Augment Code vs Continue
- ✦ Deep Codebase Indexing
- ✦ AI Chat
- ✦ Code Completion
- ✦ Autocomplete
- ✦ Chat Interface
- ✦ Edit Mode
Real-World Scenarios: When to Choose Which
The question that matters: “In what situation will I regret choosing A over B after 3 months?”
Augment's long-context codebase index answers questions like 'which services consume this Kafka topic?' across a monorepo, returning specific file references rather than generic answers.
A single natural language request like 'add request ID logging to all middleware' propagates consistently across every relevant file in the repository.
Augment tracks recent edits and predicts the next logical change, surfacing suggestions before the engineer navigates to the next file.
Continue routes completions through Ollama or LM Studio, giving teams autocomplete and chat on private codebases without sending any code to a cloud API.
Continue's context provider API indexes internal wikis, runbooks, or proprietary SDKs so the assistant answers questions with company-specific knowledge rather than generic web answers.
config.json specifies different models for autocomplete, chat, and edit per repository, so a Python repo uses DeepSeek-Coder while a React repo uses GPT-4o.
Pricing Intelligence
Augment Code Plans
Paid plans only
- • Unlimited completions
- • Deep context
- • VS Code + JetBrains
- • Team codebase
- • Shared context
- • Admin controls
Continue Plans
Free tier available
- • Open source
- • Bring your own model
- • VS Code & JetBrains
- • Hosted models
- • Team features
- • Priority support
Feature Matrix
2 differences found across 15 standardized features
- •Deep Codebase Indexing
- •AI Chat
- •Code Completion
- •Multi-file Context
- •VS Code Extension
- •JetBrains Plugin
- •Code Review
- •Security Scanning
- •Test Generation
- •Refactoring
- •Documentation
- •Slack Integration
- •Team Features
- •Enterprise Security
- •Local Processing
- •Autocomplete
- •Chat Interface
- •Edit Mode
- •Multi-model Support
- •VS Code Extension
- •JetBrains Plugin
- •Custom Prompts
- •Codebase Context
- •Model Switching
- •Local Model Support
- •RAG for Codebase
- •Tab Autocomplete
- •Inline Editing
- •Test Generation
- •Documentation
Pros & Cons Face-Off
Evaluative strengths and weaknesses: not feature lists
- +Best deep codebase understanding
- +Excellent for large enterprise codebases
- +Strong privacy and security controls
- +Works with both VS Code and JetBrains
- −More expensive than GitHub Copilot
- −Less known than competitors
- −Overkill for small projects
- +Works with any LLM including local models
- +Both VS Code and JetBrains support
- +Highly customizable
- +Active open-source community
- −Setup requires more configuration than Copilot
- −Quality depends heavily on chosen model
- −Enterprise features still maturing
At a Glance
Frequently Asked Questions
Related Comparisons
Sources
- 1.Augment Code Official Pricing–Vendor pricing page
- 2.Continue Official Pricing–Vendor pricing page
- 3.Augment Code Official Website–Official product website
- 4.Continue Official Website–Official product website