AI coding tools now range from autocomplete to agents that take a task and return a change for review. Picking one for a team is less about demos and more about fit with how you build, review and secure code.

The options

  • [GitHub Copilot](/products/github-copilot): works inside popular editors and GitHub itself, a natural pick if your code and reviews are on GitHub.
  • [Cursor](/products/cursor): an AI-first code editor built around agents that edit across files.
  • [Claude Code](/products/claude-code): an agent from Anthropic that works in your terminal and codebase.
  • [OpenAI Codex](/products/openai-codex): OpenAI's coding agent for writing, testing and reviewing code.
  • [Tabnine](/products/tabnine): positioned for teams that want to run the assistant privately in their own environment.
  • [JetBrains AI Assistant](/products/jetbrains-ai-assistant): built into IntelliJ, PyCharm and other JetBrains tools.

What matters for a team

  1. Editor fit. Developers adopt tools that live where they already work.
  2. Code privacy. Ask whether your code is used to train models, where it is processed and whether private deployment is available.
  3. Admin controls. Look for seat management, policy settings and usage reports.
  4. Review habits. AI-written code still needs human review and tests. Do not let the tool relax your pull request process.
  5. Licences. Ask how the vendor handles code that resembles open-source projects.

A sensible rollout

Give two or three tools to a small group for a month, measure review time and bug rates, and then standardise. Many teams end up allowing one editor tool and one agent tool.

Browse the full list of AI tools or read the AI vendor evaluation checklist.