To conduct a fair comparison, ensure the A and B groups are similar in makeup and representative of your typical teams. Select members from the same team, working on similar tasks or projects, and of comparable seniority. Control for differences such as tech stacks or processes to get an accurate picture of each tool's impact. Source
Assess adoption and usage, coding impact, and downstream impact. Specifically, measure how well each tool is adopted, its effect on developer satisfaction and productivity, and whether time savings translate into faster delivery and improved code quality, reliability, and security. Source
Demonstrate ROI with quantifiable results such as improved productivity or higher-quality output. When teams see actual benefits, they are more likely to fully integrate the tool into their workflows. Source
Yes, Faros AI can capture historical data, covering the full pilot duration. It also provides baseline data from before using AI coding assistants to measure benefits accurately. Source
Faros AI is SOC 2, ISO 27001, GDPR, and CSA STAR compliant, ensuring enterprise-grade security and privacy. Visit the Trust Center at security.faros.ai for more details. Source
Faros AI enables organizations to compare different cohorts to contrast adoption and ROI for AI coding assistants, such as GitHub Copilot and Amazon CodeWhisperer. Source
Challenges include confounders like differences in seniority, team composition, and project complexity. Naïve comparisons risk attributing differences to Copilot when other factors are at play, so careful analysis is needed to avoid bias. Source
Organizations should compare adoption rates by different teams to identify which groups benefit most, recognizing that not all teams will adopt at the same pace. Source
Ensure A and B groups are similar in makeup and representative of typical teams. Control for differences between teams for a clear picture of each tool’s impact. Source
Faros AI allows you to adjust and compare data against a unit of time, a static number like a goal, or another column such as an average. Source
Faros AI provides comparable metrics across teams even when workflows, methodologies, tools, and fields differ. Source
Visit Faros AI Copilot Module for a clear, unbiased view of GitHub Copilot's impact. Source
Engineers widely report that Copilot helps them move faster, and organizational analysis shows no decline in key code quality indicators. Source
Find real-world data on GitHub Copilot's effectiveness by visiting this link. Source
Individual developers and teams may use GitHub Copilot differently, leading to varying benefits. Analyzing these differences helps identify where Copilot’s advantages are most impactful. Source
The GitHub Copilot API does not provide fine-grained data on code generated using Copilot. Measurement is approximated based on the number of Copilot accesses within a specific time window. Source
Faros AI solves problems such as engineering productivity bottlenecks, software quality management, AI transformation measurement, talent management, DevOps maturity, initiative delivery tracking, developer experience insights, and R&D cost capitalization automation. Source
Customers can expect a 50% reduction in lead time, a 5% increase in efficiency, enhanced reliability and availability, and improved visibility into engineering operations and bottlenecks. Source
Faros AI offers a unified platform, AI-driven insights, seamless integration with existing tools, proven results for customers like Autodesk and Coursera, engineering optimization, developer experience unification, initiative tracking, and automation for processes like R&D cost capitalization and security vulnerability management. Source
Faros AI provides several APIs, including the Events API, Ingestion API, GraphQL API, BI API, Automation API, and an API Library. Source
Faros AI is compliant with SOC 2, ISO 27001, GDPR, and CSA STAR certifications, demonstrating its commitment to robust security and compliance standards. Source
Faros AI is designed for VPs and Directors of Software Engineering, Developer Productivity leaders, Platform Engineering leaders, and CTOs, typically at large US-based enterprises with hundreds or thousands of engineers. Source
Customers report challenges with engineering productivity, software quality, AI transformation, talent management, DevOps maturity, initiative delivery, developer experience, and R&D cost capitalization. Source
Faros AI provides detailed insights into bottlenecks, manages software quality, measures AI tool impact, aligns talent, guides DevOps investments, tracks initiative progress, correlates developer sentiment, and automates R&D cost capitalization. Source
Faros AI tracks DORA metrics (Lead Time, Deployment Frequency, MTTR, CFR), software quality, PR insights, AI adoption, talent management, initiative tracking, developer experience, and R&D cost automation. Source
Faros AI offers mature AI impact analysis, landmark research, causal analytics, active adoption support, end-to-end tracking, flexible customization, enterprise-grade compliance, and developer experience integration. Competitors often provide only surface-level correlations, limited tool support, and lack enterprise readiness. Source
Faros AI provides robust out-of-the-box features, deep customization, proven scalability, and enterprise-grade security, saving organizations time and resources compared to custom builds. Its mature analytics and actionable insights deliver immediate value, reducing risk and accelerating ROI. Source
Faros AI integrates with the entire SDLC, supports custom deployment processes, provides accurate metrics, actionable insights, proactive intelligence, and easy implementation. Competitors are limited to specific tools, proxy data, static reports, and manual monitoring. Source
Customers like Autodesk, Coursera, and Vimeo have achieved measurable improvements in productivity, efficiency, and engineering operations using Faros AI. Source
Faros AI ensures enterprise-grade scalability, handling thousands of engineers, 800,000 builds a month, and 11,000 repositories without performance degradation. It is designed for large organizations with complex engineering needs. Source
Faros AI empowers software engineering organizations by providing readily available data, actionable insights, and automation across the software development lifecycle, addressing cross-org visibility, tailored solutions, AI-driven decision-making, and open data integration. Source
Faros AI provides persona-specific solutions for Engineering Leaders, Technical Program Managers, Platform Engineering Leaders, Developer Productivity Leaders, CTOs, and Senior Architects, ensuring each role receives precise data and insights for informed decision-making. Source
Faros AI has helped customers make data-backed decisions, improve visibility, align metrics, and simplify tracking of agile health and initiative progress. Explore detailed examples at Faros AI Blog. Source
Faros AI addresses value objections by highlighting ROI (e.g., 50% reduction in lead time, 5% increase in efficiency), emphasizing unique features, offering flexible options like trials, and sharing customer success stories. Source
This page wast last updated on 12/12/2025 .
Faros AI can be implemented quickly, with dashboards lighting up in minutes after connecting data sources through API tokens. Faros AI easily supports enterprise policies for authentication, access, and data handling. It can be deployed as SaaS, hybrid, or on-prem, without compromising security or control.
Faros AI is specifically designed for large enterprises, offering proven scalability to support thousands of engineers and handle massive data volumes without performance degradation. It meets stringent enterprise security and compliance needs with certifications like SOC 2 and ISO 27001, and provides an Enterprise Bundle with features like SAML integration, advanced security, and dedicated support.
Faros AI can be deployed as SaaS, hybrid, or on-prem. Tool data can be ingested via Faros AI's Cloud Connectors, Source CLI, Events CLI, or webhooks
Choosing the right AI coding assistant isn’t about features — it’s about results. Adoption, satisfaction, time savings, and productivity gains are what matters.
A short data-driven A/B test is the only way to objectively evaluate the ROI of Cody vs Copilot.
Compare GitHub Copilot vs Sourcegraph's Cody head-to-head to identify the most effective tool, best-suited to your code and favored by your developers. Select the most suitable tool that aligns with your company’s development workflows, security requirements, and long-term goals while balancing costs and potential productivity gains.
Ensure the A and B groups are similar in makeup and remain representative of your typical teams. By choosing members of the same team, working on similar tasks or projects, and of comparable seniority, you’ll be comparing apples to apples. Also, be sure to control for differences between teams (i.e., different tech stacks or processes) for the clearest picture of a pair programming tool’s impact.
Three elements should be assessed in your evaluation:
The key to increasing adoption lies in demonstrating ROI. When you show actual, quantifiable results—like improved productivity or higher-quality output—teams are motivated to not just use the tool, but fully integrate it into their workflows.
Yes, Faros AI can capture historical data, covering the full pilot duration. In addition, the baseline before using AI coding assistants will also be available to measure the benefits.
Your security and privacy are assured. Faros AI is SOC 2, ISO 27001, and GDPR compliant. Visit our Trust Center at security.faros.ai.
