Augment Code vs JetBrains AI: Which is Best for Your Codebase?
August 28, 2025
TL;DR
Augment Code delivers whole-system intelligence through its Context Engine, which uses real-time semantic understanding to analyze code structure, dependencies, and architectural patterns across your entire codebase. JetBrains AI Assistant provides deep native IDE integration with cloud-based AI features and optional local processing across 11 JetBrains IDEs plus VS Code.
Choose Augment Code for enterprise-scale codebase intelligence with verified SOC 2 Type II and ISO/IEC 42001 certifications. Choose JetBrains AI Assistant for seamless workflow integration within the JetBrains ecosystem with optional local model support and free tier access.
Both Augment Code and JetBrains AI Assistant help developers write code faster, but they approach the problem from opposite directions. Augment Code's Context Engine analyzes the full development context across your entire codebase to generate completions that understand your architecture. JetBrains AI Assistant integrates deeply into the JetBrains IDE ecosystem, extending familiar workflows that developers already use daily.
Enterprise teams face mounting pressure to adopt AI coding tools while navigating security requirements and standardizing on tools. This guide helps technical decision-makers understand the practical differences through direct feature comparisons and persona-based recommendations.
Augment Code vs JetBrains AI at a Glance
The fundamental difference is architectural philosophy. Augment Code's Context Engine reasons about your entire system, understanding how components interact across repositories. JetBrains AI Assistant provides a deep understanding of the currently loaded IDE project using RAG-based context collection.
The comparison table below covers context capabilities, benchmark performance, IDE support, security certifications, and pricing structures that most directly impact enterprise adoption decisions.
| Feature | Augment Code | JetBrains AI Assistant |
|---|---|---|
| Context Approach | Semantic understanding across the entire codebase | Project-scoped with RAG-based retrieval |
| Benchmark | 70.6% SWE-bench Verified (Sonnet 4) | No public benchmark has been published |
| IDE Support | VS Code, JetBrains, Vim/Neovim, CLI | 11 JetBrains IDEs + VS Code |
| Security | SOC 2 Type II, ISO 42001, CMEK | Optional local processing; no formal AI attestations |
| Pricing | $20/mo, $60/mo, $200/mo tiers | Free tier; $10/mo Pro, $20/mo Ultimate |
Key Differences: Augment Code vs JetBrains AI
Understanding how each tool handles core capabilities clarifies which platform fits specific development workflows and organizational requirements. The sections below examine context understanding, IDE integration, and security approaches in detail.
Context Understanding and Code Awareness
Context scope determines whether AI assistance operates at the file level or understands entire system architectures. Augment Code's Context Engine surfaces design-level patterns and maintains long-term architectural awareness, reasoning about how changes in one file affect components elsewhere.
JetBrains AI Assistant leverages the IDE's semantic indexing and RAG-based retrieval to provide deep understanding within the currently loaded project. The 2025.1 release introduced advanced RAG-based context awareness that automatically surfaces relevant files, methods, and classes.
For teams debugging payment flows that span multiple microservices or implementing features that require cross-repository coordination, Augment Code's system-wide intelligence provides architectural-level insights. JetBrains AI Assistant excels within single-repository boundaries where deep IDE integration matters more than cross-repository reasoning.
IDE Integration and Developer Experience
IDE integration strategy affects daily workflow friction and adoption velocity. JetBrains AI Assistant integrates seamlessly into IntelliJ IDEA, PyCharm, WebStorm, GoLand, PhpStorm, RubyMine, CLion, DataGrip, DataSpell, Rider, and RustRover. AI-suggested changes appear in the same diff viewer used for IDE refactorings, making the experience feel native because it extends existing inspection and code analysis tools developers already know.
Augment Code offers broader tool compatibility through verified integrations with VS Code, JetBrains IDEs, Vim/Neovim, CLI, and Slack. Teams with mixed editor preferences benefit from consistent AI assistance regardless of individual tool choices. Initial setup requires workspace indexing through "augment init", but subsequent sessions leverage pre-built context and dependency graphs for faster responses.
Security and Compliance
Enterprise procurement requires transparent data handling policies and verifiable compliance certifications. Augment Code maintains SOC 2 Type II and ISO/IEC 42001 certifications with customer-managed encryption keys (CMEK) for organizations requiring data sovereignty controls. Independent auditors verify the complete AI pipeline from model training to code suggestions. The company never trains on customer code.
JetBrains AI Assistant offers optional local processing through Ollama integration and its proprietary Mellum model, enabling teams to run code completions on-premises. However, advanced AI features beyond local completions may still leverage cloud services.
This approach suits privacy-conscious teams seeking additional control, though it does not eliminate cloud dependencies for all capabilities. JetBrains does not currently publish formal security attestations for the AI Assistant product, though its other products hold various compliance certifications.
Feature-by-Feature Comparison: Augment Code vs JetBrains AI
Beyond core differentiators, setup workflows, pricing structures, and model options significantly impact adoption decisions. The sections below examine practical considerations for team deployment and ongoing usage.
Setup and Usability
JetBrains AI Assistant enables a single plugin toggle in any JetBrains IDE version 2023.3 or later, requiring no additional setup for immediate use. The 2025.1 release introduced a free tier with unlimited code completion and local AI support, plus a limited cloud quota for advanced features. Developers already using JetBrains products can start immediately without workflow changes or configuration.
Augment Code requires initial repository indexing through the CLI or IDE extension. The setup process runs "augment init," which processes workspace files to build the Context Engine before providing AI assistance. For large codebases, initial indexing takes longer but enables subsequent queries to leverage precomputed dependency graphs and cross-repository relationships that update in real time as code changes.
Pricing and Cost Efficiency
Augment Code uses credit-based pricing with three individual tiers: $20/month (Indie with 40,000 credits), $60/month (Standard with 130,000 credits), and $200/month (Max with 450,000 credits), plus custom Enterprise options with volume discounts. All tiers include SOC 2 Type II compliance, and credits never expire during an active subscription. Auto top-up is available at $15 per 24,000 credits for teams that need additional capacity.
JetBrains AI offers a free tier with unlimited code completion and local AI support, plus a limited quota for cloud-based features. Paid tiers include AI Pro ($10/month) and AI Ultimate ($20/month with bonus credits). The AI Pro tier is included with the All Products Pack subscription, making it effectively free for teams already paying for JetBrains tools. The August 2025 quota model update aligned credits with subscription price, where 1 AI Credit equals $1 USD.
Model Options and Flexibility
JetBrains AI Assistant provides explicit model selection, including GPT-4o, GPT-4.1, Claude 3.7 Sonnet, Gemini 2.5 Pro, and their proprietary Mellum model for local processing. Teams can choose models based on task requirements or compliance constraints, switching between cloud and local options as needed.
Augment Code uses intelligent model routing via its Context Engine, automatically selecting the optimal models for each task. The platform currently leverages Claude Sonnet 4, which improved SWE-bench performance from 60.6% to 70.6% compared to Claude 3.7. Rather than exposing model selection, Augment focuses on delivering consistent results through its context-aware architecture.
What Users Like: Augment Code vs JetBrains AI
User feedback reveals practical outcomes in daily coding tasks, from handling complex refactorings to maintaining workflow consistency. These insights, drawn from developer reviews across professional networks and software review platforms as of late 2025, highlight how each tool performs under real workloads rather than marketed features.
Augment Code User Feedback
Developers report Augment Code excels at navigating large, interconnected codebases, providing step-by-step guidance that feels like collaborating with a senior engineer. Users note reduced time spent on debugging and refactoring across multiple files, with the tool effectively tracing dependencies during SDK migrations or feature implementations spanning services. Challenges arise with support responsiveness for billing issues, though the core AI capabilities consistently deliver value in production settings.
- Maintains architectural awareness in messy legacy code, accelerating complex refactors
- Generates reliable tests and reasons through long code chains across files
- Handles CLI interactions like git commands and Docker diagnostics autonomously
- Enables new conversations per task to manage context without overload
JetBrains AI Assistant User Feedback
Users describe JetBrains AI Assistant as a natural extension of familiar IDE tools, minimizing disruption for teams already invested in JetBrains ecosystems. Feedback emphasizes seamless integration that enhances existing inspections and refactors, leading to faster adoption without setup friction. Local processing options appeal to privacy-focused workflows, though some note limitations in cross-repository tasks.
- Feels native in different viewers and refactoring tools, boosting daily productivity
- Provides unlimited local completions in the free tier for quick iterations
- Surfaces relevant code elements via RAG within loaded projects reliably
- Supports model switching for task-specific performance without reconfiguration
| Aspect | Augment Code | JetBrains AI Assistant |
|---|---|---|
| Complex Refactors | Strong (multi-file dependency tracing) | Moderate (project-scoped awareness) |
| Workflow Friction | Initial indexing, then fast | Minimal (native IDE extension) |
| Support Reliability | Mixed (product praised, billing issues noted) | Consistent (plugin-based access) |
| Privacy Options | Enterprise certs (SOC 2, ISO 42001) | Optional local Ollama/Mellum processing |
Augment Code benefits teams tackling distributed microservices or regulated environments that need deep context, while JetBrains AI Assistant suits standardized JetBrains users who prioritize frictionless, local-first experiences.
Who Is Each Tool Best For?
Selecting between Augment Code and JetBrains AI Assistant depends on team workflows, codebase architecture, and compliance requirements. The recommendations below focus on outcomes rather than feature lists.
Who Augment Code Is Best For
- Enterprise architecture teams managing microservices, polyglot systems, and code spread across multiple repositories where understanding cross-service impacts is critical for daily decisions
- Compliance-heavy organizations in finance, healthcare, and regulated industries requiring SOC 2 Type II and ISO/IEC 42001 certifications with customer-managed encryption keys
- Teams debugging complex flows that span different services owned by multiple teams, where system-wide context prevents hours of manual investigation
- Procurement teams needing verifiable third-party attestations rather than vendor self-declarations to satisfy enterprise security review requirements
Who JetBrains AI Assistant Is Best For
- JetBrains-standardized teams committed to IntelliJ IDEA, PyCharm, WebStorm, or other JetBrains IDEs who want AI assistance that extends familiar workflows
- Privacy-focused development teams seeking optional local AI processing through Ollama integration and the proprietary Mellum model for code completions
- Cost-conscious teams and startups benefiting from the free tier with unlimited local code completion
- Developers valuing native integration who already rely on JetBrains inspections, refactoring tools, and keyboard shortcuts
Ship Faster When Your AI Understands the Whole System
Most AI coding tools lose context mid-refactor, invent functions that don't exist, and force developers to re-explain the project structure with every conversation. These limitations compound in large codebases where a single API change cascades through dozens of files across multiple services. Teams waste hours debugging AI-generated code that compiles but breaks production because the tool never understood how components interact.
Tired of AI assistants that forget your codebase exists? Augment Code's Context Engine maintains architectural awareness across your entire system, so suggestions align with how your code actually works. Try a free trial of Augment Code and test it on your most complex multi-file refactor.
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Molisha Shah
GTM and Customer Champion