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.

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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.

Deployment

SaaS, self-hosted (on-prem, cloud-prem, air-gapped)

SaaS, cloud (AWS only), VPC

Security

SOC 2 Type 2

AWS shared responibility model

Data retention

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

IDE Support

Supported Languages

Supports all major programming languages

Primarily supports languages relevant to AWS SDKs and automation, such as Python, JavaScript, and Shell

Git Support

Model Support

Built on AWS Bedrock leveraging
AWS-augmented foundation models

Model Control

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

Code Generation

Code Completion

Documentation Generation

Generate documentation in any language

Documentation generation in Java, Python, JavaScript and Typescript

AI Chat

Testing

Test case identification

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

Mock and stub creation

Test code generation

Supports all programming languages

Supporting only Java and Python projects

Supported test frameworks

All major testing frameworks

Pytest, Unittest, JUnit, Mockito

Ease of use

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

Integration with CI/CD

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

Auto-fix tests

Code Coverage

Test and behavior coverage analysis and various static code analysis techniques

In-line chat

Code review

Ease of use

Integrated into Git (Github, GitLab, ButBucket, Azure DevOps) and into IDE

Code reviews only in IDE via chat interface

Automated code reviews

Generate review walk through in Git with PR description, title, type, summary of changes and ticket compliance

Issue detection

Using static analysis to detect issues and provide remediation

Code security scanning to generate detection message and recommended fix

Code suggestions and insights

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