AI Code Review for Bitbucket: The Complete Guide

If you're using Bitbucket for version control, you've probably noticed a frustrating pattern: most AI code review tools don't support Bitbucket. This guide explains why, what options exist, and how to get AI-powered code reviews for Bitbucket Cloud, Server, and Data Center.

TL;DR: Git AutoReview is the only AI code review tool with full support for Bitbucket Cloud, Bitbucket Server, and Bitbucket Data Center. It includes human-in-the-loop approval, multi-model AI (Claude, Gemini, GPT), and Jira integration.

The Bitbucket Support Problem

A quick look at AI code review tool support:

Tool	GitHub	GitLab	Bitbucket Cloud	Bitbucket Server/DC
Git AutoReview	✅	🔜 Q1 2026	✅ Full	✅ Full
CodeRabbit	✅	✅	❌	❌
Qodo	✅	✅	⚠️ Limited	❌
GitHub Copilot	✅	❌	❌	❌
Sourcery	✅	✅	❌	❌
Bito AI	✅	✅	⚠️ Basic	❌

The pattern is clear: Most AI code review tools are GitHub-first, GitLab-second, and Bitbucket-never.

Why Don't AI Tools Support Bitbucket?

1. Market Share

GitHub dominates with ~90% of public repositories. GitLab is second. Bitbucket, despite being owned by Atlassian (a $60B company), has smaller market share in the developer tools space. Tool makers prioritize where the users are.

2. API Differences

Bitbucket's API differs significantly from GitHub and GitLab:

• Different authentication flows
• Different PR comment structures
• Different webhook formats
• Server/Data Center have separate APIs from Cloud

Building integrations requires dedicated engineering effort.

3. Enterprise Focus

Bitbucket's strength is in enterprise — especially companies already using Jira, Confluence, and other Atlassian products. Enterprise sales cycles are longer, so startups often skip Bitbucket initially.

4. Self-Hosted Complexity

Bitbucket Server and Data Center are self-hosted, meaning:

• Firewall and network configuration
• Custom authentication (SSO, LDAP)
• Version compatibility issues
• No standardized deployment

This complexity deters many SaaS tools from supporting on-premise Bitbucket.

What Bitbucket Users Need

Based on Atlassian Community discussions and our user research, Bitbucket teams need:

1. Pull Request Integration: AI comments directly on PRs, not separate dashboards
2. Server/Data Center Support: Many enterprises can't use cloud-only tools
3. Jira Integration: Link code reviews to tickets and verify acceptance criteria
4. Human Approval: Control over what AI publishes (especially for regulated industries)
5. Atlassian Stack Compatibility: Works with existing Confluence, Jira workflows

AI Code Review Options for Bitbucket

Option 1: Git AutoReview (Recommended)

Git AutoReview is a VS Code extension that provides full Bitbucket support across all deployment types.

Supported Bitbucket Versions:

• ✅ Bitbucket Cloud (bitbucket.org)
• ✅ Bitbucket Server (self-hosted)
• ✅ Bitbucket Data Center (enterprise)

Key Features for Bitbucket:

• Pull request reviews with inline comments
• Human-in-the-loop approval before publishing
• Multi-model AI (Claude, Gemini, GPT)
• Jira integration with acceptance criteria verification
• BYOK for privacy and cost control

Pricing: $14.99/month for teams (not per-user)

→ Learn more about Bitbucket AI Code Review

Option 2: CodeAnt AI

CodeAnt AI offers native PR integration with Bitbucket, including:

• Line-by-line AI reviews
• Vulnerability scanning
• Bitbucket Pipelines integration
• On-premise deployment option

Limitation: No human approval workflow — comments auto-publish.

Option 3: Panto AI

Panto AI provides:

• Close PR integration
• Jira/Confluence context awareness
• On-premise deployment

Limitation: Less mature than other options, limited documentation.

Option 4: DeepSource

DeepSource offers:

• Direct Bitbucket integration
• Automated issue detection
• Autofix suggestions
• Security analysis

Limitation: Focused on static analysis rather than AI-powered review.

Option 5: SonarQube + Bitbucket

SonarQube provides:

• PR decoration for Bitbucket
• Static analysis (SAST)
• Community edition (free)
• Self-hosted support

Limitation: Not AI-powered — rule-based analysis only.

Setting Up AI Code Review for Bitbucket

With Git AutoReview (Step-by-Step)

For Bitbucket Cloud

1. Install the Extension

   Open VS Code → Extensions → Search "Git AutoReview" → Install
   

2. Connect Bitbucket Cloud

   • Open Git AutoReview settings
   • Select "Bitbucket Cloud"
   • Authenticate with your Atlassian account
   • Grant repository access

3. Configure AI Models

   • Add your API keys (Claude, Gemini, or GPT)
   • Or use included credits on paid plans

4. Review a Pull Request

   • Open a PR in the extension
   • Click "Review with AI"
   • Review suggestions before publishing
   • Approve and publish selected comments

For Bitbucket Server/Data Center

1. Install the Extension (same as Cloud)

2. Configure Server Connection

   • Open Git AutoReview settings
   • Select "Bitbucket Server" or "Bitbucket Data Center"
   • Enter your server URL (e.g., https://bitbucket.yourcompany.com)
   • Generate a Personal Access Token in Bitbucket
   • Enter the token in Git AutoReview

3. Network Configuration (if needed)

   • Ensure VS Code can reach your Bitbucket server
   • Configure proxy settings if required
   • Whitelist Git AutoReview domains for AI API calls

4. Review PRs (same workflow as Cloud)

Bitbucket + Jira Integration

One of Bitbucket's strengths is deep Jira integration. Git AutoReview leverages this:

Acceptance Criteria Verification

When you connect Jira:

1. Git AutoReview reads the linked Jira ticket
2. AI analyzes if the code changes satisfy acceptance criteria
3. You get a verification report before approving the PR

Example Output:

Jira Ticket: PROJ-1234 - Add user authentication

Acceptance Criteria Analysis:
✅ AC1: User can log in with email/password — Implemented in auth/login.ts
✅ AC2: Failed login shows error message — Implemented in components/LoginForm.tsx
⚠️ AC3: Lock account after 5 failed attempts — Not found in code changes

Recommendation: Verify AC3 implementation or update ticket scope.

Smart Context

Jira context helps AI understand:

• What the code is supposed to do
• Business requirements and constraints
• Related tickets and dependencies

This leads to more relevant, actionable code review comments.

Challenges and Workarounds

Challenge 1: Server/DC Network Restrictions

Problem: AI APIs require internet access, but Server/DC may be behind strict firewalls.

Workaround with Git AutoReview:

• BYOK means only outbound calls to AI providers (Anthropic, Google, OpenAI)
• Whitelist specific API endpoints
• No inbound connections required
• Code never stored on third-party servers

Challenge 2: Self-Hosted AI Requirements

Problem: Some enterprises can't send code to external AI providers.

Current Status: Git AutoReview uses cloud AI providers. Self-hosted AI support (local LLMs) is on the roadmap for 2026.

Challenge 3: SSO/LDAP Authentication

Problem: Enterprise Bitbucket uses SSO or LDAP, not standard OAuth.

Workaround: Use Personal Access Tokens (PATs) generated in Bitbucket. These work with all authentication systems.

Bitbucket vs GitHub for AI Code Review

If you're choosing between platforms, here's how they compare for AI code review:

Factor	GitHub	Bitbucket
AI Tool Options	20+ tools	3-5 tools
Native AI Features	GitHub Copilot	Atlassian Rovo (limited)
Enterprise Features	GitHub Enterprise	Bitbucket DC
Issue Tracking	GitHub Issues	Jira (superior)
DevOps Integration	GitHub Actions	Bitbucket Pipelines
Best AI Review Tool	Many options	Git AutoReview

Bottom Line: GitHub has more AI tool options, but Bitbucket's Jira integration is unmatched. If you're in the Atlassian ecosystem, stay with Bitbucket and use Git AutoReview for AI code review.

Frequently Asked Questions

Does CodeRabbit support Bitbucket?

No. As of January 2026, CodeRabbit only supports GitHub and GitLab. There's no announced timeline for Bitbucket support.

Which AI code review tool supports Bitbucket Data Center?

Git AutoReview is the only AI code review tool with full support for Bitbucket Data Center. It connects via Personal Access Tokens and works with self-hosted Bitbucket installations.

Can I use GitHub Copilot with Bitbucket?

No. GitHub Copilot only works with GitHub repositories. For Bitbucket AI code review, use Git AutoReview or another Bitbucket-compatible tool.

Is there a free AI code review tool for Bitbucket?

Git AutoReview offers a free tier with 5 reviews per month for Bitbucket. For unlimited reviews, the Team plan is $14.99/month.

How do I integrate AI code review with Jira?

Git AutoReview has native Jira integration. Connect your Atlassian account, and the AI will automatically read linked Jira tickets, verify acceptance criteria, and provide context-aware reviews.

Does Atlassian offer native AI code review?

Atlassian introduced Rovo in 2025, which includes some AI features. However, Rovo focuses on search and assistance rather than detailed PR code review. For comprehensive AI code review, third-party tools like Git AutoReview are recommended.

Conclusion

Bitbucket users have historically been underserved by AI code review tools. Most tools focus on GitHub, leaving Atlassian customers without options.

Git AutoReview fills this gap with:

• ✅ Full Bitbucket Cloud, Server, and Data Center support
• ✅ Human-in-the-loop approval
• ✅ Multi-model AI (Claude, Gemini, GPT)
• ✅ Jira integration with AC verification
• ✅ BYOK for enterprise privacy requirements
• ✅ Team pricing ($14.99/month, not per-user)

If you're using Bitbucket and want AI-powered code reviews, Git AutoReview is currently the most comprehensive option available.

→ Try Git AutoReview for Bitbucket

Related Resources

• Bitbucket AI Code Review — Dedicated landing page
• Jira Integration — Connect code reviews to Jira tickets
• Git AutoReview vs CodeRabbit — Detailed comparison
• Best AI Code Review Tools 2026 — Compare 10 tools