Devin
PaidThe world's first autonomous AI software engineer by Cognition Labs. Handles full development tasks including planning, coding, debugging, and deployment.
What does this tool do?
Devin is an autonomous AI software engineer developed by Cognition Labs that handles end-to-end development workflows including planning, coding, debugging, and deployment. Rather than generating code snippets like traditional AI coding assistants, Devin operates as an independent agent capable of understanding complex project requirements, making architectural decisions, and completing substantial coding tasks with minimal human intervention. The tool can be fine-tuned on domain-specific patterns and examples, enabling it to handle repetitive refactoring work, migrations, and engineering tasks at scale. The Nubank case study demonstrates its practical application: the company deployed Devin to migrate millions of lines of code across data class implementations, reducing what would have been an 18-month effort involving 1,000+ engineers down to weeks, with engineers only reviewing and approving changes rather than performing the manual work themselves.
AI analysis from Feb 23, 2026
Key Features
- Autonomous planning and task decomposition for complex development projects without step-by-step prompting
- Fine-tuning capability using organization-specific code examples to dramatically improve performance on domain-specific patterns and variations
- Integrated debugging and error handling that learns from mistakes across multiple attempts on similar tasks
- Code generation, refactoring, and migration capabilities with dependency tracing and multi-file coordination
- Human-in-the-loop workflow where the tool submits changes for engineer review and approval rather than direct deployment
- Tool creation and optimization—Devin builds its own scripts and utilities to automate repetitive mechanical components of larger tasks
- Enterprise deployment options with documentation and customer support infrastructure
Use Cases
- 1Large-scale code refactoring and architecture migrations where repetitive patterns can be learned and applied across thousands of similar tasks
- 2Data transformation pipelines and ETL system modernization with consistent, discretionary decision-making requirements
- 3Bug fixing and debugging in established codebases where Devin can trace dependencies and implement fixes autonomously
- 4Feature development and implementation for well-defined requirements where the tool can plan implementation steps and handle edge cases
- 5Legacy code modernization and framework upgrades involving systematic changes across multiple files and modules
- 6Automated deployment and infrastructure management tasks that require integrated planning and execution
- 7Project acceleration in organizations with scarce engineering resources where human review of AI-generated changes is feasible
Pros & Cons
Advantages
- Dramatic efficiency gains demonstrated at scale—8-12x faster completion on complex refactoring with 20x cost savings compared to human engineers
- Autonomous task completion with human oversight through code review, reducing engineering hours while maintaining quality control through the approval process
- Fine-tuning capability that dramatically improves performance on domain-specific tasks—Nubank saw 4x speed improvement and doubled task completion scores after fine-tuning on migration examples
- Learning and improvement over time as the tool encounters more examples, avoiding rabbit holes and finding faster solutions to previously-seen edge cases much like an experienced engineer would
Limitations
- Pricing details not publicly available on the marketing website, making cost-benefit analysis difficult for prospective customers without contacting sales
- Requires significant upfront investment to collect training examples and fine-tune the model for domain-specific tasks, which may not be practical for organizations without dedicated ML/AI teams
- Complex, discretionary tasks with high variation in requirements may still need human engineers for initial problem definition and teaching Devin the approach
- Effectiveness heavily dependent on clear requirements and well-defined patterns; ambiguous or novel problems lacking historical examples may result in lower completion rates
Pricing Details
Pricing details not publicly available.
Who is this for?
Engineering leaders and CTO offices at mid-to-large enterprises facing large-scale code migrations, refactoring, or modernization projects. Best suited for organizations with repetitive coding patterns, substantial technical debt, or constrained engineering resources. Also relevant for teams managing legacy system modernization, ETL transformations, and infrastructure-heavy projects where clear patterns can be established and human review capacity exists.