Devin - The first AI software engineer that autonomously codes

什么是 Devin

If your engineering team spends more time on repetitive tasks than on building something new, you're not alone. Software development today is plagued by endless code migrations, bug fixes that never stop piling up, and test suites that constantly need updating. These tasks are necessary but often consume the best hours of your most talented engineers—who should be focused on innovation, not maintenance.

This is exactly the problem Devin was built to solve.

Devin is the world's first end-to-end AI software engineer. Unlike other AI coding assistants that can autocomplete snippets or suggest fixes, Devin can independently handle the entire software engineering lifecycle: writing code, running it, testing it, and delivering working solutions. If a human developer can complete a task in 3 hours, Devin likely can too—and it works around the clock without fatigue.

What makes Devin different is its autonomous approach. It doesn't just assist you; it works independently on your behalf. You assign it a task through your existing workflow—whether it's a Linear ticket, a Slack message, or a GitHub issue—and Devin gets to work, figuring out the implementation details, handling the edge cases, and delivering results.

This capability has already transformed how leading companies work. Nubank, Brazil's largest fintech company, uses Devin to handle their massive engineering workload. They're not a small startup—Nubank has over 1,000 engineers managing a code base that spans millions of lines. With Devin, they've achieved 8 to 12 times efficiency improvement in engineering tasks and saved over 20 times in costs. That's not a small optimization; it's a fundamental shift in how software gets built.

Devin serves as an AI software engineer that integrates directly into your existing tools. Whether your team uses GitHub for code review, Linear for project management, or Slack for communication, Devin fits naturally into your workflow. It creates pull requests, responds to comments, and keeps everyone informed—all without requiring you to learn a new platform.

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Devin 的核心功能

Every feature in Devin is designed around one goal: getting software engineering work done without requiring constant human oversight. Here's what you can actually use.

Autonomous Task Completion is Devin's core capability. You give it a task—whether it's implementing a new feature from a Linear ticket, fixing a bug reported in Jira, or rebuilding an outdated API—and Devin handles the entire process. It writes the code, runs tests to verify functionality, and delivers a working solution. This isn't AI-assisted coding where you guide every step; Devin figures out the implementation details independently. For most tasks that a human can complete in 3 hours, Devin can handle them successfully.

Devin IDE gives you real-time visibility into what Devin is working on. This isn't a separate tool you need to learn—it's embedded within your workflow. You can watch Devin work through complex implementations, pause to review specific code changes, or even jump in to make direct edits when needed. It's designed for the moments when you need to collaborate rather than just delegate.

Ask Devin transforms how you interact with your codebase. Instead of spending hours reading through files to understand how something works, you can ask Devin directly. It understands your entire code repository and provides context-aware answers rooted in your actual implementation. Whether you're trying to understand a legacy module or troubleshoot an intermittent issue, Devin gives you answers, not just links.

Devin API enables programmatic control for teams that want to integrate Devin into their automated workflows. You can trigger Devin sessions directly from your CI/CD pipelines, batch-process multiple tasks, or build custom integrations. The API itself is free to use—it simply consumes ACU (Agent Compute Units) like any other Devin operation.

Continuous Learning means Devin gets better at understanding your team's specific context over time. It learns your code conventions, understands your team's patterns, and builds a mental model of your codebase. The results speak for themselves: Nubank found that after fine-tuning Devin on their specific patterns, task completion scores doubled. This isn't generic AI—it's your personalized engineering assistant.

Scheduling & Management (released March 2026) takes Devin to the next level for规模化 work. Now you can deploy multiple Devins to work in parallel, coordinating them to handle large-scale migrations, batch bug fixes, or concurrent feature development. This is particularly valuable for enterprises with significant engineering throughput.

Each of these features translates directly to time saved and quality improved. You're not getting more tools—you're getting more done.

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谁在使用 Devin

Devin isn't a one-size-fits-all solution. Different teams use it differently depending on their needs. Let's look at who benefits most and how.

Code Migration & Refactoring teams see the most dramatic results. If you've ever upgraded a JavaScript codebase to TypeScript, migrated from Angular 16 to 18, or tackled a major framework migration, you know this work is tedious but mechanically straightforward—the kind of work that's perfect for automation. Devin handles these migrations autonomously, freeing your engineers to focus on the architectural decisions that require human judgment. Nubank's migration of 600+ lines of code across their data infrastructure—work that was originally estimated at 18 months—was completed in just weeks using Devin.

Data Engineering & Analytics teams use Devin to build and maintain data pipelines. Moving from one data warehouse to another, building ETL processes, cleaning and preprocessing data—these are tasks that require precision and repetition. Devin executes them reliably, which is exactly what you need when dealing with terabytes of data and complex transformations. The 100,000+ data classes Nubank migrated? Devin handled that workload without the team having to manually touch each one.

Bug Fixing & Ticket Management is where many teams first see Devin's value. Every engineering team has a backlog of issues—some critical, some minor, many duplicates. Devin integrates directly with Linear and Jira, so you can simply tag a ticket with Devin and it starts working. It investigates the issue, identifies the root cause, implements a fix, and updates the ticket. For teams drowning in bugs, this is transformative. Engineers stop being ticket processors and start being actual engineers again.

Application Development teams use Devin to accelerate feature delivery. Whether it's building new functionality from scratch, fixing frontend bugs, writing unit tests, or creating end-to-end test coverage, Devin handles the implementation work while you focus on design and review. The result: faster releases with better test coverage.

Technical Debt Cleanup is unglamorous but essential. Accumulated lint errors, code style violations, deprecated patterns—these small issues add up and create technical debt that slows everyone down. Devin batch-processes these fixes systematically, improving code quality across your codebase without requiring engineers to spend their time on cleanup work.

New Repository Onboarding becomes dramatically faster when new engineers can actually understand the codebase they're joining. Instead of spending weeks reading through documentation that may be outdated, new team members can ask Devin to explain how the critical systems work. Devin provides contextual explanations based on the actual code, not outdated wikis.

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技术架构与特性

Understanding what powers Devin helps you evaluate whether it's the right fit for your technical needs.

The End-to-End Agent Architecture is Devin's foundation. Unlike copilots that work alongside you, Devin operates as a complete software engineering agent. It has its own IDE, shell access, and browser capabilities built in. When you give it a task, it can navigate your codebase, make changes, run tests, debug issues, and verify results—all without requiring constant hand-holding. This architecture is what enables the 8-12x efficiency gains teams consistently report.

The MCP (Model Context Protocol) Marketplace extends Devin's capabilities through a growing ecosystem of integrations. With support for 100+ MCP servers, Devin connects to the tools your team already uses: project management platforms like Asana and Notion, payment systems like Stripe, cloud infrastructure like AWS, monitoring tools like Datadog, and data platforms like Snowflake. This isn't a closed system—it's designed to fit into your existing stack.

Codebase Indexing & Understanding is what makes Devin truly contextual. It doesn't just look at the file you're working on—it builds a comprehensive understanding of your entire repository. This means when it implements a feature, it knows about your existing patterns, understands your dependencies, and produces code that fits naturally with what you already have. This is critical for avoiding the "AI generates code that technically works but doesn't fit your project" problem.

The Integration Ecosystem connects Devin with your daily tools. GitHub integration means Devin creates pull requests, responds to review comments, and handles code review tasks independently. Linear integration lets you assign tickets directly through labels—add the Devin label and it starts working. Slack and Teams integration lets you @mention Devin to assign tasks and receive progress updates. These aren't bolt-on features; they're deeply embedded into how Devin operates.

Security & Compliance reflect serious enterprise requirements. Devin runs aBug Bounty program through Bugcrowd with a commitment to respond to security reports within 48 hours. More importantly, you own everything Devin produces. All code generated is your intellectual property—the input you provide and the output Devin creates belongs to you, not to Cognition. This is crucial for enterprises with strict IP requirements.

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定价与使用额度

Devin offers three pricing tiers designed for different team sizes and needs. Understanding how pricing works starts with understanding ACU—Agent Compute Unit.

ACU (Agent Compute Unit) is Devin's work measurement. Instead of charging per minute or per API call, Devin measures the actual computational work done: virtual machine time, model inference, network bandwidth, and other resources consumed while actively executing tasks. When Devin is running a task or its virtual machine is active, it consumes ACU. When idle, it doesn't. This means you pay for actual work done, not seat time.

Here's how the plans compare:

Core Plan is the entry point at $20 minimum (prepaid). You get unlimited users, up to 10 concurrent sessions, and automatic recharge when your balance runs low. At $2.25 per ACU, it's flexible for teams that want to test Devin on specific projects before committing to a larger plan. The math is simple: if Devin saves your team 20 hours of work in a month, and your engineers cost $100/hour, that's $2,000 in value for a few hundred dollars in ACU.

Team Plan at $500/month includes 250 ACU (effectively $2.00 per ACU—a 11% discount). But the real value is in the Advanced Mode features: checking other Devin sessions for context, automatically creating improvement playbooks, and batch session suggestions. You also get unlimited concurrent sessions instead of the 10-session cap, plus optional onboarding calls to help your team get started effectively. For teams actively using Devin, this plan offers the best value.

Enterprise Plan is for organizations that need custom deployments. You get VPC deployment options, SAML/OIDC SSO for security, centralized administration, team workspace isolation, and a dedicated customer success team. Pricing is custom because every organization's needs are different. If you're managing thousands of engineers across multiple business units, this plan provides the control and support you need.

Our recommendation: start with Core to validate Devin works for your specific use cases. Once you see the efficiency gains and need Advanced Mode features, upgrade to Team. Enterprise is worth considering when you have security requirements that Core and Team can't meet.

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常见问题