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What is Runheed?

Runheed is an innovative error-tracking solution tailored for developers who leverage AI coding agents in building and maintaining production applications. It provides SDKs for both Node.js/TypeScript and Python that capture exceptions, categorizing them by fingerprint while delivering in-depth insights such as stack traces, breadcrumbs, request details, environmental conditions, release information, and optional context from the agent's session. The platform's remote Model Context Protocol (MCP) server enables compatible agents to efficiently sift through error groupings, evaluate comprehensive contexts and trends, monitor project health, and document corrections for identified errors. In cases where a previously resolved error resurfaces, it automatically reopens with its associated resolution note for easy reference. Setup frameworks are offered for widely-used applications like Express, Next.js, Fastify, FastAPI, Django, and Flask, alongside an HTTP endpoint for error reporting from other programming languages. Users are notified via email whenever new error types emerge, ensuring they stay informed. The Solo plan, available at $29 per workspace monthly, includes a 7-day trial, allowing potential users to experience its features before making a financial commitment, which enhances Runheed's attractiveness for developers aiming to optimize their error management strategies. With its comprehensive tools and user-friendly setup, Runheed stands out as a valuable resource in the realm of software development.

What is Amazon SageMaker Debugger?

Improve machine learning models by capturing real-time training metrics and initiating alerts for any detected anomalies. To reduce both training time and expenses, the training process can automatically stop once the desired accuracy is achieved. Additionally, it is crucial to continuously evaluate and oversee system resource utilization, generating alerts when any limitations are detected to enhance resource efficiency. With the use of Amazon SageMaker Debugger, the troubleshooting process during training can be significantly accelerated, turning what usually takes days into just a few minutes by automatically pinpointing and notifying users about prevalent training challenges, such as extreme gradient values. Alerts can be conveniently accessed through Amazon SageMaker Studio or configured via Amazon CloudWatch. Furthermore, the SageMaker Debugger SDK is specifically crafted to autonomously recognize new types of model-specific errors, encompassing issues related to data sampling, hyperparameter configurations, and values that surpass acceptable thresholds, thereby further strengthening the reliability of your machine learning models. This proactive methodology not only conserves time but also guarantees that your models consistently operate at peak performance levels, ultimately leading to better outcomes and improved overall efficiency.

Media

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Media

Integrations Supported

ChatGPT
Claude
Claude Code
Claude Desktop
Cline
Codex CLI
Cursor
Model Context Protocol (MCP)
Zed

Integrations Supported

AWS Lambda
Amazon CloudWatch
Amazon SageMaker
Amazon SageMaker Studio
Amazon Web Services (AWS)
Change Healthcare Data & Analytics
Keras
MXNet
PyTorch
TensorFlow

API Availability

Has API

API Availability

Pricing Information

$29/month
Solo is $29 per workspace per month, or $290 annually, and includes 50,000 events per month, 3 projects, and 30-day history. Pro is $79/month or $790/year; Scale is $199/month or $1,990/year. All plans include the MCP server and unlimited agent keys; events above the monthly limit are rejected rather than billed. The 7-day trial requires a payment card; the first charge is due at the end of the trial unless canceled before then.
Free Trial Offered?

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

24 Hour Support
Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub
Webinars

Company Facts

Organization Name

Great Work LLC

Date Founded

2025

Company Location

United States

Company Website

runheed.com

Company Facts

Organization Name

Amazon

Date Founded

1994

Company Location

United States

Company Website

aws.amazon.com/sagemaker/debugger/

Categories and Features

Debugging

Not specified

Categories and Features

AI Infrastructure

Not specified

Debugging

Not specified

Machine Learning

Not specified

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