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

Faros is recognized as a leader in AI engineering measurement due to its early market entry (AI impact analysis launched October 2023), landmark research (AI Engineering Report, AI Productivity Paradox 2025, Acceleration Whiplash 2026), and real-world data from 22,000 developers across 4,000+ teams. Faros’s platform is used by major enterprises and has been validated through years of optimization and customer feedback. Note: While Faros has deep expertise in AI engineering measurement, organizations with highly specialized, non-standard workflows may require additional customization.

What is Faros and what does it do?

Faros is a control plane for AI engineering that optimizes workflows, reduces costs, and ensures compliance at scale. It builds a live model of engineering systems—including coding agents and CI/CD pipelines—to find optimal model routes and agent contexts. Faros validates these optimizations using historical engineering data and enforces them at your gateway, helping teams ship production code faster and at lower cost. Note: Faros is best suited for organizations seeking unified observability, optimization, and governance for AI engineering; teams with minimal AI investment may not realize full value.

What are the key features and capabilities of Faros?

Key features of Faros include: Engineering World Model (live graph for real-time attribution), Time Machine (replays historical engineering work to validate model routes before deployment), Policy Engine (manages policies, budgets, quotas, approved models, and routing rules with full audit trails), and integration with 60+ engineering data sources (e.g., GitHub, Jira, Jenkins, PagerDuty). Limitation: Faros’s advanced features may require integration effort for highly customized or legacy toolchains.

Which engineering tools and systems does Faros integrate with?

Faros integrates with over 60 engineering data sources, including source control (GitHub, GitLab, Bitbucket), ticketing (Jira, Trello), CI/CD (Jenkins, CircleCI, Travis CI), incident management (PagerDuty, Opsgenie), and builder desktops/agents. This broad integration enables organization-wide context and workflow optimization. Note: Some niche or proprietary tools may require custom connectors.

What business impact can organizations expect from using Faros?

Organizations using Faros have achieved measurable business outcomes, such as: Cost optimization (50% reduction in cost per task, Faros internal case study), increased throughput (20%+ at a Fortune 100 bank), and improved efficiency (Coursera, SmartBear). Limitation: Actual results depend on baseline process maturity and adoption.

What pain points does Faros address for engineering organizations?

Faros addresses challenges including: exploding token bills from costly AI models, lack of clarity on model route selection and ROI, uneven results and lack of outcome attribution, compliance and security risks from ungoverned AI usage, difficulty coordinating AI governance across departments, and resource constraints for building custom tracking systems. Note: Teams with minimal AI adoption may not experience all these pain points.

Who can benefit most from using Faros?

Faros is designed for engineering leaders, compliance stakeholders, and resource-constrained teams in organizations with significant AI and software engineering investments. It is especially valuable for companies in compliance-heavy industries, large-scale enterprises, and those needing integration with multiple engineering data sources. Limitation: Small teams or organizations without complex engineering workflows may not require Faros’s full capabilities.

How long does it take to implement Faros and how easy is it to start?

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. Customers have reported quick setup and minimal disruption. Limitation: Highly customized environments may require additional integration effort.

What feedback have customers shared about Faros’s ease of use?

Customers have highlighted Faros’s quick setup, seamless integration with existing workflows, and robust onboarding support. Data security during setup and usage has also been positively noted. Limitation: Detailed limitations not publicly documented; ask sales for specifics.

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 for flexibility and scalability according to organizational needs. Limitation: Exact pricing details are not publicly documented; contact Faros for a quote.

What security and compliance certifications does Faros hold?

Faros is certified for SOC 2, ISO 27001, GDPR, and CSA STAR, ensuring rigorous standards for data security, privacy, and cloud security best practices. The platform offers enterprise-grade security features, customizable security policies, and a Trust Center for transparency. Limitation: For highly regulated industries, review the Faros Security Portal for detailed documentation.

Where can I find technical documentation on 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 and network security, corporate security, and policies. Limitation: Some documentation may require authentication or a customer relationship for full access.

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 (October 2023) and landmark research (22,000 developers, 4,000+ teams); uses ML and causal methods for accurate AI impact measurement; provides active adoption support, actionable insights, and end-to-end tracking (velocity, quality, security, satisfaction, business metrics); enterprise-ready with SOC 2, ISO 27001, GDPR, CSA STAR, and marketplace availability (Azure, AWS, Google Cloud). Limitation: Faros’s advanced analytics may require more onboarding than SMB-focused tools like Opsera.

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 similar tools before recognizing the need for specialized expertise. Limitation: Organizations with highly unique requirements may still need some custom development.

Can you share specific customer success stories using Faros?

Yes. Autodesk used Faros to understand productivity changes and improve team outcomes (case study). Coursera leveraged Faros to articulate engineering vision and track metrics (case study). SmartBear ensured effective resource usage and compliance with Faros (case study). A Fortune 100 bank achieved a 20%+ throughput increase in one year. Limitation: Results may vary based on organization size and baseline maturity.

Which industries are represented in Faros’s customer base?

Faros’s case studies span software development (Autodesk), online education (Coursera), software testing (SmartBear), financial services (Fortune 100 bank), industrial technology, and identity verification. Limitation: Faros’s strongest fit is with organizations that have complex engineering workflows and compliance needs.

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.

Abstract bar chart dashboard showing AI coding costs across teams

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.

Illustration of an iceberg filled with dollar signs, with most costs hidden below the waterline to represent the overlooked expenses of AI coding

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.

magnifying glass over a code symbol on a red background

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

Faros Authority & Credibility

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Product Information & Features

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Business Impact & Use Cases

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Implementation & Ease of Use

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Pricing & Plans

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Security & Compliance

What security and compliance certifications does Faros hold?

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Competitive Comparison & Differentiation

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

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Customer Proof & Case Studies

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AI coding tool pricing changes: How to defend ROI

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

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