GitHub Copilot Alternatives for Teams

For a team, the best GitHub Copilot alternative is usually the one that fits the platform you already run on. On Google Cloud, look at Gemini Code Assist. If developers want an editor built around an agent, look at Cursor. If the work is long, multi-step agent tasks from a terminal or CI, look at Claude Code. If everyone lives in IntelliJ, PyCharm or Rider, look at JetBrains AI. If you need the assistant inside your own network, look at Tabnine’s private installation. And if you are on Amazon Q Developer today, note that AWS is ending support for its IDE plugins and pointing users to Kiro. Before any of that, compare three things in writing: who controls settings, what happens to your code, and what the vendor says about IP.

This page is ordered by fit, not ranked. Each tool gets a short paragraph and links to a longer comparison where one exists. No prices: plans and allowances change often, so check each vendor’s current page.

The three questions a team should ask

• Admin controls: can an administrator set models, agents, MCP servers and permissions for everyone, and can a developer override them?
• Data use: does the vendor train on your prompts and code, retain them, or both, and does that change with the plan or a setting?
• IP protection: does the vendor offer an indemnity, a filter for code that matches public repositories, or attribution of where code came from?

For comparison, Copilot’s own answers sit in GitHub: enterprise owners set policies and organization owners work within them, content exclusion keeps chosen files out of suggestions and review, and the MCP servers policy is off by default for Business and Enterprise. The side-by-side with Google’s assistant is in Gemini Code Assist vs GitHub Copilot.

If you run on Google Cloud: Gemini Code Assist

Code Assist Standard and Enterprise are sold and administered through Google Cloud, with access granted by IAM roles, VPC Service Controls perimeters, and optional logging of prompts and responses to Cloud Logging. Google’s security, privacy and compliance page↗ (opens in a new tab) says Google does not use your data to train its models without permission and lists Code Assist Standard and Enterprise as a Generative AI Indemnified Service. It runs in VS Code, JetBrains IDEs and Android Studio, and its agent mode is marked Preview. The consumer edition for individuals stopped serving requests on June 18, 2026, so plan on a Standard or Enterprise license.

If developers want an AI editor: Cursor

Cursor is an editor built around its agent, with a CLI and cloud agents that run in isolated virtual machines. For teams, its enterprise documentation covers SSO, SCIM, role-based access, MDM policies, audit logs and model access controls. On data, Cursor’s privacy and data governance page↗ (opens in a new tab) says that with Privacy Mode on, your code is never used for training by Cursor or its model providers; Privacy Mode is on by default for Enterprise teams and can be enforced for a whole team. Cloud agents are the one feature that stores code, because they work on your repository over time. Compared in Cursor vs Codex and GitHub Copilot agent vs Cursor agent, and against other editors in Cursor alternatives.

If the work is agent tasks: Claude Code

Claude Code is Anthropic’s agent for the terminal, IDE extensions, a desktop app and the web. It has no inline completions; you ask for outcomes and review diffs. Administrators set managed settings through a managed-settings.json file, MDM or the claude.ai admin console, and those settings override everything a developer sets, including which MCP servers are allowed. On data, Anthropic’s data usage page↗ (opens in a new tab) says it does not train generative models on code or prompts sent under commercial terms, meaning Team and Enterprise plans, the API and cloud platforms, unless the customer opts in. Consumer plans have a setting instead. The full comparison is in Claude Code vs GitHub Copilot, and you can also run Claude inside VS Code through Copilot’s Claude harness.

If everyone uses JetBrains IDEs: JetBrains AI

JetBrains AI Assistant brings AI Chat, completion and coding agents into IntelliJ IDEA, PyCharm, WebStorm, Rider and the rest. It can run Junie, Claude Agent, Codex or GitHub Copilot as integrated agents, any agent that speaks the Agent Client Protocol, or a third-party model with your own key. Where a company manages AI through JetBrains IDE Services or JetBrains Central, an administrator decides which agents are available and whether developers can add their own. JetBrains’ data handling page↗ (opens in a new tab) says requests and code context go to the model provider, and that detailed collection of your conversations, which JetBrains may use for product improvement and training, is opt-in and off by default. Junie is compared in Junie vs Claude Code.

If you are on AWS: Amazon Q Developer, now Kiro

Amazon Q Developer is still documented, but its IDE story is ending. AWS’s end-of-support notice↗ (opens in a new tab) says support for the Amazon Q Developer IDE plugins ends on April 30, 2027, and that inline suggestions, chat and code generation are all available in Kiro. Kiro is AWS’s agentic coding tool built around specs, with an IDE, a CLI, a web app and support for editors that speak ACP. For organizations it uses IAM Identity Center for subscriptions, lets administrators govern which models and MCP servers developers can use, deploy managed permission rules, and track usage on a dashboard. See what Kiro is and Kiro vs Claude Code.

If code cannot leave your network: Tabnine

Tabnine is the option on this list built for private deployment. Besides its hosted SaaS, Tabnine Enterprise can be installed in your own VPC or on premises, including fully air-gapped. Its privacy page↗ (opens in a new tab) describes a no-train-no-retain policy: code context is used to answer the request and discarded. For IP, its Provenance and Attribution feature checks code generated in chat against public code on GitHub and flags matches with the source repository and its license. It runs in VS Code, JetBrains IDEs, Visual Studio and Eclipse, and Tabnine Agent can use MCP servers.

Which fits which team

• Your code, reviews and permissions already live in GitHub: stay with Copilot, which reaches pull requests and the cloud agent as well as the editor.
• You govern everything through Google Cloud IAM and want an indemnified service: Gemini Code Assist.
• Developers want an editor built around an agent, with Privacy Mode enforced for the team: Cursor.
• You want long agent tasks in terminals, scripts and CI, under commercial data terms: Claude Code.
• Your developers work in JetBrains IDEs and you want to control which agents they can use: JetBrains AI.
• You are on Amazon Q Developer: plan the move to Kiro before April 30, 2027.
• Code cannot leave your network: Tabnine’s private installation.

Many teams end up with two: an assistant in the editor for everyday work, and an agent for longer tasks. That works if both read one AGENTS.md and connect to the same MCP servers. Which file each tool reads is in AI context files compared.

One task list for whichever assistant you choose

Changing assistants should not mean losing track of what is in flight. On fenbs the work lives on a board: tasks in lanes To Do, Next Up, In Progress and Completed, each with a note, a plan and a test status, and the owner’s standing instructions on the Decisions and rules page, which every connected AI assistant reads first. Any of these tools that speaks MCP connects to https://fenbs.ai/api/mcp, signing in with OAuth or with a token you issue with a name, scopes and an optional expiry. Roles are set per company, and History records each change under the assistant that made it. fenbs does not replace your assistant’s admin console; it keeps the list of work in one place both people and assistants can see.