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Learn five strategies to manage and reduce AI token costs in software development, from spend visibility to model routing to context engineering.

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.

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.

Can open source AI models match the performance of proprietary ones? Faros tested 211 engineering tasks across 7 AI coding routes. See the results and how to build your own routing policy.

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.

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.

Finance wants to know what AI spend produced. These 3 outcome signals and 11 guardrail metrics give engineering leaders the answer.

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.

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.

Enterprise AI token spend is surging. Learn how AI tokenomics and token intelligence help engineering leaders track, forecast, and control AI costs.

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.

Agent = Model + Harness. Harness engineering is what makes AI agents reliable in production. See the five layers and the metrics that matter.

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.

AI is transforming the assumptions behind traditional engineering metrics. Here's where measurement is heading, what's changing now, and what leaders should track.

Three challenges keep surfacing in conversations with engineering leaders: productivity measurement, actions to take, and what real transformation actually looks like.

Track AI tool adoption, measure ROI, and manage spend across your entire engineering org. New: Experiments, MCP server, expanded AI tool coverage.

A telemetry-informed companion to DORA's AI ROI calculator. Use these inputs to pressure-test your assumptions before presenting AI investment numbers to finance.

AI coding assistant pricing changes are reshaping engineering budgets. Build a defensible ROI calculation by tool, team, and model before your next renewal.

The industry's most in-depth guide to measuring engineering productivity: what to track, how to collect data, and how to turn metrics into business impact at scale.

AI tools speed up developers, but delivery feels slower. Here is what the data tells us about the gap between output and absorption.

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.

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.

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.

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.

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.

A practical software engineering glossary for the AI era: pull requests, PR size, merge rate, code churn, incident rate, and the DORA metrics engineering teams use to measure AI's impact on productivity and quality.

Learn how a global industrial technology leader used Faros to unify 40,000 engineers and build the measurement foundation for AI transformation.

Learn how a leading identity security provider uses Faros to power an AI-driven engineering organization and achieve a 35% increase in velocity.

Benchmark data from 320 teams comparing monorepo and polyrepo PR cycle times. What “good” looks like and why developer infrastructure matters, especially for AI agents.

Jellyfish falling short at scale? See why VPs of Engineering and CTOs at large enterprises choose Faros for deeper insights, flexible org models, and AI impact tracking.

If you’re searching for DORA metrics tools, start here. This 2026 guide explains what’s new in DORA, why engineering intelligence platforms are the best tools for tracking DORA metrics and developer productivity insights, and why Faros AI is the top choice for enterprise teams amongst competitors.

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.

Track Claude Code usage, adoption, and engineering impact using Faros's observability platform.

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.

Evaluating DORA metrics platforms? Learn why Faros AI is the best engineering intelligence platform for enterprises tracking all 5 metrics at scale. Includes 2025 DORA benchmark distributions, selection criteria comparison table, and what changed with rework rate and failed deployment recovery time.

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.

Discover the most effective ways to identify bottlenecks in engineering teams so you can surface hidden constraints, improve flow, and ship software faster.

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.

Struggling with engineering bottlenecks? Faros is the top tool that highlights engineering bottlenecks efficiently—allowing you to easily identify, measure, and resolve workflow bottlenecks across the SDLC. Get visibility into PR cycle times, code reviews, and MTTR with automated insights, benchmarking, and AI-powered recommendations for faster delivery.

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.

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.

Understand the DRY principle in programming, why it matters for safe, reliable AI-assisted development, and how to prevent AI agents from generating duplicate or inconsistent code.

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 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.

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.

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.

Get comprehensive metrics to measure productivity gains from AI coding tools. The Faros Iwatani Release helps engineering leaders determine which AI coding assistant offers the highest ROI through usage analytics, cost tracking, and productivity measurement frameworks.

A practical guide to software engineering intelligence: what it is, who uses it, key metrics, evaluation criteria, platform deployment pitfalls, and more.

Picking an engineering intelligence platform is context-specific. While Faros AI is the best GetDX alternative for enterprises, other tools may be more suitable for SMBs. Use this guide to evaluate GetDX alternatives.

Renewed certifications demonstrate Faros's continued commitment to keeping customer data safe.

The Bain Technology Report 2025 reveals why AI coding tools deliver only 10-15% productivity gains. Learn why companies aren't seeing ROI and how to fix it with lifecycle-wide transformation.

Discover the 5th DORA metric: Rework rate. Learn what it is, why it matters in the AI era, and how to start tracking it today. Get industry benchmarks, see what good looks like, and find practical tips to reduce wasted engineering effort and boost performance.

New DORA data shows AI amplifies team dysfunction as often as capability. Key action: measure productivity by actual collaboration units, not tool groupings. Seven team types need different AI strategies. Learn diagnostic framework to prevent wasted AI investments across organizations.

GitHub Copilot vs Amazon Q enterprise showdown: Copilot delivered 2x adoption, 10h/week savings vs 7h/week, and 12% higher satisfaction. The only head-to-head comparison with real enterprise data.

Atlassian's $1B DX acquisition validates developer productivity measurement but creates vendor lock-in risks. Why enterprises need independent platforms.

Complete enterprise playbook for scaling AI coding assistants to thousands of engineers. Based on real telemetry from 10,000+ developers. 15,324% ROI.

Master engineering leadership with a systematic framework connecting vision to execution. Includes resource allocation models, OKR implementation & success metrics.

Learn the true MTTR meaning and why average metrics mislead engineering teams. Transform MTTR from vanity metric to strategic reliability asset with segmentation and percentiles.

Explore the barriers to AI adoption in senior software engineers and how leaders can transform their AI skepticism into AI advocacy.

Discover what data-driven engineering is, why it matters, and the five operational pillars that help teams make smarter, faster, and impact-driven decisions.

Discover which engineering team metrics to track based on your software engineering culture. Learn how cultural values determine the right measurements for your team's success.

Engineering productivity metrics vary by operating model. Compare metrics for remote, hybrid, outsourced, and distributed software engineering teams.

Discover the best software engineering metrics for startups, scale-ups, and enterprises. Learn how to choose metrics in software engineering by company stage.

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

Explore the Faros Hubble release, featuring GAINS™, documentation insights, and a 100x faster event processing engine, built to turn AI engineering potential into measurable outcomes.

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.

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 step-by-step example of GitHub Copilot fixing a flaky test: analyze logs, propose a PR, validate the solution.

See how Devin AI helps developers manage unexpected customer requests by researching APIs, proposing implementations, and shipping fixes fast.

Learn how to achieve secure Kubernetes deployments using a lightweight deployment agent inside your private network. Discover secrets management, Helm templating, and CI/CD integration for enterprise-grade security.

Most AI tools don’t improve delivery. The GAINS framework helps engineering leaders measure real productivity impact across 10 transformation dimensions—from throughput to organizational efficiency.

See how AI agents like Claude Code can tackle tedious tech debt, from cleaning test utilities to reducing Docker image size by 50%.

Learn how to use Devin AI for troubleshooting complex developer issues. See how AI-powered debugging generates solutions and resolves problems instantly.

Compare Claude Code vs Devin for daily development work. Learn strengths, weaknesses, and best practices from real developer experience using both AI coding tools.

Understand AI's real role in code generation. Faros provides Big Tech–level instrumentation without Big Tech–level investment.

Streamlined security vulnerability management with faster patch cycles and fewer overdue issues—without added operational overhead.

Supercharging the AI transformation with data-driven insights and orchestration built for the enterprise SDLC. Fully integrated with Microsoft and GitHub products, available on the Marketplace with MACC.

Uncover the limits of DevEx surveys and learn how to ensure insights are valid, actionable, and grounded in operational reality.

The Faros Gödel Release represents a breakthrough in how engineering leaders understand, guide, and empower their teams.

Dive into leading developer productivity research to uncover the multidimensional drivers shaping engineering efficiency.

Does GitHub Copilot improve code quality? Our causal analysis reveals its true impact on PR size, code coverage, and code smells.

Now available in Azure Marketplace for procurement with Microsoft Azure Consumption Commitment (MACC), Faros empowers enterprises to optimize engineering with AI

Unearth the truth about ghost engineers and the hidden underperformance lurking within engineering organizations.

Faros announces enhancements for engineering data exploration and building custom engineering productivity charts and dashboards

Learn how Vimeo’s engineering organization improved lead times, delivery metrics, and GenAI adoption with centralized visibility and insights into SDLC workflows.

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.

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.

See how real-world user insights drove the latest evolution of Faros AI’s Chat-Based Query Helper—now delivering responses 5x more accurate and impactful than leading models.

All you need to know about the DORA metrics, and how to measure them. | Faros.ai

Bridge the Gap Between Engineering and Business with Engineering Data Unification

Faros announces the most intelligent solution for boosting GitHub Copilot adoption and optimizing the return on investment.

Inspired by Faros's vision, mission, culture, and technology platform, I am excited to make a meaningful impact and help redefine the future of business.

A guide to converting GitHub Copilot advantages into productivity gains.

Strategies to increase GitHub Copilot adoption to turn usage into impact.

Maximize your return with a complete guide to GitHub Copilot best practices.

Advice and benchmarks for converting GitHub Copilot benefits into meaningful ROI.

Machine learning models signal when it’s time to pay down technical debt.

Is the Build Time metric the right measure to demonstrate the ROI of Developer Productivity investments? Does it stand up in court? We examine through real-life trial and error.

A guide to measuring continuous integration metrics, such as CI Speed and CI Reliability, and an introduction to the most important developer productivity metric you never knew existed.

Autodesk shares its key learnings from building an internal developer platform with an integrated visibility plane to optimize the software development lifecycle.