Qodo is the AI code review platform that helps teams catch critical issues early, reduce review noise, and maintain consistent code quality across fast-moving, AI-driven development.
Qodo is the enterprise AI code review platform that ensures quality scales with AI-driven development. Our agents run across the IDE and Git with shared cross-repo context and organization-specific rules to deliver precise, high-signal reviews. The result is cleaner code, faster review cycles, and higher-quality software across teams, repositories, and AI-generated development.
Highlights
Multi-agent code review in your Git: Qodo runs specialized agents across every PR to write descriptions, flag real issues with remediations, and enforce your team's governance and security standards at scale, all with full codebase context. (GitHub, GitLab, Bitbucket, Azure DevOps)
Cross-repo codebase context: Review code against your full, cross-repo codebase to catch breaking changes, dependency conflicts, architecture-level issues, and other hidden risks.Agentic review automation: Build configurable, specialized review agents with shared context to enforce code quality rules, use tools to execute specific review tasks
A centralized rules system to define and enforce organizational standards across the repository - security policies, coding and style standards, architectural conventions, and compliance requirements, applied consistently to all pull requests so quality holds at scale.
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing is based on the duration and terms of your contract with the vendor. This entitles you to a specified quantity of use for the contract duration. If you choose not to renew or replace your contract before it ends, access to these entitlements will expire.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
You choose between two contract options priced by developer seat count. Both bundle the same qodo Gen and qodo Merge products; they differ only in team size. Trial Teams covers 5 developers, while Teams covers 50 developers. Pricing scales with the number of developers you support, so you pick the option that matches your team size. Each is billed as a contract commitment rather than by usage.
Top-of-mind questions for buyers
What counts as one developer for billing under these contract options?
A developer is a seat on your engineering team that the contract covers. Trial Teams covers 5 developers and Teams covers 50 developers. Your engineers do not need to create separate accounts to use the platform once it is installed on your repositories.
What products do both contract options include, and do they differ in features?
Both options bundle qodo Gen and qodo Merge. They differ only in the number of developers supported, not in included products. Trial Teams supports 5 developers; Teams supports 50 developers. You pick the option that matches your team size.
Are there limits on repositories or the number of reviews I can run?
There are no limits on repositories or reviews under these plans. You can install the platform on as many repositories as you want. Billing is tied to the number of developers your contract covers, not to repository count or review volume.
Request a private offer to receive a custom quote.
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AWS Marketplace subscribers are entitled to our Standard Support: support@qodo.ai
Enterprise customers receive priority support and a dedicated CSM.
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
Specialized agents run across pull requests to write descriptions, flag issues with remediations, and enforce governance and security standards across GitHub, GitLab, Bitbucket, and Azure DevOps.
Cross-Repository Codebase Context
Review code against full cross-repository codebase to detect breaking changes, dependency conflicts, and architecture-level issues using shared organizational context.
Centralized Rules Engine
Centralized system to define and enforce organizational standards including security policies, coding standards, architectural conventions, and compliance requirements consistently across all pull requests.
IDE and Git Integration
Agents operate across integrated development environments and Git platforms with shared cross-repository context to deliver precise code reviews.
Configurable Review Agents
Build specialized review agents with shared context to enforce code quality rules and execute specific review tasks with organization-specific configurations.
AI-Powered Code Generation and Review
Automated merge request generation based on user stories and AI-driven merge request reviews integrated into development workflows
Unified DevSecOps Platform
Comprehensive platform covering the entire software development lifecycle with integrated security and compliance controls from commit to production
Multi-Step Workflow Automation
AI agents capable of executing complex, multi-step tasks including test generation, code upgrades, and deployment workflow automation
Enterprise Security and Compliance
Built-in enterprise-grade security controls with unified platform for automating secure code deployment and reducing risk across the development pipeline
Integrated Development Environment Integration
Direct embedding of AI capabilities into GitLab daily workflows with unified data store for streamlined operations across AWS and GitLab
Multi-Language Code Support
Supports analysis and navigation across multiple programming languages within enterprise codebases
Distributed Codebase Navigation
Enables instant access to code context across multiple repositories, branches, and code hosts at enterprise scale
Code Change Compliance Verification
Ensures accuracy, compliance, and consistency of human and AI-driven code changes across the organization
Enterprise-Scale Performance
Maintains performance and functionality across large, complex, and fragmented codebases including monoliths and microservices
AI Agent Integration
Provides code understanding and context capabilities for AI agents to operate effectively on real-world enterprise code
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