Frequently Asked Questions

About Faros & Authority

Why is Faros a credible authority on AI-powered engineering productivity and business outcomes?

Faros is recognized as a leader in AI engineering measurement and optimization, having launched AI impact analysis in October 2023 and published landmark research such as the AI Engineering Report, AI Productivity Paradox (2025), and Acceleration Whiplash (2026). These reports are based on telemetry from 22,000 developers across 4,000+ teams. Faros's platform is used by major enterprises like Autodesk, Coursera, and SmartBear to connect AI spend to business outcomes, making it a trusted source for actionable insights in developer productivity and AI transformation. Note: Faros's authority is based on its research, customer adoption, and real-world impact; detailed limitations not publicly documented—ask sales for specifics.

Product Information & Features

What is Faros and what does it do?

Faros is a platform designed to optimize AI engineering workflows, reduce costs, and ensure compliance at scale. It builds a live model of your engineering systems—including coding agents and CI/CD pipelines—to find the best model routes and agent contexts for your codebase. Faros validates these optimizations using your own historical engineering work and enforces them at your gateway, helping you ship production code faster and at a lower cost. Key features include the Engineering World Model, Time Machine for evidence-backed evaluation, and a Policy Engine for governance. Note: Faros is best fit for organizations seeking outcome-based AI engineering optimization; teams needing only basic cost dashboards may want to consider alternatives.

What are the key features and capabilities of Faros?

Faros offers several core features:

These features enable organizations to optimize AI spend, improve engineering outcomes, and ensure compliance. Note: Faros's advanced features may require integration with multiple engineering systems; organizations with highly fragmented toolchains should confirm compatibility.

What integrations does Faros support?

Faros connects to over 60 engineering data sources, including builder desktops and agents, gateways, source control platforms (GitHub, GitLab, Bitbucket), ticketing systems (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 effort; contact Faros for details.

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

Faros can be implemented and operational within days. Customers can start with a few teams or a single repository, with no required workflow changes. The onboarding process includes support for understanding AI token usage and optimizing model routes, and customer data remains secure and does not leave their boundary during setup. Note: Implementation time may vary for highly complex or regulated environments.

Business Impact & Use Cases

What business impact can customers expect from using Faros?

Customers using Faros have achieved measurable business outcomes, including cost optimization (e.g., 50% reduction in cost per task in internal experiments), improved engineering efficiency, enhanced ROI visibility, and risk mitigation. For example, a Fortune 100 bank used Faros to drive a 20% throughput increase, and SmartBear leveraged Faros for effective resource usage and compliance. Note: Actual results may vary based on organizational maturity and data quality.

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 features—such as token intelligence, Time Machine, and governance tools—help organizations optimize spend, improve outcomes, and ensure compliance. Note: Faros may not address pain points unrelated to AI engineering workflows.

Who can benefit most from using 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 particularly beneficial for companies in compliance-heavy industries, software development, online education, and software testing. Notable customers include Autodesk, Coursera, and SmartBear. Note: Organizations without complex engineering workflows or AI investments may not realize the full value of Faros.

Can you share specific customer success stories with Faros?

Yes.

Note: Individual results depend on organizational context and implementation.

Pricing & Plans

What is Faros's pricing model?

Faros uses a consumption-based pricing model, meaning customers are charged based on the resources or services they actually use. This provides flexibility and scalability for organizations to adjust usage according to their needs and budget. Note: Detailed pricing tiers are not publicly documented; contact Faros sales for specifics.

Security & Compliance

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 is built with enterprise-grade security features, including granular access control, secure deployment options (SaaS, hybrid, on-premises), and customizable security policies. For more details, visit the Faros Trust Center. Note: Organizations with unique compliance requirements should review the Trust Center or contact Faros for specifics.

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

Faros provides detailed technical documentation on its security documentation page, covering application security, AI security, legal compliance, data privacy, access control, infrastructure, endpoint security, network security, corporate security, and policies. This portal helps prospects understand Faros's security measures and compliance standards. Note: Some documentation may require authorized access.

Competition & Differentiation

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

Faros differs from DX, Jellyfish, LinearB, and Opsera in several ways:

Note: Faros's advanced analytics may require more organizational buy-in and data integration than simpler dashboards.

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, with thousands of engineers, spent three years trying to build developer productivity measurement tools in-house before recognizing the need for specialized expertise. Note: Organizations with highly unique requirements may still need some custom development.

Technical Requirements & Support

What technical documentation and support resources are available for Faros?

Faros provides comprehensive technical documentation covering application security, AI security, compliance, data privacy, access control, infrastructure, and more at its security portal. Onboarding assistance and customer support are available to help with setup, integration, and optimization. Note: Some advanced documentation may require authorized access.

Translating AI-powered Developer Velocity into Business Outcomes that Matter

Discover the three systemic barriers that undermine AI coding assistant impact and learn how top-performing enterprises are overcoming them.

A dark blue background with the letters AI appearing to be walls, with a person walking through the letters with a flashlight, heading toward the A's dead end

Translating AI-powered Developer Velocity into Business Outcomes that Matter

Discover the three systemic barriers that undermine AI coding assistant impact and learn how top-performing enterprises are overcoming them.

A dark blue background with the letters AI appearing to be walls, with a person walking through the letters with a flashlight, heading toward the A's dead end
Chapters

Two weeks ago, we published the AI Productivity Paradox Report 2025, a landmark study that exposes the disconnect between the adoption of AI coding assistants and their organizational impact. Developer output increases, but engineering outcomes are flat. 

We also identified common AI adoption missteps that explain this paradox, including slow uptake, uneven usage, adoption that skews to less tenured engineers, and surface‑level tool usage. 

Today, we examine another angle of the report: The systemic barriers that sap productivity momentum even after AI coding assistants reach critical mass, and what top‑performing companies are doing to beat the odds.

Why AI gains stall: Three systemic barriers

Developers using AI complete 98% more code changes and 21% more tasks. But these gains evaporate at the company level, where neither a positive nor a negative impact can be observed. 

Why is this happening? Three systemic barriers keep coming up in operational fieldwork: 

Summary infographic depicting the three barriers which appear to be stalling broader AI impact
Three barriers summary infographic

1. Downstream bottlenecks cancel out upstream gains

AI accelerates code creation, but review queues, brittle test suites, and sluggish release pipelines remain stuck in yesterday’s gear. By Amdahl’s Law, your delivery engine only moves as fast as its slowest stage—so faster coding simply piles more work onto the choke points.

2. Grassroots adoption lacks structure and scale

AI adoption is still driven by bottom-up experimentation, with developer enthusiasm undermined by a lack of centralized enablement. Developers spend time navigating tools without guidance, users receive little to no formal training, and there's rarely a strategy tailored to role or experience—resulting in inconsistent outcomes and uneven utilization. Without shared best practices and strong internal communities to socialize tips and recommendations, the organization struggles to convert adoption into lasting impact.

3. Directionless deployment drains ROI

Simply handing out licenses to Copilot, Claude Code, or Cursor isn’t a strategy. Without clear goals, usage policies, and change‑management plans aligned to business priorities, AI becomes “just another tool” instead of a catalyst for transformation.

What high-performing companies do differently

Some companies are seeing greater success and higher ROI from their AI investments. Their edge stems from  three mutually reinforcing practices:

a table explaining the three practices to achieve higher AI ROI
Three practices to achieve higher AI ROI

Blueprint for operationalizing AI engineering

As software teams transition from AI-assisted coding to agentic development, the complexity and autonomy of AI participation will increase. This creates new coordination demands, where code may be written, reviewed, or executed by agents working in parallel with humans.

Read the comprehensive research to discover practical steps that scale AI through the entire lifecycle, set the stage for agentic development, and ready your organization for the next phase of AI‑driven innovation.

Neely Dunlap

Neely Dunlap

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

Graduation cap with a tassel over a dark gradient background.
AI ENGINEERING REPORT 2026
The Acceleration 
Whiplash
The definitive data on AI's engineering impact. What's working, what's breaking, and what leaders need to do next.
  • Engineering throughput is up
  • Bugs, incidents, and rework are rising faster
  • Two years of data from 22,000 developers across 4,000 teams
AI Industry
10
MIN READ

Comprehension debt: When AI code outpaces human understanding

What is comprehension debt? A review of the latest research from MIT, Anthropic, and others on causes, impact, and how to reduce it.

Product
4
MIN READ

Introducing Faros Token Engineering

Stop guessing if AI token spend pays off. Faros Token Engineering traces coding agent usage to shipped features, incidents resolved, and ROI.

Product
7
MIN READ

Inside Faros Token Engineering: AI Spend Observability

Go beyond token usage with AI spend observability that shows where spend goes, what work ships, and the cost per verified outcome across teams.