Best AI IDEs and Editors for 2025

AI IDEs in 2025 revolve around repo awareness, low-latency context, and safe collaboration. This guide compares Cursor, Windsurf, VS Code with Copilot, JetBrains AI, and how research assistants like Google Antigravity complement deep code understanding without touching source. Pair whichever IDE you choose with Propel Code so PR review, policies, and evidence stay consistent. It links to deeper dives on AI code review and AI test generation.

What makes an AI IDE great

The best AI IDEs balance speed, privacy, and explainability. Local indexing keeps latency low and prevents unnecessary data egress. Clear diffs and rationale build trust in suggested changes.

Cursor

Cursor leads for repo-scale context and inline edits. It handles multi-file refactors and test generation well. Set branch protections so AI commits still pass review and CI.

Windsurf

Windsurf emphasizes navigation speed and lightweight context windows. It is effective for frontend workflows and smaller services. Pair with a review tool like Propel for policy enforcement.

VS Code with Copilot

VS Code remains the extension-rich hub. Copilot helps with boilerplate and test scaffolds. Combine it with typed linting to keep suggestions aligned with your standards.

JetBrains AI

JetBrains AI shines for JVM and polyglot monorepos. It blends static analysis with AI completions, which reduces noisy suggestions in strongly typed codebases.

Using Antigravity responsibly

Keep Antigravity on the research side: summarizing RFCs, spec docs, and vendor changelogs. Do not feed proprietary code. Treat it as a design brief helper that can inform your IDE work without direct code access.

Team rollout checklist

• Decide which IDEs are supported and document onboarding.
• Set data policies and disable training where possible.
• Require PR review with Propel before merges.
• Track metrics: time to first review, review latency, merge success.
• Collect feedback weekly to tune prompts and disable noisy features.

Performance and privacy hardening tips

• Enable local indexing to reduce latency and avoid unnecessary data egress.
• Scope context to the smallest set of files; avoid whole-repo dumps into prompts.
• Disable training and telemetry where offered; review retention policies quarterly.
• Use project-level configs to standardize prompt templates and coding conventions.
• Keep AI suggestions behind branch protections and CI checks to prevent bypasses.

Language- and stack-specific notes

For typed backends, JetBrains AI and VS Code with strict linting keep signal high. For JS/TS frontends, Cursor and Windsurf excel at component refactors and Playwright scaffolding. For polyglot monorepos, prioritize tools with fast cross-language indexing and smart search.

Team training plan

1. Host a 45-minute onboarding covering data policies, prompts, and guardrails.
2. Provide a prompt library for common tasks (tests, refactors, docs).
3. Ask teams to log accepted vs rejected suggestions for two weeks.
4. Review metrics (latency, acceptance rate, PR cycle time) and tune defaults.
5. Publish a short FAQ and revisit quarterly as models and policies change.

FAQ

Can we standardize on one AI IDE?

Standardizing reduces support burden. Offer an approved set with clear defaults per team. Keep plugin and data policies consistent.

How do we keep compliance happy?

Use local indexing, disable training, and log AI usage. Pair IDE output with CI checks and Propel policy enforcement before merging code.

Ready to raise review quality while AI speeds up editing? Add Propel to GitHub and connect it to your preferred IDE so every change is checked before merge.

Sources and further reading

• VS Code Copilot overview for configuration and privacy controls in IDE workflows.
• JetBrains AI Assistant docs on features, indexing, and data handling.
• OWASP Proactive Controls as a checklist to keep AI-assisted changes aligned with secure coding practices.