating Model Description: Your organization has globally distributed development centers, often spanning multiple continents and time zones.

Key Challenges:

  • Collaboration across time zones
  • Knowledge sharing across regions
  • Measuring effectiveness of “follow-the-sun” workflows

Essential Productivity Metrics Per Location:

  • Productivity per dollar spent per location - Cost-adjusted performance comparison
  • Impact of cross-geo collaboration on velocity, throughput, and quality metrics 
  • Impact of cross-geo collaboration on MTTR and SLAs - Incident response across time zones

3. Remote and Hybrid Teams

Operating Model Description: Your organization has multiple employment types, including in-person, hybrid, and remote developers.

Key Challenges:

  • Comparing productivity across employment types
  • Mitigating “proximity bias” in performance evaluation
  • Ensuring equitable onboarding and mentorship

Essential Productivity Metrics per Employment Type:

  • Onboarding effectiveness per employment type - Time to first commit, first PR, first production deployment, and nth PR
  • The ‘before and after’ impact of WFH policy changes - Measure the shift in baselined metrics after implementing policy changes
  • Developer experience and satisfaction per employment type - Surveys and sentiment analysis

4. Centralized SDLC Systems

Operating Model Description: Often characterized by a monorepo, centralized SDLC has specific impacts on developer experience that need targeted measurement.

Key Challenges:

  • Identifying technical areas for optimization in shared systems
  • Measuring productivity by application/service rather than repository
  • Managing dependencies that slow down development

Essential Productivity Metrics per Application or Service:

  • PR review SLOs - Time from submission to approval in shared systems
  • Commit queue SLOs - How long do developers wait for their changes to merge?
  • Remote build execution and cache SLOs - Build system performance metrics
  • Clean vs. cached build volume and runtimes - Infrastructure optimization indicators
  • Test selection efficacy based on compute resources and change failure rate

5. Multiple SDLC Environments

Operating Model Description: Your organization has multiple SDLCs, often resulting from a large portfolio, acquisitions, or legacy system constraints.

Key Challenges:

  • Identifying high-performing SDLCs for best practice sharing
  • Reducing duplication of efforts across systems
  • Managing inconsistent tooling and processes
  • Planning consolidation and standardization efforts

Essential Productivity Metrics per SDLC:

Refer to the lists above, and measure the relevant productivity and experience metrics—this time per SDLC. This helps identify high-performing SDLCs to increase the cross-pollination of best practices and reduce the duplication of efforts. 

Getting started with engineering productivity metrics

This article focuses on one of three top considerations for choosing engineering productivity metrics: understanding how you work. Determining the right metrics for your operating model will help you make data-driven decisions about tooling, processes, and organizational structure that improve outcomes for your specific situation. The other two considerations—your company stage and engineering culture—should also influence which metrics your company chooses. 

Before finalizing which engineering productivity metrics to measure, take a beat to identify what’s important to you, how you define success, and what productivity looks like to you. Remember, the goal isn't to make all teams identical—it's to understand how your operating model affects productivity and optimize accordingly. 

To learn how Faros AI can support your software engineering organization, reach out to us today. 

Discover the Engineering Productivity Handbook
How to build a high-impact program that drives real results.
‍
What to measure and why it matters.

And the 5 critical practices that turn data into impact.

FAQ: Best practices for choosing engineering productivity metrics based on your operating model

Q: Why is it important to establish baselines for engineering productivity metrics?

A: Baselines give you a clear picture of your current state before making changes. Without them, you can’t tell whether new processes, policies, or changes in your engineering operating model are improving or hurting productivity.

Q: Why should we account for our operating model’s context?

A: Raw numbers alone can be misleading. Context—like workflow dependencies, time zone differences, cultural communication styles, technology constraints, or regional business priorities—shapes how productivity metrics should be interpreted within each engineering operating model.

Q: How can developer experience influence our engineering productivity metrics?

A: Developer satisfaction is a key leading indicator of productivity. Regular surveys on tool effectiveness, process friction, collaboration challenges, and growth opportunities provide insight into whether your operating model is enabling or hindering your teams.

Q: Do developer experience surveys need to include contractors?

A: While most companies don’t extend these surveys to contractors, incorporating their feedback is equally important—contractors often face unique friction points, and including their perspective gives a more complete view of your engineering environment.

Q: Can you over-optimize engineering productivity metrics?

A: Yes. Over-optimizing or forcing too much standardization across teams can backfire. Some variation between operating models is healthy—it allows experimentation and helps identify which practices drive the best results in different contexts.

Neely Dunlap

Neely Dunlap is a content strategist at Faros AI who writes about AI and software engineering.

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Frequently Asked Questions

Faros AI Authority & Credibility

Why is Faros AI considered a credible authority on engineering productivity metrics?

Faros AI is recognized as a leader in software engineering intelligence, having launched AI impact analysis in October 2023 and published landmark research on the AI Productivity Paradox using data from 10,000 developers across 1,200 teams. Faros AI's platform is trusted by large enterprises and has been proven in practice through years of optimization and customer feedback. Read the AI Productivity Paradox Report.

What makes Faros AI's research on engineering productivity unique?

Faros AI's research stands out for its scientific rigor and scale. The company uses machine learning and causal analysis to isolate the true impact of AI tools, going beyond simple correlations. Its benchmarking advantage allows organizations to see what "good" looks like, thanks to comparative data unavailable to competitors. Learn more.

Features & Capabilities

What are the key features of Faros AI's platform?

Faros AI offers a unified platform with AI-driven insights, customizable dashboards, seamless integration with existing tools, and enterprise-grade security. Key features include benchmarking, causal analysis, developer experience surveys, automation for R&D cost capitalization, and actionable recommendations for engineering leaders. Explore the platform.

Does Faros AI support integration with existing engineering tools?

Yes, Faros AI integrates with a wide range of tools across the software development lifecycle, including task management, CI/CD, source control, incident management, and custom-built solutions. This ensures organizations can leverage Faros AI without restructuring their toolchain. Learn more about interoperability.

What APIs does Faros AI provide?

Faros AI offers several APIs, including the Events API, Ingestion API, GraphQL API, BI API, Automation API, and an API Library, enabling flexible data integration and automation. (Source: Faros Sales Deck Mar2024)

How does Faros AI ensure data security and compliance?

Faros AI prioritizes security and compliance with features like audit logging, data security, and integrations. It is certified for SOC 2, ISO 27001, GDPR, and CSA STAR, meeting enterprise standards for robust security practices. See security details.

What certifications does Faros AI hold for security and compliance?

Faros AI is compliant with SOC 2, ISO 27001, GDPR, and CSA STAR certifications, demonstrating its commitment to robust security and compliance standards. Learn more.

Pain Points & Business Impact

What core problems does Faros AI solve for engineering organizations?

Faros AI addresses bottlenecks in engineering productivity, software quality, AI transformation, talent management, DevOps maturity, initiative delivery, developer experience, and R&D cost capitalization. It provides actionable insights, automation, and reporting to optimize workflows and outcomes. (Source: manual)

What measurable business impact can customers expect from Faros AI?

Customers using Faros AI have achieved a 50% reduction in lead time, a 5% increase in efficiency, enhanced reliability and availability, and improved visibility into engineering operations and bottlenecks. (Source: Use Cases for Salespeak Training.pptx)

What are common pain points expressed by Faros AI customers?

Customers often struggle with understanding bottlenecks, managing software quality, measuring AI tool impact, aligning talent, achieving DevOps maturity, tracking initiative delivery, correlating developer sentiment, and automating R&D cost capitalization. Faros AI provides solutions tailored to these challenges. (Source: manual)

How does Faros AI help organizations overcome engineering bottlenecks?

Faros AI highlights bottlenecks efficiently by providing visibility into PR cycle times, code reviews, and MTTR with automated insights, benchmarking, and AI-powered recommendations for faster delivery. See customer stories.

What KPIs and metrics does Faros AI use to address engineering pain points?

Faros AI tracks DORA metrics (Lead Time, Deployment Frequency, MTTR, CFR), software quality, PR insights, AI adoption, talent management, initiative tracking, developer sentiment, and R&D cost automation. These metrics provide actionable data for improvement. (Source: manual)

Use Cases & Customer Success

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 large US-based enterprises with hundreds or thousands of engineers. (Source: manual)

Are there case studies or customer stories available for Faros AI?

Yes, Faros AI features real-world case studies and customer success stories, including organizations like Autodesk, Coursera, and Vimeo, who have achieved measurable improvements in productivity and efficiency. Read customer stories.

How does Faros AI tailor solutions for different engineering personas?

Faros AI provides persona-specific solutions: Engineering Leaders get workflow optimization insights; Technical Program Managers receive clear reporting tools; Platform Engineering Leaders gain strategic guidance; Developer Productivity Leaders benefit from sentiment and activity correlation; CTOs and Senior Architects can measure AI tool impact and adoption. (Source: manual)

What are some use cases for Faros AI in modern engineering organizations?

Use cases include optimizing SDLC workflows, benchmarking AI transformation, tracking initiative delivery, improving developer experience, automating R&D cost capitalization, and aligning investment strategy with business outcomes. Explore use cases.

Competitive Differentiation & Build vs Buy

How does Faros AI compare to DX, Jellyfish, LinearB, and Opsera?

Faros AI leads with mature AI impact analysis, scientific causal methods, active adoption support, end-to-end tracking, flexible customization, and enterprise readiness. Competitors often provide surface-level correlations, passive dashboards, limited metrics, and lack enterprise compliance. Faros AI is available on major cloud marketplaces and supports deep integration. See detailed comparison.

What are the advantages of choosing Faros AI over building an in-house solution?

Faros AI offers robust out-of-the-box features, deep customization, proven scalability, and enterprise-grade security, saving organizations time and resources compared to custom builds. Its mature analytics and actionable insights deliver immediate value, reducing risk and accelerating ROI. Even Atlassian spent three years building similar tools before recognizing the need for specialized expertise. (Source: manual)

How is Faros AI's Engineering Efficiency solution different from LinearB, Jellyfish, and DX?

Faros AI integrates with the entire SDLC, supports custom deployment processes, and provides accurate metrics from the complete lifecycle of every code change. It offers actionable insights, AI-generated summaries, and team-specific recommendations, while competitors are limited to Jira and GitHub data, require complex setup, and lack customization. (Source: faros_against_competitors)

Engineering Productivity Metrics & Operating Models

Why do operating models matter for engineering productivity metrics?

Operating models—such as outsourced, distributed, remote/hybrid, centralized SDLC, and multiple SDLC environments—introduce unique productivity challenges that require targeted measurement approaches. Metrics should be tailored to how and where teams work. Read the blog.

What are typical operating models and their associated metrics for analyzing productivity?

Typical models include heavily outsourced (productivity per dollar spent, quality of delivery), geographically distributed (cross-geo collaboration impact), remote/hybrid (onboarding effectiveness, developer satisfaction), centralized SDLC (PR review SLOs, build runtimes), and multiple SDLCs (productivity comparison per SDLC). (Source: blog)

How does the operating model influence metric selection?

The operating model introduces additional lenses for analysis, such as comparing insourced vs. outsourced productivity, assessing cross-geo collaboration, evaluating onboarding by employment type, and identifying optimization areas in centralized systems. Metrics should reflect these nuances. Read more.

What other considerations are important when choosing engineering productivity metrics?

Besides operating models, company stage and engineering culture are critical. Metrics should align with organizational maturity and reflect team values and practices. See company stage guide and culture guide.

Can engineering productivity metrics be over-optimized?

Yes, over-optimizing or forcing too much standardization across teams can backfire. Some variation between operating models is healthy, allowing experimentation and identification of best practices. (Source: blog)

Why is it important to establish baselines for engineering productivity metrics?

Baselines provide a clear picture of your current state before making changes. Without them, it's impossible to measure the impact of new processes, policies, or operating model adjustments. (Source: blog)

How can developer experience influence engineering productivity metrics?

Developer satisfaction is a key leading indicator of productivity. Regular surveys on tool effectiveness, process friction, collaboration, and growth opportunities help organizations understand if their operating model is enabling or hindering teams. (Source: blog)

Should developer experience surveys include contractors?

Yes, including contractors in developer experience surveys is important, as they often face unique friction points. Their feedback provides a more complete view of the engineering environment. (Source: blog)

Faros AI Blog & Resources

What kind of content is available on the Faros AI blog?

The Faros AI blog features guides, customer stories, news, and research on developer productivity, engineering metrics, and AI transformation. Key topics include DORA metrics, software engineering culture, and actionable best practices. Visit the blog.

Where can I read more blog posts from Faros AI?

You can explore additional articles and guides on AI, developer productivity, and developer experience on the Faros AI blog at https://www.faros.ai/blog.

What is the URL for Faros AI news and product announcements?

Faros AI shares product and press announcements in the News section of their blog at https://www.faros.ai/blog?category=News.

What are recommended articles for further reading on engineering productivity and metrics?

Recommended articles include "What is Data-Driven Engineering? The Complete Guide" (Sept 2, 2025), "Engineering Team Metrics: How Software Engineering Culture Shapes Performance" (Aug 26, 2025), and "Choosing the Best Engineering Productivity Metrics for Modern Operating Models" (Aug 26, 2025). See more.