Continue vs Aider

Detailed side-by-side comparison to help you choose the right tool

Continue

🔴Developer

AI Coding

Open-source AI coding extension for VS Code and JetBrains — bring any model, configure custom rules, share assistants across your team.

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Starting Price

Custom

Aider

🔴Developer

AI Development Assistants

Free, open-source AI coding tool that edits files directly in your terminal with automatic git commits. Works with Claude, GPT-4o, DeepSeek, and local models.

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Starting Price

Free

Feature Comparison

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FeatureContinueAider
CategoryAI CodingAI Development Assistants
Pricing Plans36 tiers18 tiers
Starting PriceFree
Key Features
  • Multi-model AI support including OpenAI, Claude, Gemini, and local models
  • Native IDE extensions for VS Code and JetBrains with smooth workflow integration
  • MCP server connectivity for development toolchain integration
  • Direct code file editing across multiple files in a single operation
  • Automatic git commits with meaningful messages for every change
  • Repository mapping for whole-codebase understanding of architecture and dependencies

Continue - Pros & Cons

Pros

  • Open-source under Apache 2.0 — no vendor lock-in or proprietary protocols
  • Genuinely supports JetBrains as a first-class citizen, not an afterthought
  • YAML config and Continue Hub make team-wide standardization trivial
  • Transparent pricing: $3/M tokens Starter, $20/seat/month Team — no hidden seat costs
  • Mix local and hosted models in one extension — cheap autocomplete, expensive chat
  • Strong on-prem story for regulated enterprises (BYOK, internal proxies, no telemetry)

Cons

  • Less polished UX than closed-source competitors like Cursor or GitHub Copilot
  • Agent mode is younger than Cline's and has fewer guardrails
  • Hub assistants quality varies — vetting community configs takes effort
  • $3/M tokens Starter is competitive but not the cheapest if you BYO API keys directly

Aider - Pros & Cons

Pros

  • Completely free and open-source with no feature gating or usage limits
  • Direct file editing eliminates the copy-paste cycle of suggestion-based tools
  • Automatic git commits create a clean, reviewable history of every AI change
  • Model-agnostic: use whichever LLM fits the task and budget, including local models for free
  • Repo mapping enables complex multi-file refactoring that simpler tools cannot handle
  • Terminal-native works everywhere: local dev, SSH sessions, CI environments, any OS

Cons

  • Requires terminal comfort; no GUI available for developers who prefer visual interfaces
  • Direct file editing demands more trust than suggestion-based tools (though git makes reverting easy)
  • Initial setup requires configuring API keys for your chosen LLM provider
  • No inline code suggestions or visual diffs like IDE-based assistants (Copilot, Cursor)
  • LLM costs are separate and can add up during heavy refactoring sessions ($5-20/day with cloud models)

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