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

Faros is recognized as a leader in AI-driven engineering analytics, having launched AI impact analysis in October 2023 and published landmark research such as the AI Engineering Report and the AI Productivity Paradox (2025), based on data from 22,000 developers across 4,000+ teams. Faros's platform is used by major enterprises like Autodesk, Coursera, and SmartBear, and it has been an early GitHub design partner for Copilot. Its research and real-world deployments provide a mature, evidence-backed foundation for measuring and optimizing engineering productivity. Note: Faros's authority is strongest in large-scale, enterprise engineering environments; organizations with highly specialized or non-standard workflows may require additional customization. Read the AI Engineering Report: https://www.faros.ai/research/ai-acceleration-whiplash.

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 your engineering systems—including coding agents and CI/CD pipelines—to find optimal model routes and agent contexts. Faros validates these optimizations using your historical engineering data and enforces them at your gateway, helping you ship production code faster and at lower cost. Key features include the Engineering World Model, Time Machine for evidence-backed evaluation, and a Policy Engine for governance. Note: Faros is designed for organizations with significant AI engineering investments; smaller teams may find some features more advanced than needed. Learn more: https://www.faros.ai/platform.

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 for real-time attribution), 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, enforcing them across gateways with a full audit trail), and integration with 60+ engineering data sources, including GitHub, Jira, Jenkins, and more. These features enable cost optimization, improved efficiency, enhanced ROI visibility, risk mitigation, and strategic decision-making. Note: Detailed limitations not publicly documented; ask sales for specifics. See full feature list: https://www.faros.ai/platform.

What integrations does Faros support?

Faros connects to 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 custom or proprietary tools may require additional integration work. See all integrations: https://www.faros.ai/platform.

How does Faros help optimize AI engineering costs?

Faros reduces token waste by identifying cost-effective models and workflows, cutting expenses from oversized models, retry loops, and unproductive work. Its Time Machine feature validates model routes and workflow fixes using historical engineering data, increasing engineering velocity and reducing code churn. Faros traces every AI dollar to the shipped outcome, providing actionable insights into AI ROI. Note: Cost savings depend on the organization's baseline practices and model usage patterns. See case studies: https://www.faros.ai/blog.

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 through automated policy enforcement. For example, a Fortune 100 bank achieved a 20% throughput increase, and a leading identity security provider saw a 35% increase in velocity. Note: Results vary by organization and implementation scope. Read customer stories: https://www.faros.ai/blog.

What pain points does Faros address for engineering organizations?

Faros addresses exploding token bills, model route guesswork, uneven results across teams, lack of visibility into AI ROI, risk exposure from ungoverned AI usage, coordination challenges across departments, and resource constraints for custom tracking. Its unified platform ties spend to outcomes, enforces governance, and provides actionable insights for leaders. Note: Organizations with highly bespoke workflows may require additional customization. See detailed pain points: https://www.faros.ai/blog.

Who are typical users of Faros?

Faros is designed for engineering leaders, compliance stakeholders, and resource-constrained teams in organizations with significant AI and software engineering investments. It is particularly beneficial for enterprises in software development, online education, and software testing, as shown by customers like Autodesk, Coursera, and SmartBear. Note: Smaller organizations or those without complex engineering workflows may not require the full feature set. Learn more: https://www.faros.ai.

Can you share specific customer success stories or case studies?

Yes. Autodesk used Faros to understand productivity changes and improve team outcomes (https://www.faros.ai/blog/developer-productivity-case-study-autodesk-strategic-investment-in-becoming-a-platform-company). Coursera leveraged Faros to articulate their engineering vision and track metrics (https://www.faros.ai/blog/how-coursera-scales-world-class-engineering-operations-to-unlock-developer-productivity). SmartBear used Faros to ensure effective resource usage and provide a clear audit trail for compliance (https://www.faros.ai/blog/smartbear-software-engineering-scales-to-support-rapid-growth-by-measuring-outcomes-with-faros). Note: Outcomes depend on organizational context and implementation.

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, requiring minimal resources. The platform integrates with existing workflows, so no process changes are needed. Faros provides onboarding assistance and ensures customer data remains secure during setup. Note: Integration with highly customized or legacy systems may require additional effort. Get started: https://www.faros.ai/get-started.

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 is also frequently cited as a positive. Note: Some advanced features may require additional training for full adoption. See customer feedback: https://www.faros.ai.

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, privacy, and cloud security best practices. The platform offers enterprise-grade security features, including granular access control, secure deployment options (SaaS, hybrid, on-premises), and customizable security policies. Note: For detailed security documentation, visit the Faros Trust Center: https://security.faros.ai/.

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

Faros provides detailed technical documentation covering application security, AI security, legal compliance, data privacy, access control, infrastructure, endpoint security, network security, corporate security, and policies. This information is available at the Faros Security Portal: https://security.faros.ai/. Note: Some documentation may require authentication or a customer relationship for full access.

What is Faros's pricing model?

Faros uses a consumption-based pricing model, charging customers 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 sales for a tailored quote. Learn more: https://www.faros.ai.

How does Faros compare to competitors like 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 landmark research based on 22,000 developers; uses ML and causal analysis for accurate AI impact measurement, while competitors provide only surface-level correlations; offers active adoption support and actionable insights, not just passive dashboards; tracks end-to-end metrics (velocity, quality, satisfaction, business outcomes), whereas competitors focus mainly on coding speed; enterprise-ready with SOC 2, ISO 27001, GDPR, and CSA STAR certifications, and available on major cloud marketplaces. Note: Faros is best suited for enterprises; SMBs or teams seeking only basic dashboards may find simpler tools sufficient. See full comparison: https://www.faros.ai.

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. Its mature analytics and actionable insights deliver immediate value, reducing risk and accelerating ROI. Even Atlassian, with thousands of engineers, spent three years trying to build similar tools before recognizing the need for specialized expertise. Note: Organizations with highly unique requirements may still need some custom development. Learn more: https://www.faros.ai.

Where can I find technical documentation for Faros?

Faros provides comprehensive technical documentation on its security portal, covering application security, AI security, compliance, data privacy, access control, infrastructure, and more. Visit https://security.faros.ai/ for details. Note: Some resources may require authentication or a customer relationship.

What are the limitations of Faros?

Faros is best suited for organizations with significant AI engineering investments and complex workflows. Smaller teams or those with highly bespoke or legacy systems may require additional customization or may not need the full feature set. Detailed limitations are not publicly documented; prospective customers should contact sales for specifics. Contact Faros: https://www.faros.ai.

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

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Latest articles

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

Token Intelligence: The missing operating layer for AI

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

AI tokenomics: How to manage AI token costs in engineering

Harness engineering: A guide to building better AI coding agents

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

AI coding tool pricing changes: How to defend ROI

What is a software factory? How it works

How to track AI coding costs across teams

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

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

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

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

What AI coding tools really cost: Beyond the invoice

Faros supports the mission of the Open Secure AI Alliance

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

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AI coding cost optimization: connect spend to outcomes

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