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| Pricing | Free | Paid |
| Capabilities | 15 decomposed | 15 decomposed |
| Times Matched | 0 | 0 |
Analyzes user requests against the entire codebase using semantic filtering (reducing 4,456+ sources to 682 relevant ones) and generates numbered, actionable task lists before any code execution. Users can add, skip, or modify steps before the agent proceeds. This plan-first approach enables structured multi-file changes while maintaining human oversight at the decision point, not just execution point.
Unique: Generates explicit, user-editable task plans before execution rather than streaming changes or using implicit chain-of-thought reasoning. Combines semantic codebase filtering (84.7% context reduction) with goal decomposition, allowing users to modify the plan mid-generation before any files are touched.
vs alternatives: Unlike Cursor or Claude Code which stream changes immediately, Augment Code surfaces the full plan first, enabling teams to enforce approval workflows and catch architectural issues before implementation begins.
Executes planned tasks sequentially while creating checkpoints at each step, allowing users to accept changes, revert to any prior checkpoint, or redirect the agent mid-task without losing work. Each checkpoint captures file state and execution context, enabling granular rollback without manual version control. Integrates with Git for version tracking but provides finer-grained undo than traditional commits.
Unique: Implements a checkpoint system that captures state at each task step, enabling granular rollback and mid-task redirection without requiring manual Git operations. This is distinct from traditional undo (which is linear) and commit-based versioning (which is coarse-grained).
vs alternatives: Provides finer-grained control than Cursor's streaming changes or Claude Code's batch edits — users can accept/reject individual steps and redirect the agent without losing prior work or requiring manual Git resets.
Allows users to create and maintain workspace Rules — persistent, user-approved memory items that capture project-specific patterns, conventions, and decisions. Rules are stored in the workspace and applied across all agent sessions, enabling the agent to learn from user feedback without automatic memory accumulation. Users explicitly approve, edit, or discard each memory before it's saved.
Unique: Implements explicit user-curated memory via workspace Rules, requiring user approval before persistence. This trades automation for transparency and control — users decide what the agent learns rather than relying on implicit learning.
vs alternatives: Unlike Cursor or Copilot which have implicit context learning, Augment Code surfaces all memory decisions to users for explicit approval, enabling teams to enforce consistent learning and prevent unwanted pattern adoption.
Uses a credit-based consumption model where tasks consume credits based on complexity and resource usage. Credits are purchased in tiers (Indie: 40k/month, Standard: 130k/month, Max: 450k/month) with auto top-up at $15 per 24k credits. Credits are consumed by agent execution and code review tasks. The exact credit-to-token mapping and per-task cost estimation are not published.
Unique: Implements credit-based consumption tied to agent execution and code review, with tiered monthly allocations and auto top-up. This differs from per-seat licensing (GitHub Copilot) or token-based pricing (OpenAI API) by abstracting consumption into a proprietary credit system.
vs alternatives: More flexible than GitHub Copilot's per-seat model (which charges regardless of usage) but less transparent than OpenAI's token-based pricing (which directly maps to computational cost).
Provides native plugins for VS Code and JetBrains IDEs (IntelliJ, PyCharm, etc.) that embed the agent directly into the development environment. Users interact with the agent through IDE UI elements (sidebar, inline suggestions, context menus) without leaving their editor. The plugin architecture maintains local IDE state while communicating with the cloud-hosted agent.
Unique: Provides native IDE plugins that embed the agent directly into VS Code and JetBrains IDEs, maintaining local IDE state while communicating with cloud-hosted agent. This differs from web-based interfaces or CLI tools by integrating into the developer's primary workflow.
vs alternatives: More integrated than Cursor (which is a separate editor) or Copilot (which uses IDE extensions but less deeply) — Augment Code plugins provide first-class IDE integration with native UI elements.
Provides Augment CLI, a terminal-based interface to the agent that uses the same Context Engine and planning logic as the IDE plugins. Enables developers who prefer terminal workflows to use the agent without opening an IDE. CLI supports piping, scripting, and CI/CD integration.
Unique: Provides a CLI interface to the same agent backend as IDE plugins, enabling terminal-first workflows and CI/CD integration. The CLI uses the same Context Engine and planning logic, ensuring consistency across interfaces.
vs alternatives: Unlike Cursor or Copilot which are GUI-first, Augment Code CLI enables terminal-based workflows and CI/CD integration without IDE dependency.
Provides enterprise-grade security features including SOC 2 Type II compliance, CMEK (Customer-Managed Encryption Keys), ISO 42001 compliance, SIEM integration, data residency options, granular access controls, comprehensive audit trails, and enterprise SSO (OIDC, SCIM). These features are available on Enterprise tier and ensure data protection, regulatory compliance, and organizational control.
Unique: Provides comprehensive enterprise security features including CMEK, SOC 2 Type II, ISO 42001, SIEM integration, and enterprise SSO. These features are bundled in Enterprise tier, enabling organizations to meet strict compliance and security requirements.
vs alternatives: GitHub Copilot and Cursor lack explicit enterprise security features — Augment Code's Enterprise tier provides compliance certifications, CMEK, and SIEM integration for regulated industries.
Maintains a 'live understanding' of the entire codebase by indexing code, dependencies, architecture, and history, then performs semantic filtering to surface only relevant context (reducing 4,456+ sources to 682 relevant ones per example). Uses a proprietary Context Engine to determine relevance without exposing the filtering mechanism. Stores user-approved memories as workspace Rules that persist across sessions.
Unique: Uses proprietary semantic filtering to reduce codebase context by 84.7% (4,456 → 682 sources) while maintaining relevance, combined with explicit user-curated workspace Rules that persist across sessions. The filtering approach (vector-based, AST-based, or hybrid) is undisclosed but claims to improve token efficiency without losing critical context.
vs alternatives: Unlike Cursor or Copilot which rely on implicit context selection or token budgets, Augment Code explicitly surfaces filtered context and allows users to curate persistent Rules, trading some automation for transparency and control.
+7 more capabilities
Devin autonomously navigates and analyzes codebases by reading file structures, parsing dependencies, and building semantic understanding of code organization without explicit user guidance. It uses agentic reasoning to identify key files, trace execution paths, and understand architectural patterns through iterative exploration rather than requiring developers to manually point it to relevant code sections.
Unique: Uses multi-turn agentic reasoning with tool-use (file reading, grep-like search, dependency parsing) to autonomously build codebase mental models rather than relying on static indexing or developer-provided context — treats codebase exploration as a reasoning task
vs alternatives: Unlike GitHub Copilot which requires developers to manually navigate to relevant files, Devin proactively explores and reasons about codebase structure, reducing context-setting friction for large projects
Devin breaks down high-level software engineering tasks into concrete subtasks, creates execution plans with dependencies, and reasons about optimal ordering and resource allocation. It uses planning-reasoning patterns to identify prerequisites, estimate complexity, and adapt plans based on intermediate results without requiring explicit step-by-step instructions from users.
Unique: Combines multi-turn reasoning with codebase analysis to create context-aware task plans that account for actual code dependencies and architectural constraints, rather than generic task-splitting heuristics
vs alternatives: More sophisticated than simple prompt-based task lists because it reasons about code structure and dependencies; more autonomous than Copilot which requires developers to manually break down tasks
Devin analyzes project dependencies, identifies outdated or vulnerable packages, and autonomously updates them while ensuring compatibility and functionality. It uses dependency graph analysis to understand impact of updates, runs tests to validate compatibility, and generates migration code if breaking changes are detected.
Augment Code scores higher at 56/100 vs Devin at 42/100. Augment Code also has a free tier, making it more accessible.
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Unique: Autonomously manages dependency updates with compatibility validation and migration code generation, treating dependency updates as a reasoning task rather than simple version bumping
vs alternatives: More comprehensive than Dependabot because it handles breaking changes and generates migration code; more autonomous than manual updates because it validates and fixes compatibility issues
Devin analyzes code to identify missing error handling, generates appropriate exception handlers, and improves error management by reasoning about failure modes and recovery strategies. It uses code analysis to understand where errors might occur and generates context-appropriate error handling code.
Unique: Analyzes code to identify failure modes and generates context-appropriate error handling, treating error management as a reasoning task rather than applying generic patterns
vs alternatives: More comprehensive than static analysis tools because it reasons about failure modes; more effective than manual error handling because it systematically analyzes all code paths
Devin identifies performance bottlenecks by analyzing code complexity, running profilers, and reasoning about optimization opportunities. It generates optimized code, applies algorithmic improvements, and validates performance gains through benchmarking without requiring developers to manually identify optimization targets.
Unique: Uses profiling data and code analysis to identify optimization opportunities and generate improvements, treating optimization as a reasoning task with empirical validation
vs alternatives: More targeted than generic optimization heuristics because it uses actual profiling data; more autonomous than manual optimization because it identifies and implements improvements automatically
Devin translates code between programming languages by analyzing source code semantics, mapping language-specific constructs, and generating functionally equivalent code in target languages. It handles language idioms, library mappings, and type system differences to produce idiomatic target code rather than literal translations.
Unique: Translates code semantically while adapting to target language idioms and conventions, rather than performing literal syntax translation — produces idiomatic target code
vs alternatives: More effective than simple transpilers because it understands semantics and idioms; more maintainable than manual translation because it handles systematic conversion automatically
Devin generates infrastructure-as-code and deployment configurations by analyzing application requirements, understanding deployment targets, and generating appropriate configuration files. It creates Docker files, Kubernetes manifests, CI/CD pipelines, and infrastructure code that matches application needs without requiring manual specification.
Unique: Analyzes application requirements to generate deployment configurations that match actual needs, rather than applying generic infrastructure templates
vs alternatives: More comprehensive than infrastructure templates because it understands application-specific requirements; more maintainable than manual configuration because it generates consistent, validated configs
Devin generates code that respects existing codebase patterns, style conventions, and architectural constraints by analyzing surrounding code and project structure. It uses tree-sitter or similar AST parsing to understand code structure, applies pattern matching against existing implementations, and generates code that integrates seamlessly rather than producing isolated snippets.
Unique: Analyzes codebase ASTs and architectural patterns to generate code that integrates with existing structure, rather than producing generic implementations — uses codebase as a style guide and constraint system
vs alternatives: More context-aware than Copilot's line-by-line completion because it reasons about multi-file architectural patterns; more autonomous than manual code review because it proactively ensures consistency
+7 more capabilities