Qodo vs Amazon Q
From advanced context awareness to robust testing capabilities, see how Qodo’s AI code integrity platform is built for enterprises tackling complex coding challenges—far beyond basic code completion and surface-level context.
The Qodo difference
Qodo offers deeper context-awareness, advanced reasoning capabilities, and robust testing tools, making it a quality-focused solution across the entire software development lifecycle.
Global context-awareness
Customize and control context indexing and retrieval to ensure only quality code informs AI development
Comprehensive testing and review
Robust testing and review tools that enable test automation and execution of clean, healthy code.
Solve complex coding challenges
Code-oriented, multi-stage flow that guides LLMs through reasoning and iterative testing.
Deploy anyway, anywhere
Infrastructure agnostic, supporting diverse environments, including on-premises, cloud-premises, and air-gapped.
SaaS, self-hosted (on-prem, cloud-prem, air-gapped)
SaaS, cloud (AWS only), VPC
SOC 2 Type 2
AWS shared responibility model
48 hour log retention on Qodo side for troubleshooting only. Zero data retention on model provider (OpenAI, AWS Bedrock, Qodo)
Stores questions, responses and additional context
Supports all major programming languages
Primarily supports languages relevant to AWS SDKs and automation, such as Python, JavaScript, and Shell
Built on AWS Bedrock leveraging
AWS-augmented foundation models
Choose any model, any time
No control over models
Choose which files and repos to index, and add custom tags and repo-level filtering
No control over context collection
Generate documentation in any language
Documentation generation in Java, Python, JavaScript and Typescript
Code analysis that incorporates understanding of dependencies and imports to identify behaviours in code
Uses project structure, existing code, and targeted file in the workspace to identify appropriate test cases
Supports all programming languages
Supporting only Java and Python projects
All major testing frameworks
Pytest, Unittest, JUnit, Mockito
Plug-and-play in IDEs, with automated test creation requiring minimal manual configuration. Run and interact with tests inside the IDE
Testing is more manual and focused on debugging AWS resources rather than generation code tests.
Tests cannot be run inside in the IDE
Easily integrates with Git workflows and CI/CD pipelines, enabling automated test execution during code reviews
Works within AWS services to ensure deployed infrastructure or code is functioning as intended
Test and behavior coverage analysis and various static code analysis techniques
Integrated into Git (Github, GitLab, ButBucket, Azure DevOps) and into IDE
Code reviews only in IDE via chat interface
Generate review walk through in Git with PR description, title, type, summary of changes and ticket compliance
Using static analysis to detect issues and provide remediation
Code security scanning to generate detection message and recommended fix
Automatic and detailed code suggestions tailored to organizations to optimize code efficiency, adhere to best practices, improve logics, structure and readability
Generates code issues related to various quality issues, including but not limited to AWS best practices
