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What is Panto?

Panto is an innovative AI-enhanced code review solution designed to elevate both the security and quality of software by integrating fluidly into pre-existing development frameworks. Its distinctive AI operating system aligns code with pertinent business contexts derived from tools like Jira and Confluence, thus enabling effective, context-aware code evaluations. Capable of supporting over 30 programming languages, Panto conducts more than 30,000 security assessments to guarantee a comprehensive review of the codebase. The "Wall of Defense" feature operates consistently to detect vulnerabilities and propose solutions, preventing flawed code from reaching production stages. Furthermore, Panto's dedication to zero code retention, adherence to CERT-IN regulations, and on-premises deployment options underline its emphasis on data security and compliance with industry standards. Developers benefit from reviews characterized by a high signal-to-noise ratio, which helps reduce cognitive strain and allows them to focus on critical logic and design elements. This commitment to clarity and efficiency not only streamlines the code review process but also empowers teams to achieve substantial improvements in their overall development workflows. Ultimately, Panto serves as a vital tool for developers seeking to optimize their coding practices while maintaining robust security measures.

What is Amazon CodeGuru?

Amazon CodeGuru is a cutting-edge tool designed for developers that employs machine learning to provide valuable recommendations for improving code quality while identifying the most expensive lines of code within an application. By integrating Amazon CodeGuru into your existing software development workflow, you can take advantage of automated code reviews that help highlight and enhance these costly segments of code, ultimately leading to lower overall expenses. In addition, Amazon CodeGuru Profiler aids developers by pinpointing the most resource-demanding lines of code, offering detailed visual representations and actionable advice for optimizing the code to save costs. Moreover, Amazon CodeGuru Reviewer uses machine learning techniques to uncover critical issues and subtle bugs during the application development process, thus improving the code's overall quality. This all-encompassing strategy not only simplifies the development process but also cultivates a more efficient and economically viable coding environment. By leveraging these tools, developers can significantly enhance both their productivity and the reliability of their applications.

Media

Media

Integrations Supported

Bitbucket
GitHub
Azure DevOps
Confluence
GitLab
Jira

Integrations Supported

Bitbucket
GitHub
AWS App Mesh
AWS CodeCommit
AWS Fargate
AWS Lambda
Amazon EC2
Amazon EKS
Amazon Elastic Container Service (Amazon ECS)
Java
Python

API Availability

Has API

API Availability

Has API

Pricing Information

$12 per month

Pricing Information

Pricing not provided
Free Trial Offered?

Supported Platforms

SaaS
On-Prem

Supported Platforms

SaaS
On-Prem

Customer Service / Support

Web-Based Support

Customer Service / Support

Standard Support
Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

Panto

Date Founded

2024

Company Location

United States

Company Website

www.getpanto.ai/

Company Facts

Organization Name

Amazon

Date Founded

1994

Company Location

United States

Company Website

aws.amazon.com/codeguru/

Categories and Features

AI Code Review

Not specified

Code Quality

Not specified

Code Review

Not specified

Code Security

Not specified

Categories and Features

AI Code Review

Not specified

Application Performance Monitoring (APM)

Baseline Manager
Full Transaction Diagnostics
Performance Control
Resource Management
Root-Cause Diagnosis
Server Performance

Code Quality

Not specified

Machine Learning

Deep Learning
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Templates
Visualization

Static Code Analysis

Not specified

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