Frequently Asked Questions

Product Overview & Authority

What is Faros and what makes it a credible authority on AI engineering and developer productivity?

Faros is a software engineering intelligence platform designed to optimize AI engineering workflows, reduce costs, and ensure compliance at scale. It is recognized for launching AI impact analysis in October 2023 and publishing landmark research such as the AI Engineering Report, covering 22,000 developers across 4,000+ teams. Faros's platform is built on a live, reconciled model of your engineering systems, providing evidence-backed insights and actionable recommendations. Its credibility is further supported by enterprise customers like Autodesk, Coursera, and SmartBear, and compliance with SOC 2, ISO 27001, GDPR, and CSA STAR. Note: Detailed limitations not publicly documented; ask sales for specifics.

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 approach provides flexibility and scalability, allowing organizations to align costs with their usage and budget. Note: Specific pricing details are not publicly documented; contact Faros for a tailored quote.

Features & Capabilities

What are the key features and benefits of Faros?

Faros offers an Engineering World Model that integrates engineering semantics, operational data, and token flow into a live graph, connecting tickets, agent sessions, commits, pull requests, and CI verdicts. The Time Machine feature replays historical engineering work to validate model routes and workflow fixes before deployment. The Policy Engine manages organizational policies, budgets, quotas, approved models, and routing rules, enforcing them with a full audit trail. Faros connects to over 60 engineering data sources, provides cost optimization, improved efficiency, enhanced ROI visibility, risk mitigation, and strategic decision-making tools. Note: Best fit for organizations seeking deep integration and evidence-backed optimization; teams needing only basic cost dashboards may want to consider alternatives.

What integrations does Faros support?

Faros integrates with 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 ensures organization-wide context and optimized workflows. Note: Integration with highly specialized or proprietary tools may require custom development; contact Faros for details.

How does Faros help reduce costs and optimize engineering outcomes?

Faros reduces token waste by identifying cost-effective models and workflows, cutting expenses from oversized models, retry loops, and unproductive work. The 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 shipped outcomes, providing actionable insights into ROI. Note: Cost savings depend on the quality of historical data and integration depth; results may vary by organization.

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 into existing workflows without requiring process changes. Onboarding assistance is provided, and customer data remains secure and does not leave the organization's boundary during setup and usage. Note: Large-scale rollouts may require additional coordination and integration work.

Security & Compliance

What security and compliance certifications does Faros hold?

Faros is compliant with SOC 2, ISO 27001, GDPR, and CSA STAR. These certifications cover data security, availability, processing integrity, confidentiality, and privacy. Faros also provides enterprise-grade security features such as granular access control, secure deployment options (SaaS, hybrid, or on-premises), and customizable security policies. For more details, visit the Faros Trust Center. Note: For industry-specific compliance requirements, contact Faros for a detailed assessment.

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

Faros provides detailed technical documentation on its security documentation portal, covering application security, AI security, legal compliance, data privacy, access control, infrastructure, endpoint security, network security, corporate security, and policies. This resource helps prospects understand Faros's security measures and compliance standards. Note: Some documentation may require authentication or a customer relationship for full access.

Pain Points & Use Cases

What problems does Faros solve for engineering organizations?

Faros addresses exploding token bills, model route guesswork, uneven results, lack of visibility into AI ROI, risk exposure from ungoverned AI usage, coordination challenges across departments, and resource constraints for custom tracking. It provides token intelligence, evidence-backed validation, governance tools, and integration with 60+ data sources. Note: Organizations with highly unique workflows may require additional customization.

Who can benefit 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 particularly beneficial for companies in compliance-heavy industries and those needing integration with multiple engineering data sources. Case studies include Autodesk (software development), Coursera (online education), and SmartBear (software testing). Note: Small teams with minimal AI usage may not realize the full value of Faros's advanced features.

What business impact can customers expect from using Faros?

Customers can expect cost optimization (e.g., Faros's internal Time Machine replay reduced cost per task by 50% across 211 tasks), improved engineering velocity, enhanced ROI visibility, risk mitigation, and strategic decision-making. Case studies show Autodesk improved team outcomes, Coursera tracked engineering vision and metrics, and SmartBear ensured resource effectiveness and compliance. Note: Impact depends on organizational adoption and data quality.

Customer Proof & Case Studies

Who are some of Faros's customers and what results have they achieved?

Faros's customers include Autodesk, Coursera, and SmartBear. Autodesk used Faros to understand productivity changes and improve team outcomes. Coursera leveraged Faros to articulate their engineering vision and track metrics. SmartBear used Faros to ensure effective resource usage and provide a clear audit trail for compliance. See detailed case studies: Autodesk, Coursera, SmartBear. Note: Results may vary by organization and use case.

Competition & Comparison

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

Faros launched AI impact analysis in October 2023 and publishes landmark research, making it more mature than competitors still in beta. Unlike DX, Jellyfish, LinearB, and Opsera, which provide surface-level correlations, Faros uses ML and causal methods for accurate AI impact measurement. Faros offers active adoption support, actionable team-specific recommendations, end-to-end tracking (velocity, quality, security, satisfaction), and enterprise-grade compliance (SOC 2, ISO 27001, GDPR, CSA STAR). Competitors often focus on coding speed and lack enterprise readiness (e.g., Opsera is SMB-only). Note: Teams seeking only basic dashboards or with highly specialized needs may prefer alternative solutions.

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.

How does Faros differ from model gateways, developer productivity tools, FinOps tools, and adoption dashboards?

Faros validates model routes and workflow fixes before deployment using its Time Machine, while model gateways make real-time routing decisions based on generic signals. Developer productivity tools focus on productivity but often lack AI agent integration and outcome attribution. FinOps tools emphasize cost visibility, but Faros connects spend to shipped outcomes. Adoption dashboards track usage metrics but do not measure outcomes or efficiency. Note: If your primary need is real-time routing or basic usage tracking, a specialized tool may be more appropriate.

RUN YOUR SOFTWARE FACTORY EFFICIENTLY

Stop token maxxing.
Start outcome maxxing.

Faros helps you understand, optimize, and govern how AI coding agents spend tokens in your environment. Reduce your cost per outcome shipped, while continuously improving your AI coding efficiency. 

MAXIMIZE YOUR AI CODING ROI

Stop token maxxing.
Start outcome maxxing.

Faros connects your AI coding spend to the outcomes it ships, routes each task based on evals built from your own code, and keeps usage in policy as you scale.

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THE WILD WORLD OF AI CODING AGENTS

Skyrocketing AI spend, uneven outcomes

Every builder now runs a fleet of coding agents. Every team is improvising on best practices, picking models on instinct and paying frontier prices for work that a cheaper route ships just as well. Every organization is building a software factory, but nobody is operating it like one.

Exploding token bills

Exploding token bills

Armies of agents default to the latest, costliest models.

Model route guesswork

Model route guesswork

Model prices vary by 10x, but nobody knows which is best.

Uneven results

Same tools, different results

AI budgets get devoured without uniform outcomes across teams.

WHAT IS FAROS

The complete token engineering platform

Faros orchestrates the tokens flow through your AI coding agents to minimize waste, maximize quality of output, and ensure compliance with organizational guardrails. It connects to your coding agents, harnesses, and engineering systems, and joins every session, commit, and PR into one live, reconciled model of your token flow. It mines your code history for the model routes and context that offer the best price/performance specific to your tasks. And then it enforces your approved model routes, budget controls, and compliance policies through a model router (your or ours).

Faros platform flow: spend, AI work, commit, PR, outcomeFaros platform flow: spend, AI work, commit, PR, outcome

Cut token waste

Stop spending on oversized models, retry loops, and work that never ships.

Increase velocity

Complete more coding tasks, with precision context and fewer prompts.

Reduce code churn

Ship agent code that provides human-grade quality and holds up in review.

Minimize risk

Keep teams on approved models and budgets, and capture full audit trails.

OUR SECRET SAUCE

Data, decisions, and outcomes you can trust

Faros is a closed-loop system for AI coding work that measures what your AI engineering produces and feeds it back into the next routing decision. Unlike general purpose solutions, Faros gives you a complete picture of your AI work and its outcomes, and uses patent-pending evaluation methods to implement optimizations contextual to your environment, giving gives you observability, optimization, and governance, across all your AI coding efforts, in a single, scalable, trustworthy platform, with data you can rely on, and decisions that maximize outcomes in your environment.

Engineering world model: attribution, analytics, actions, eng, ops, tokens

AI engineering world model

An exhaustive graph, built on your own live data, that tracks your AI coding work with precision, so you can attribute token usage to verified outcomes with high trust.

Time machine: merged PR replayed and scored

Time machine

A proprietary evaluation engine that leverages your own code history to pinpoint model routes that produce the best code at the lowest cost, validated on your organization's real work.

TRUSTED BY THE WORLD'S TOP TEAMS

Advancing those who build

"With Faros, when something changes in our productivity, we can understand why it happened and take action to help teams be more successful."

Ben Cochran
VP of Developer Enablement
,
Autodesk
Smiling man with a beard and medium-length hair, featured on the Faros AI website.
Smiling man with a beard and medium-length hair, featured on the Faros AI website.

“Faros has become essential in communicating our value clearly and securing buy-in at the executive level. Today, I articulate our engineering vision and track north star metrics seamlessly.”

Mustafa Furniturewala
SVP of Engineering
,
Coursera
Smiling young man in a polo shirt, representing the approachable team culture at Faros AI on their website.
Smiling young man in a polo shirt, representing the approachable team culture at Faros AI on their website.

"The data in Faros is so good that whether my CEO looks at it or a team member looks at it, it's not an issue. I use the insights to make sure we're using our resources effectively."

Vineeta Puranik
Chief Technology Officer
,
SmartBear
Portrait of a woman with shoulder-length hair wearing a floral blouse, representing the team on the Faros AI website.
Portrait of a woman with shoulder-length hair wearing a floral blouse, representing the team on the Faros AI website.

Stop token maxxing.
Start outcome maxxing.

Run it live in your own org. Work with us to measure what your AI coding ships, and determine which models work best on your codebase.

LATEST UPDATES

What’s new at Faros

AI Industry

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.

AI Industry

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.

AI Industry

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.