AI 코딩 도구를 선택하는 것이 어려운 이유 (그리고 올바른 도구를 선택하는 방법)

When you use AI coding assistants, you eventually run into the same question:

“Which tool actually fits me?”

GitHub Copilot, Cursor, Claude, Replit — they all seem similar in that “AI writes code for you.” But once you actually use them, what they’re good at turns out to be completely different.

Short function autocompletion? Any of them works fine. But multi-file changes, testing, fitting into an existing codebase? The differences become obvious fast.

“Fit for your workflow” matters more than “number of features”

A developer’s day isn’t just writing code. The thinking before it, the testing after it, the revising that never ends — the actual time spent writing code might be the smallest part of the whole thing.

G2 put together a comparison of the best AI coding assistants for 2026, evaluating tools beyond simple autocomplete — looking at codebase understanding, context switching, debugging, and agentic capabilities.

Different tools fit different developers

No single tool is universally best. For example:

• AWS-native development → Amazon Q Developer
• Legacy enterprise/mainframe modernization → IBM watsonx Code Assistant
• Long-context reasoning for full-stack work → Claude
• Context-aware IDE experience → Cursor
• Broad language/framework support → GitHub Copilot
• Build and deploy without a local setup → Replit

The question isn’t “which tool is best” — it’s “which tool fits my workflow.”

Tools covered

GitHub Copilot, Replit, Gemini, Amazon Q Developer, IBM watsonx Code Assistant, Claude, Cursor, and SoftSpell.

For the full breakdown including strengths, weaknesses, and real user reviews:https://learn.g2.com/best-ai-coding-assistants