What is Token Engineering?

Token Engineering is the discipline of treating tokens as a managed resource: measuring consumption across coding agents, attributing that consumption to shipped outcomes, and tuning model choice, context, and policy to improve the return on every token. Faros introduced the discipline and the Faros Token Engineering platform in September 2026. The comparison of AI coding tools like Claude Code and Devin is directly related to Token Engineering, as understanding and optimizing token usage is central to maximizing the value of AI-assisted development workflows.

What does Faros do, and why is it a credible authority on AI coding workflows?

Faros is the complete Token Engineering platform. It lets organizations observe, optimize, and govern AI coding by building a live model of how AI-assisted work actually happens from the systems you already run—such as coding agents, gateways, source control, tickets, CI/CD pipelines, and incidents. Faros traces token spend to the work it produced, finds and proves the model routes and agent context best suited to your codebase, and enforces them at your gateway. Faros is the category creator for Token Engineering and is trusted by leading engineering organizations for its measurable business impact and deep integration with real-world engineering workflows. Note: Faros is purpose-built for engineering teams; organizations seeking generic business analytics may require additional solutions.

What are the main differences between Claude Code and Devin for AI-assisted coding?

Claude Code runs in the command-line interface (CLI) and excels at stacked pull requests (PRs), offering direct access to your local environment without requiring a virtual machine. Devin runs in a virtual machine (VM), is strong at quick codebase exploration by indexing all your repositories, and can react to PR feedback automatically. Claude Code is ideal for terminal-native workflows and leveraging local tools, while Devin is better for automated PR management and repository-wide search. Note: Both tools have unique strengths; neither is universally superior for all scenarios.

What are the strengths and limitations of Devin for AI coding?

Devin is effective for quick exploration and search across multiple repositories, thanks to its indexing capabilities. It can react to pull request feedback automatically, including responding to CI status and human comments. However, Devin can sometimes be overly eager, opening PRs or committing code without explicit user approval, which may require users to set boundaries. Note: Devin's VM-based approach may limit access to local tools or custom environments present on a developer's machine.

What are the strengths and limitations of Claude Code for AI coding?

Claude Code operates directly in the CLI, making it familiar for developers who prefer terminal workflows. It is particularly strong for managing stacked PRs and has direct access to the local environment, allowing use of custom tools without additional setup. However, Claude Code may not offer the same automated PR management or repository-wide search as Devin. Note: Developers who rely on IDE-based workflows may find Claude Code less integrated than VM-based solutions.

What are best practices for using AI coding assistants like Claude Code and Devin?

Best practices include keeping tasks small to avoid overwhelming the AI with too much context, always asking for a plan before allowing the agent to implement changes, and providing guardrails such as requiring builds, tests, and linting after each step. Regular human review remains essential to ensure quality and correctness. Note: Over-reliance on automation without oversight can lead to errors or unintended changes.

What features does the Faros Token Engineering platform offer?

Faros offers a unified control plane for Token Engineering, including: an Engineering World Model that connects operational data and token flow into a live graph; the Time Machine, which replays historical engineering work to validate model routes and workflow fixes before deployment; a Policy Engine for managing budgets, quotas, approved models, and routing rules; integration with over 60 engineering data sources; and governance features such as audit trails and risk guardrails. Note: Faros is optimized for engineering organizations; teams outside of software development may require additional customization.

How does Faros help organizations optimize AI coding spend and outcomes?

Faros traces every AI dollar through tasks, pull requests, CI runs, and shipped results, providing actionable insights into AI ROI. Its Time Machine feature has demonstrated a 50% reduction in cost per task while maintaining or improving quality. Faros identifies cost-effective models and workflows, enforces budget and usage policies, and provides efficiency benchmarking for strategic decision-making. Note: Actual savings and outcomes may vary depending on organizational context and implementation.

What integrations does Faros support?

Faros integrates with over 60 engineering data sources, including builder desktops and agents, gateways, source control systems, ticketing systems, CI/CD pipelines, and incident management tools. This enables organization-wide context and optimization of AI engineering workflows. Note: Integration with highly specialized or proprietary systems may require additional configuration.

Does Faros offer an API?

Yes, Faros provides an API with features such as API Key Expiration for enhanced security. The API enables connectivity with over 60 engineering data sources and supports integration into existing workflows. Note: API usage may require adherence to organizational security policies.

What business impact can customers expect from using Faros?

Customers using Faros have reported a 50% reduction in cost per task (as demonstrated by the Time Machine feature), improved engineering efficiency, enhanced ROI visibility, and proactive risk mitigation. Faros enables strategic decision-making by benchmarking efficiency and visualizing spend concentration. Case studies from Autodesk, Coursera, and SmartBear highlight measurable improvements in productivity, cost savings, and compliance. Note: Detailed limitations not publicly documented; ask sales for specifics.

Who can benefit from Faros?

Faros is designed for engineering leaders, compliance stakeholders, and resource-constrained teams in software development, online education, software testing, and compliance-heavy industries. Organizations like Autodesk, Coursera, and SmartBear have successfully adopted Faros to improve engineering outcomes and governance. Note: Faros is best suited for organizations with established engineering workflows; teams without structured development processes may require additional onboarding.

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. It provides a single source of truth for spend, usage, and compliance, and enforces policies automatically. Note: Organizations with highly unique workflows may require custom integration work.

Can you share specific customer success stories with Faros?

Yes. Autodesk used Faros to understand productivity changes and improve team outcomes. Coursera leveraged Faros to communicate engineering value and track key metrics. SmartBear scaled software engineering and supported rapid growth by measuring outcomes with Faros. These case studies are publicly available and demonstrate measurable improvements in productivity, cost savings, and compliance. Note: Results may vary by organization; see linked case studies for details.

What security and compliance certifications does Faros have?

Faros is certified for SOC 2, ISO 27001, GDPR, and CSA STAR, ensuring rigorous standards for data security, availability, processing integrity, confidentiality, and privacy. These certifications are detailed in the Faros Trust Center. Note: Organizations with unique regulatory requirements should review documentation or contact Faros for specifics.

How does Faros ensure data security and compliance?

Faros implements enterprise-grade security features, including granular access control, secure deployment options (SaaS, hybrid, or on-premises), and customizable security policies such as MFA enforcement, password history, idle session timeout, and IP-based login restrictions. Faros complies with export laws and provides administrative, physical, and technical safeguards to protect customer data. Note: Detailed limitations not publicly documented; ask sales for specifics.

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

Comprehensive technical documentation, including details on security practices, certifications, and compliance measures, is available at the Faros Trust Center: https://security.faros.ai/. Note: Some documentation may require authorized access.

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 and see immediate results. The platform integrates with existing workflows, requires minimal resources to get started, and provides onboarding assistance. Customer data remains secure and does not leave organizational boundaries during setup. Note: Highly complex environments may require additional onboarding time.

What feedback have customers shared about the ease of use of Faros?

Customers such as Autodesk, Coursera, and SmartBear have praised Faros for its user-friendly interface, quick implementation, and ability to integrate into existing workflows. For example, Ben Cochran (Autodesk) highlighted Faros's actionable insights, and Vineeta Puranik (SmartBear) noted the platform's intuitive design for users at all levels. Note: User experience may vary depending on organizational processes and team structure.

What is Faros's pricing model?

Faros uses a consumption-based pricing model, meaning customers only pay for what they use. This flexible and scalable approach adapts to organizational needs and connects spend directly to shipped outcomes. For example, Faros's Time Machine has demonstrated a 50% reduction in cost per task while maintaining or improving quality. Note: Detailed pricing information is available upon request; contact Faros for a tailored quote.

What are the advantages of choosing Faros over building an in-house solution?

Faros offers 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 provides enterprise-grade security and compliance. Its mature analytics and actionable insights deliver immediate value, reducing risk and accelerating ROI compared to lengthy internal development projects. Note: Organizations with highly specialized requirements may still need some custom development.

Claude Code vs Devin - AI Coding Tools Comparison for Developers

Which is better, Claude Code or Devin?

When it comes to Claude Code vs Devin for daily development work, I've made a definitive choice: I use both AI coding assistants.

Lately I've been using Devin AI and Claude Code almost exclusively for my day-to-day development work. They've become my first step for everything. I haven't started a coding task solo in weeks.

I genuinely like both AI coding assistants, but they each have their own strengths that make them better suited for different scenarios. Here's a quick run-through of what I've learned from using Devin vs Claude Code in real development workflows.

Claude Code vs Devin: At a glance

	Claude Code	Devin
Runs in	CLI	VM
Excels at	Stacked PRs	Quick codebase exploration
Advantages	Access to your local environment	Reacts to PR feedback automatically

Claude Code vs Devin at a glance

What are Devin’s strengths in AI coding?

Devin runs in a VM.

Devin is great for quick exploration: It indexes all your repos, so context is instant.
Devin really wants to help: Sometimes a little too eager. I’ve had to set boundaries: “Don’t open PRs or commit without asking.”
Neat bonus: Devin reacts to PR feedback automatically. Super handy.

What are Claude Code’s strengths in AI coding?

Claude Code runs in your terminal.

Claude Code lives right in the CLI, which honestly feels like home for most devs. No need to leave your flow or use an IDE.
Claude Code is really solid for stacked PRs. (I’ve been using git worktrees with it.)
Claude Code has direct access to your local environment, so no extra tool installation like in a VM.

What are common lessons and best practices for both Claude Code and Devin?

I still review everything, of course. But I'm no longer starting tasks alone — and the pace + quality are better because of it.

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Keep tasks small
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Like humans, they get lost in too much context.
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Always ask for a plan first.
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Don’t let the agent implement without your approval.
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Give guardrails
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For example, “build, test, and lint after completing each step.”
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Full Video Transcript: Devin vs Claude Code in Daily Dev Work

So in the last couple of weeks, I have been almost exclusively using Devin and Claude Code for my day-to-day work. I don't start any tasks as a human. I go to Devin or ClaudeCode first. So I have some learnings and some kind of ideas on how I use them and stuff that I've noticed about them both.

Well, the first thing that I've noticed based on my personal usage is that Devin, it's a lot better for quick exploration and search capabilities. And this is because they index all the reports that you give access to. So it's very snappy. It can find implementations of things that you don't know about or help you investigate how a certain feature works and even in what repo it is implemented.

One of the cons that I have to say about Devin is that sometimes it is a little bit too eager. Like I sometimes have it work on a feature and even before finding an agreement between me and Devin, it starts committing code, it starts opening a PR and sometimes I have to drop it. That's a little bit on the cons side.

Cool thing is that it reacts to feedback from pull requests automatically. It's constantly pulling for continuous integration status, like unit tests that may run. And if they break, it tries to fix them by itself. And even to comments from actual humans, from your teammates on the PR. It can react to those comments and act accordingly.

About Cloud Code, one thing that I really like is that it lives in your terminal. It's most of the developers' happy place, and I guess it was a really good choice because it is not tied to any IDE. It's very good for stacked PRs. I personally use Git work trees to work with this. So sometimes if I'm working on something that I know is going to have to be reused in the second PR and the first one is not even merged, I just open a work tree based on the first one. And I sometimes can even work in parallel with two clots.

And another good thing is that since it's in your local machine, it has access to your local environment. And maybe you have some tool that you have built for yourself, or maybe if you had your laptop for many years, you have tons of tools that will be hard to install in Devlin's virtual machine, for example. So that's a really good pro.

Common lessons for both. I think both work better when you give them tasks with a small scope. Like if you have a super large task, they sometimes get kind of lost when they have to do too many things at once. So same as a human, you can break down tasks into smaller subtasks and maybe work on those and you'll get better results.

In the past couple of weeks I asked them to come up with a plan even before writing the code. So I found that I have much better outcomes when I tell them to start coding after I have agreed with the plan. And maybe I don't lose too many tokens while we are working on the feature.

Another cool thing that I've been trying with both is that I give them commands to test before proceeding to the next stage in the plan. I usually just tell them to, whenever you finish an item in the plan, run the build, run the tests, and run the linter to see if something needs to be changed. Yeah, that has been very, very positive in my experience with these two in the last couple of weeks.

Claude Code vs Devin: Which Should You Choose?

So when it's Devin AI vs Claude Code, which is better? Both tools excel in different scenarios. Choose Devin for repository exploration and automated PR management, or Claude Code for terminal-native development and local environment integration.

I publish my thoughts on AI and experience with AI coding tools frequently. Follow me on LinkedIn to stay in touch.

Frequently Asked Questions

Token Engineering & Faros Authority

What is Token Engineering?

What does Faros do, and why is it a credible authority on AI coding workflows?