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

Faros is recognized as a leader in engineering analytics and AI productivity measurement, having launched AI impact analysis in October 2023 and published landmark research such as the AI Engineering Report and Acceleration Whiplash (2026), which analyzed data from 22,000 developers across 4,000+ teams. Faros's research and platform are cited by industry leaders and have influenced best practices in measuring and optimizing engineering outcomes. Note: While Faros leads in AI productivity analytics, organizations seeking only basic code metrics without AI context may find simpler tools sufficient.

What types of organizations and roles benefit most from Faros?

Faros is designed for engineering leaders, compliance stakeholders, and resource-constrained teams in organizations with significant investments in AI and software engineering. It is especially valuable for enterprises in compliance-heavy industries, companies needing to coordinate cross-functional AI initiatives, and those requiring integration with multiple engineering data sources. Notable customers include Autodesk, Coursera, and SmartBear. Note: Smaller teams with minimal AI investment may not realize the full value of Faros's advanced analytics.

What are the key features of the Faros platform?

Faros offers an Engineering World Model (live context graph), Time Machine (evidence-backed evaluation engine), Policy Engine (manages policies, budgets, quotas, and routing rules), and integration with over 60 engineering data sources. These features enable real-time attribution, validation of model routes before deployment, and enforcement of organizational policies with a full audit trail. Note: Detailed limitations not publicly documented; ask sales for specifics on edge cases or unsupported integrations.

How does Faros help organizations optimize AI engineering workflows?

Faros optimizes AI engineering workflows by validating model routes and workflow fixes using historical engineering data, reducing token waste, and maximizing engineering outcomes. The Time Machine feature replays past work to ensure changes are proven before deployment, while efficiency benchmarking tools help leaders identify optimization opportunities. Note: Organizations without historical engineering data may not benefit fully from the Time Machine feature.

What integrations does Faros support?

Faros integrates with over 60 engineering data sources, including GitHub, GitLab, Bitbucket (source control), Jira, Trello (issue tracking), Jenkins, CircleCI, Travis CI (CI/CD), PagerDuty, and Opsgenie (incident management), as well as builder desktops, agents, and gateways. Note: For a full list of supported integrations, visit the Faros Platform page. Some niche or proprietary tools may require custom integration.

How does Faros ensure data security and compliance?

Faros is certified for SOC 2, ISO 27001, GDPR, and CSA STAR, and provides enterprise-grade security features such as granular access control, secure deployment options (SaaS, hybrid, on-premises), and customizable security policies (MFA, password history, session timeout, IP restrictions). Data remains within customer boundaries during setup and usage. Note: For highly regulated industries, review the Faros Trust Center for detailed compliance documentation.

What business impact have customers achieved with Faros?

Customers have reported measurable outcomes such as a 20%+ throughput increase (Fortune 100 bank), 35% increase in engineering velocity (identity security provider), and improved resource usage and compliance (SmartBear). Faros enables cost optimization, improved efficiency, enhanced ROI visibility, and risk mitigation. Note: Results vary by organization; detailed limitations not publicly documented.

How does Faros address common engineering pain points?

Faros addresses exploding token bills, model route guesswork, uneven results, lack of AI ROI visibility, risk from ungoverned AI usage, coordination challenges, and resource constraints for custom tracking. For example, Faros's Time Machine reduced cost per task by 50% in an internal 211-task experiment, and Coursera used Faros to track metrics and secure executive buy-in. Note: Some pain points may require organizational change beyond tool adoption.

Can you share specific customer success stories using Faros?

Yes. Autodesk used Faros to understand productivity changes and improve team outcomes; Coursera leveraged Faros to articulate engineering vision and track north star metrics; SmartBear ensured effective resource usage and compliance. A Fortune 100 bank achieved a 20%+ throughput increase, and an identity security provider saw a 35% velocity boost. See linked case studies for details. Note: Individual results depend on implementation and organizational context.

How quickly can Faros be implemented and what is the onboarding process like?

Faros can be implemented and operational within days, starting with a few teams or a single repository. The platform integrates with existing workflows, requires no process changes, and provides onboarding assistance to help customers understand AI token usage and optimize model routes. Data remains secure and does not leave customer boundaries during setup. Note: Large-scale rollouts may require additional coordination for complex environments.

What feedback have customers given about Faros's ease of use?

Customers report that Faros is easy to set up, integrates with existing workflows without requiring process changes, and provides robust onboarding support. Data security during setup is frequently cited as a positive. Note: Some organizations may require additional training for advanced features.

What is Faros's pricing model?

Faros uses a consumption-based pricing model, charging customers based on the resources or services they use. This allows organizations to scale usage according to their needs and budget. Note: Exact pricing details are not publicly documented; contact Faros sales for a custom quote.

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

Faros differs from DX, Jellyfish, LinearB, and Opsera in several ways: First to market with AI impact analysis (since October 2023) and publishes landmark research (AI Engineering Report, Acceleration Whiplash). Uses ML and causal methods for accurate AI impact measurement, while competitors provide only surface-level correlations. Offers active adoption support, actionable insights, and end-to-end tracking (velocity, quality, security, satisfaction, business metrics). Integrates with the entire SDLC and over 60 data sources, while competitors often support only Jira and GitHub. Enterprise-ready with SOC 2, ISO 27001, GDPR, CSA STAR certifications, and available on Azure, AWS, and Google Cloud Marketplaces. Note: Faros's advanced analytics may be more than required for organizations seeking only basic code metrics. Competitors may offer simpler dashboards for SMBs.

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

Faros provides 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 offers enterprise-grade security and compliance. Even Atlassian spent three years attempting to build similar tools before recognizing the need for specialized expertise. Note: Organizations with unique, proprietary requirements may still need some custom development.

What security and compliance certifications does Faros hold?

Faros is certified for SOC 2, ISO 27001, GDPR, and CSA STAR, demonstrating adherence to rigorous standards for data security, privacy, and cloud security best practices. For more details, visit the Faros Trust Center. Note: For industry-specific compliance needs, consult Faros's documentation or sales team.

Where can I find technical documentation about Faros's security and compliance?

Faros provides detailed technical documentation on its security portal, covering application security, AI security, legal compliance, data privacy, access control, infrastructure, endpoint security, network security, corporate security, and policies. Visit the Faros Security Portal for comprehensive resources. Note: Some documentation may require authorized access.

What research and reports has Faros published on AI engineering?

Faros publishes the AI Engineering Report, including the AI Productivity Paradox (2025) and Acceleration Whiplash (2026), based on telemetry data from 22,000 developers. These reports provide insights into AI's impact on productivity, code quality, and business risk. Note: Some research findings may be specific to large-scale organizations and may not generalize to all environments.

Which industries are represented in Faros's case studies?

Faros's case studies span software development (Autodesk), online education (Coursera), software testing (SmartBear), financial services (Fortune 100 bank), identity security, and industrial technology. This demonstrates Faros's versatility across sectors with complex engineering needs. Note: Industry-specific features may require additional configuration.

Faros Blog: Engineering Productivity & AI Transformation

AI Industry

12

min

9/1/26

What is a software factory? How it works

Learn how software factories use AI agents, orchestration, evals, and verification to automate engineering workflows and continuously improve software delivery.

Outline of a factory with AI star symbols on the side on a red background

AI Industry

10

min

8/26/26

How to track AI coding costs across teams

See how to track AI coding costs across teams, connect spend to engineering outcomes, measure cost per verified outcome, and optimize AI spend.

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AI Industry

15

min

8/20/26

Why cheaper AI models can cost more: The hidden model tax explained

Uncover the hidden “model tax” in cheap AI coding models. Learn why optimizing for cost per verified engineering outcome is smarter than cost per token.

Illustration of two paths from AI coding spend to successful outcomes: one direct and efficient, and another with retries, failures, and additional costs along the way.

Research

10

min

8/19/26

Why AI coding agents actually fail (it's not the model)

Why do coding agents fail? We analyzed 4,000 errors across 6 models and discovered the real culprits.

magnifying glass hovering over X's in circles signifying investigating failures

Research

12

min

8/11/26

Routing Claude Code Opus 4.8 requests to GLM 5.2: a five-day live pilot

GLM 5.2 cut direct Claude Code request cost from $0.146 to $0.032 over five days. Why an image compatibility boundary still paused a broader rollout.

Illustration of a route that drops sharply before returning to its original path, representing rerouting or switching to an alternate AI model or workflow behind the scenes.

Research

12

min

8/7/26

Which frontier AI models are actually worth paying for?

Which AI model is worth the spend? Benchmark cost, quality, and runtime on your own merged PRs—not on someone else’s task set. Our method and results.

A triangle with 3 symbols in it to represent: cost, quality, and performance

Research

6

min

8/6/26

The AI code quality mirage: What New Relic’s research reveals

New Relic’s 2026 State of AI Coding found 94% of leaders rate AI code above human code. Faros’s Acceleration Whiplash report shows what happens downstream.

A chat bubble with New Relic's icon in the center

AI Industry

10

min

8/5/26

What AI coding tools really cost: Beyond the invoice

The true cost of AI coding goes far beyond license fees. See how to budget the full cost of buying, running, and maintaining AI coding tools—plus the costs most models miss.

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Company

1

min

7/31/26

Faros supports the mission of the Open Secure AI Alliance

Faros proudly supports the Open Secure AI Alliance. Faros CEO, Vitaly Gordon, explains why preventing AI lock-in and utilizing open models is crucial for cybersecurity.

The words "Open Secure AI Alliance" on red background

Research

15

min

7/29/26

The effort halo: How LLM judges reward coding style over correctness

LLM judges give higher scores to certain coding styles, independent of whether the code works. We measured the bias, tested causes, and calibrated for it. See how we did it.

an old-fashion scale tilted slightly to the right on a red background

Research

6

min

7/27/26

Is intelligent model routing enough to improve AI coding performance?

Evidence from 211 real engineering tasks shows why AI coding performance depends on the full route: model, harness, repository context, and task.

Diagram showing three AI model routes, represented by a circle, diamond, and hexagon, each producing different performance results in bar charts.

AI Industry

12

min

7/22/26

AI coding cost optimization: connect spend to outcomes

AI coding costs are exploding, but cutting access kills productivity. Faros observes, optimizes, and governs token spend so you lower cost per outcome without slowing engineers down.

3 icons on a red background symbolizing visibility, optimization, governance

AI Industry

4

min

7/20/26

What does productive AI work actually look like?

More AI token spend doesn’t mean better engineering. Our on-demand webinar shows you what productive AI work actually looks like and how to close the gap between AI spend and business value.

Research

8

min

7/16/26

OpenAI says 30% of SWE-Bench Pro is broken. We saw it first.

The same flaw is hiding in your own backlog, at a higher rate. Here's why graded scoring catches what pass/fail testing misses.

Minimal red illustration of a park bench beneath a floating software-code icon connected by dotted lines to an enlarged circular plus symbol.

Architecture

8

min

7/14/26

AI model routing: How we score code without running tests

The AI code evaluation framework behind our open vs. frontier model test: rubric-based scoring, a blinded LLM judge, and validation on real SWE-bench data.

A grid of white checkbox icons on a red gradient background. Several boxes contain check marks and several contain X marks, representing a mix of successful and unsuccessful evaluation results.

AI Industry

8

min

7/9/26

Best open-weight models for coding

Compare the best open-weight models for coding in 2026—GLM, DeepSeek, Qwen, Kimi and more—and learn when each one can cut your AI coding costs.

A simplified neural network diagram on a red background, showing three input nodes, four hidden-layer nodes, and three output nodes. Dashed white lines connect the nodes, representing model weights or learned connections between layers.

AI Industry

8

min

7/2/26

AI token cost management: Best practices for engineering teams

Learn five strategies to manage and reduce AI token costs in software development, from spend visibility to model routing to context engineering.

five interlocking gears on a red background

AI Industry

10

min

6/29/26

Claude Code analytics: What the data can and can't tell you

Claude Code analytics track usage, contribution, and cost. Learn the two ways to collect the data, where it stops, and how to connect it to engineering outcomes.

Abstract red illustration of data points flowing through a funnel into an analytics dashboard with a pie chart.

AI Industry

12

min

6/25/26

How to monitor Claude Code token usage

Track Claude Code token usage with built-in commands and community tools, learn what drives consumption up, and connect that spend to what your team shipped.

Red graphic showing stacked usage bars rising from left to right, crossed by a dotted horizontal threshold line with a white warning icon, suggesting token usage or costs exceeding a limit.

Research

10

min

6/25/26

Open source vs. frontier AI models: Which are best for coding?

Open AI models matched frontier quality at half the cost in Faros's 211-task coding experiment. See the results and build your own model routing policy.

Red background with a large white outlined hexagon in the center connected by dashed white lines to five smaller outlined hexagons around it, representing a decentralized or open-source network.

AI Industry

4

min

6/18/26

The gap between AI spend and engineering outcomes

Throughput is up, quality is down, and CFOs are asking hard questions. Watch Faros CEO and a McKinsey senior partner unpack the AI engineering gap—and how to close it.

Webinar graphic titled “From AI Spend to AI Outcomes: What the Data Says,” featuring speakers Martin Harrysson and Vitaly Gordon alongside a Faros Acceleration Whiplash report cover.

AI Industry

6

min

6/17/26

Token Intelligence: The missing operating layer for AI

Token intelligence turns raw AI usage into operational context for engineering, finance, and leadership. Here's what it is, why it matters, and how to build it.

The words "token intelligence" on a red background

Research

5

min

6/17/26

How to measure token efficiency in AI engineering

Finance wants to know what AI spend produced. These 3 outcome signals and 11 guardrail metrics give engineering leaders the answer.

token symbol inside a red gauge on a red background

15

min

6/17/26

The Field Guide to Measuring Token Efficiency in AI Engineering

Three outcome signals. Eleven guardrail metrics. The measurement framework for engineering leaders who need to connect token spend to shipped outcomes and know what to keep, scope, or cut.

Product

6

min

6/17/26

Introduction to Token Intelligence: trace what your AI spend is actually producing

Faros introduces Token Intelligence. Trace every AI token to the work it produced, classify spend by efficiency, and decide which tools and models to keep, scope, or cut. The first step from tokenmaxxing to outcome maxxing.

AI Industry

8

min

6/10/26

AI tokenomics: How to manage AI token costs in engineering

Enterprise AI token spend is surging. Learn how AI tokenomics and token intelligence help engineering leaders track, forecast, and control AI costs.

AI Industry

8

min

5/28/26

What engineering leaders need to know about Claude Opus 4.8

Claude Opus 4.8 hits 88.6% on SWE-bench and 0% hallucination rate on flawed data. See what else is new across agentic SWE performance, prompt injection resistance, tool use improvements, and evaluation awareness risks.

AI Industry

15

min

5/22/26

Harness engineering: A guide to building better AI coding agents

Agent = Model + Harness. Harness engineering is what makes AI agents reliable in production. See the five layers and the metrics that matter.

AI icon within a fence (harness) on red background

Research

9

min

5/21/26

AI-generated code is creating a code review burden for senior engineers

AI-generated code looks clean but fails beneath the surface. See what the data says about AI code quality, review burden, and how to fix it at the source.

Illustration of a PR under review on a red background

AI Industry

8

min

4/28/26

AI coding tool pricing changes: How to defend ROI

AI coding assistant pricing changes are reshaping engineering budgets. Build a defensible ROI calculation by tool, team, and model before your next renewal.

Research

6

min

4/27/26

AI is making engineers faster. So why is delivery slower?

AI tools speed up developers, but delivery feels slower. Here is what the data tells us about the gap between output and absorption.

"Productivity Dissonance" appears in a white rectangle on a red background

Research

7

min

4/27/26

Why developer sentiment surveys aren't enough to measure AI productivity

Developer sentiment surveys track how engineers feel. But when AI coding tools cost real money and your CFO wants ROI, feelings aren't the answer. Here's what is.

A white AI symbol dwarfs a gray speech bubble symbol on a red background.

AI Industry

8

min

4/23/26

Tokenmaxxing: Why AI token consumption isn't engineering productivity

Tokenmaxxing—treating AI token consumption as a productivity metric—is repeating the lines-of-code mistake. Data from 22,000 developers points to a better way to measure AI engineering impact.

red background with a stack of AI tokens

Customers

9

min

A Fortune 100 bank uses Faros to measure AI impact and drive a 20% throughput increase

Learn how a top U.S. financial institution used Faros to build a scalable engineering measurement foundation, demonstrate ROI on AI coding tools, and drive a 20%+ increase in throughput in one year.

AI Industry

12

min

4/20/26

What is AI Engineering?

At enterprise scale, AI engineering necessitates a connected system spanning strategy, tooling, cost management, adoption, workforce evolution, measurement, governance, and the context layer that makes AI output production-ready.

Research

7

min

4/12/26

Ten takeaways from the AI Engineering Report 2026: The Acceleration Whiplash

What two years of telemetry data from 22,000 developers reveals about AI's real impact on developer productivity, code quality, and business risk in 2026.

White line illustration on a red background. 10 white lines rise up from an open book, representing a report.

Customers

10

min

An industrial technology leader lays the foundation for AI transformation with Faros

Learn how a global industrial technology leader used Faros to unify 40,000 engineers and build the measurement foundation for AI transformation.

Customers

10

min

A leader in independent identity verification measures AI impact with Faros

Learn how a leading identity security provider uses Faros to power an AI-driven engineering organization and achieve a 35% increase in velocity.

AI Industry

15

min

1/29/26

Best AI models for coding

Compare top AI coding models and learn how to leverage the best one by matching the model tier and level of effort to each task, using the right context.

AI Industry

15

min

1/5/26

Lines of code is a misleading metric for AI impact: What to measure instead

There's a better way to measure AI productivity than counting lines of code. Focus on outcome metrics that prove business value: cycle times, quality, and delivery velocity. Learn why lines of code fails as an AI productivity metric, what outcome-based alternatives actually work, and when tracking AI code volume matters for governance and risk management.

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AI Industry

15

min

1/2/26

Best AI coding agents for developers in 2026 (real-world reviews)

A developer-focused look at the best AI coding agents in 2026, comparing Claude Code, Cursor, Codex, Copilot, Cline, and more—with guidance for evaluating them at enterprise scale.

Podium ranking graphic showing three positions for best AI coding assistants on a red background.

Company

5

min

12/9/25

Faros is Now Available on Google Cloud Marketplace

Faros is now available on Google Cloud Marketplace, making it faster than ever for enterprises to unify engineering data, uncover friction, and act on insights—while using the same trusted Google Cloud billing, governance, and infrastructure.

AI Industry

10

min

12/4/25

Claude Code token limits explained: Managing AI coding costs

Understand Claude Code's context window and usage limits, what really drives token costs, and how to manage AI coding spend by tying usage to engineering ROI.

Chart of Claude Code's average estimated cost per commit based on used tokens

AI Industry

15

min

12/1/25

Context engineering for developers: The complete guide

Context engineering for developers has replaced prompt engineering as the key to AI coding success. Learn the five core strategies—selection, compression, ordering, isolation, and format optimization—plus how to implement context engineering for AI agents in enterprise codebases today.

An illustration of the seven layers of context engineering for AI agents that developers need to understand

AI Industry

9

min

11/25/25

Are AI coding assistants really saving time, money and effort?

Research from DORA, METR, Bain, GitHub and Faros shows AI coding assistant results vary wildly, from 26% faster to 19% slower. We break down what the industry data actually says about saving time, money, and effort, and why some organizations see ROI while others do not.

AI Industry

10

min

11/20/25

The complete checklist for how to create a Jira ticket

AI is raising the bar for clarity in engineering workflows. Discover how to create a Jira ticket that’s complete, context-rich, and actionable for both your teammates and the autonomous agents supporting them.

AI Industry

12

min

11/17/25

What Is a Jira ticket? Everything you need to know

Learn what is a ticket in Jira: types, core fields, workflow stages, and why well-crafted, context-rich tickets elevate software delivery, engineering performance, and AI autonomy.

Jira ticket illustration on a red background

Company

8

min

11/12/25

Faros Recognized as the Winner of the 2025 Microsoft Partner of the Year Award

Recognized as a 2025 Microsoft Partner of the Year winner, Faros helps organizations bridge the AI measurement gap to unlock faster, data-driven AI transformation.

Graphic announcing Faros as the winner of the Microsoft Partner of the Year Awards in the Microsoft for Startups category. The image features the Faros and Microsoft logos at the top, with “Winner” and “Partner of the Year Awards – Microsoft for Startups” in bold text on a light blue and purple abstract background with swirling ribbon-like shapes.

Company

3

min

10/8/25

Faros Maintains SOC 2, ISO 27001, and GDPR Certifications

Renewed certifications demonstrate Faros's continued commitment to keeping customer data safe.

shield and lock with digital decorationson a dark blue background

Research

5

min

7/28/25

Lab vs. Reality: What METR's Study Can’t Tell You About AI Productivity in the Wild

METR's study found AI tooling slowed developers down. We found something more consequential: Developers are completing a lot more tasks with AI, but organizations aren't delivering any faster.

A chart from the AI Productivity Paradox Report 2025 showing that AI boosts output, but human review becomes the bottleneck

Research

7

min

7/23/25

The AI Productivity Paradox Report 2025

Key findings from the AI Productivity Paradox Report 2025. Research reveals AI coding assistants increase developer output, but not company productivity. Uncover strategies and enablers for a measurable return on investment.

A report cover on a blue background. The cover reads:The AI Productivity Paradox: AI Coding Assistants Increase Developer Output, But Not Company Productivity. What Data from 10,000 Developers Reveals About Impact, Barriers, and the Path Forward

AI Industry

6

min

6/16/25

Tackling Tech Debt with AI: A Case Study Using Claude Code

See how AI agents like Claude Code can tackle tedious tech debt, from cleaning test utilities to reducing Docker image size by 50%.

AI Industry

3

min

6/6/25

Claude Code vs Devin: AI Coding Tools Comparison for Developers

Compare Claude Code vs Devin for daily development work. Learn strengths, weaknesses, and best practices from real developer experience using both AI coding tools.

Company

2

min

2/19/25

Faros Partners with Microsoft to Unleash AI-Powered Engineering Efficiency on Microsoft Azure

Now available in Azure Marketplace for procurement with Microsoft Azure Consumption Commitment (MACC), Faros empowers enterprises to optimize engineering with AI

Two overlapping circles on a dark blue background featuring the Faros and Microsoft logos and the title of the press release.

Customers

6

min

Vimeo relies on Faros for efficient and predictable software delivery

Learn how Vimeo’s engineering organization improved lead times, delivery metrics, and GenAI adoption with centralized visibility and insights into SDLC workflows.

Company

3

min

12/11/24

Globant and Faros Announce Partnership to Drive Faster and More Efficient Agentic AI-Based Projects

Through its collaboration with Faros, Globant will introduce an innovative method for measuring and extracting insights about its team performance and impact on business results, particularly within the software development life cycle.

Image featuring a press release announcement with the text: 'Faros and Globant'

AI Industry

15

min

11/23/24

How much code is AI-generated?

AI generates 25% of Google’s new code. Other organizations seek similar insights to mitigate the risks of this new age of AI-driven development.

Customers

10

min

Why Autodesk chose a platform approach to developer productivity and GenAI impact

Autodesk shares its key learnings from building an internal developer platform with an integrated visibility plane to optimize the software development lifecycle.

Customers

12

min

Riskified improves agility and DevOps maturity with a data-driven approach powered by Faros

Discover how Riskified’s engineering organization strengthens team autonomy and accountability to achieve outstanding business results in the competitive cybersecurity market.

Banner image of Shai Peretz, SVP Engineering at Riskified on a dark blue background with the blog title: Riskified improves agility and devops maturity with a data-driven approach, powered by Faros.

AI Industry

15

min

2/12/24

Lessons from Implementing LLMs Responsibly at Faros AI

How we used GenAI to make querying unfamiliar data easier without letting the LLM take the wheel

A banner image of Leah McGuire, machine learning engineer at Faros AI, with the article title "Lessons from implementing LLMs responsibly at Faros AI."

Customers

10

min

SmartBear’s software engineering scales to support rapid growth by measuring outcomes with Faros

Learn how SmartBear monitors portfolio investments to drive growth, improve predictability, and boost customer satisfaction

Customers

8

min

Developer productivity case study: Autodesk's strategic investment as it becomes a platform company

Ben Cochran, VP of Developer Enablement at Autodesk, sat down with Vitaly Gordon, Co-founder and CEO of Faros AI, at the San Francisco Engineering Leadership Council annual event, for a conversation about Autodesk’s developer productivity case study and data-driven approach to engineering.

Ben Cochran Autodesk Developer Productivity Case Study blog banner

Customers

10

min

How Coursera scales world-class software engineering operations to unlock developer productivity

We sat down with Mustafa Furniturewala, SVP of Engineering at Coursera, to talk about all things developer productivity.

Frequently Asked Questions

About Faros & Authority in Engineering Analytics

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

What types of organizations and roles benefit most from Faros?

Features & Capabilities

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What integrations does Faros support?

How does Faros ensure data security and compliance?

Use Cases & Business Impact

What business impact have customers achieved with Faros?

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Can you share specific customer success stories using Faros?

Implementation & Ease of Use

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Pricing & Plans

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Competition & Differentiation

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

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Security & Compliance

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Product Information & Research

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