Augment Code
200K+ token context engine for massive codebases
About
Augment Code is an enterprise AI coding assistant built to handle the hardest problem in AI-assisted development: massive, decade-old monorepos that smaller context windows cannot process. Its proprietary 200K+ token context engine indexes and understands million-line codebases in real time, making it the only AI tool capable of working with legacy enterprise systems that other tools simply cannot fit into their context limits. Augment Code is designed for large organizations with sprawling codebases—telecom infrastructure, financial trading systems, enterprise ERP platforms, and government software. It provides code completion, AI chat, codebase-wide search, multi-file refactoring, and specialized legacy code migration assistance. The tool excels at understanding the ripple effects of changes across hundreds of interdependent modules. Pricing is credit-based, ranging from $20 to $200 per month depending on usage volume. There is no free tier—Augment is explicitly an enterprise tool for organizations where the cost of AI assistance is dwarfed by the cost of developer time on legacy systems. For teams managing complex monorepos that crash other AI tools, Augment is not just the best option—it is often the only viable one.
Key Features
- Context engine
- Code completion
- AI chat
- Codebase-wide search
- Multi-file refactoring
- Legacy code migration
Deals, Discounts & How to Save
Maximize free tiers before subscribing—most AI editors have generous free plans. Students should always apply for education discounts (often 50%+ off). If you use multiple IDEs, choose an editor with a permissive free tier to avoid paying twice.
Pros
- Handles million-line monorepos
- Massive context window (200K+ tokens)
- Real-time codebase indexing
Cons
- No free tier
- Overkill for small projects
- Newer and less proven
Pro Tips for Augment Code
Use context engine regularly—it becomes more accurate the more context it has about your coding patterns and project structure.
Configure code completion in your project settings to match your team conventions—consistent configuration produces better results.
Combine ai chat with keyboard shortcuts to minimize context switching between thinking and coding.
Use codebase-wide search regularly—it becomes more accurate the more context it has about your coding patterns and project structure.
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