Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
With Amazon SageMaker Pipelines, you can effortlessly develop machine learning workflows using a user-friendly Python SDK, while also managing and visualizing your workflows in Amazon SageMaker Studio. By reusing and storing the steps you create within SageMaker Pipelines, you can enhance efficiency and accelerate scaling. Furthermore, built-in templates allow for rapid initiation, enabling you to build, test, register, and deploy models swiftly, thereby facilitating a CI/CD approach in your machine learning setup. Many users manage numerous workflows, often with various versions of the same model. The SageMaker Pipelines model registry provides a centralized repository to monitor these versions, simplifying the selection of the ideal model for deployment according to your organizational needs. Additionally, SageMaker Studio offers features to explore and discover models, and you can also access them via the SageMaker Python SDK, ensuring versatility in model management. This integration fosters a streamlined process for iterating on models and experimenting with new techniques, ultimately driving innovation in your machine learning projects.
Description
Sage2 is a cloud-based AI model and API tailored for developers who are creating software agents and automating workflows. It processes structured task instructions and generates decisions or information in formats that machines can easily interpret, enabling applications to assign tasks like classification, routing, evaluation, and data extraction to a model specifically designed for tool-utilizing agents. Developers have the option to interact with Sage through its API, examine model functionality via browser demos, and integrate it into their agent development environments, such as Cursor and Claude Code. The service features a pricing structure for API usage that includes a free tier alongside various paid monthly plans, with usage fees and included credits differing based on the selected plan. Sage is primarily targeted at individual developers and software teams seeking to enhance their applications with decision-making and extraction functionalities without the need to manage the model independently. This flexibility allows for streamlined integration into existing workflows, making it an appealing choice for those looking to leverage AI capabilities effectively.
API Access
Has API
No
API Access
Has API
Yes
Screenshots View All
No images available
Integrations
Amazon SageMaker
Yes
Amazon Web Services (AWS)
Yes
Claude Code
No
Cursor
No
Integrations
Amazon SageMaker
No
Amazon Web Services (AWS)
No
Claude Code
Yes
Cursor
Yes
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Pricing Details
$0
The Free plan costs $0/month and includes $0.20 in usage. The Developer plan costs $14/month and includes $14 in usage. Additional usage is charged by token and web-search rates shown on the pricing page.
Free Trial
No
Free Version
Yes
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
Amazon
Founded
2006
Country
United States
Website
aws.amazon.com/sagemaker/pipelines/
Vendor Details
Company Name
Levanto Labs
Founded
2026
Country
United States
Website
levanto.ai/model
Product Features
Continuous Delivery
Application Lifecycle Management
No
Application Release Automation
No
Build Automation
No
Build Log
No
Change Management
No
Configuration Management
No
Continuous Deployment
No
Continuous Integration
No
Feature Toggles / Feature Flags
No
Quality Management
No
Testing Management
No
Continuous Integration
Build Log
No
Change Management
No
Configuration Management
No
Continuous Delivery
No
Continuous Deployment
No
Debugging
No
Permission Management
No
Quality Assurance Management
No
Testing Management
No
Machine Learning
Deep Learning
No
ML Algorithm Library
No
Model Training
No
Natural Language Processing (NLP)
No
Predictive Modeling
No
Statistical / Mathematical Tools
No
Templates
No
Visualization
No