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Apache Software Foundation

Apache SINGA

Composite Score

7.9 /10

CX Score

7.9 /10

9 Reviews Write a Review

What is Apache SINGA?

Apache SINGA is an Apache top-level project for developing an open source machine learning library. It provides a flexible architecture for scalable distributed training, is extensible to run over a wide range of hardware, and has a focus on health-care applications.

Company Details

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Apache SINGA Ratings

Real user data aggregated to summarize the product performance and customer experience.

Product scores listed below represent current data. This may be different from data contained in reports and awards, which express data as of their publication date.

96 Likeliness to Recommend

100 Plan to Renew

92 Satisfaction of Cost Relative to Value

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Emotional Footprint Overview

Product scores listed below represent current data. This may be different from data contained in reports and awards, which express data as of their publication date.

+90 Net Emotional Footprint

The emotional sentiment held by end users of the software based on their experience with the vendor. Responses are captured on an eight-point scale.

How much do users love Apache SINGA?

0% Negative

13% Neutral

87% Positive

Pros

Helps Innovate
Continually Improving Product
Reliable
Performance Enhancing

Feature Ratings

Average 88

Ensembling

Model Monitoring and Management

Data Ingestion

Data Pre-Processing

Openness and Flexibility

Data Labeling

Feature Engineering

Data Exploration and Visualization

Model Training

Performance and Scalability

Algorithm Recommendation

Vendor Capability Ratings

Average 84

Ease of Data Integration

Ease of Implementation

Availability and Quality of Training

Usability and Intuitiveness

Breadth of Features

Business Value Created

Product Strategy and Rate of Improvement

Quality of Features

Vendor Support

Ease of Customization

Ease of IT Administration

Apache SINGA Reviews

Adebayo F.

Role: Information Technology
Industry: Technology
Involvement: End User of Application

Validated Review

Verified Reviewer

Submitted May 2025

A Powerful Framework with Great Potential

Likeliness to Recommend

10 /10

What differentiates Apache SINGA from other similar products?

Apache SINGA stands out with its focus on distributed deep learning and its flexible architecture that supports both synchronous and asynchronous training, making it particularly well-suited for scalable machine learning across multiple devices and platforms.

What is your favorite aspect of this product?

My favorite aspect of Apache SINGA is its strong support for distributed training, which allows models to scale efficiently across multiple machines, making it ideal for handling large datasets and complex deep learning tasks.

What do you dislike most about this product?

What I dislike most about Apache SINGA is its steep learning curve due to limited documentation and community support, which can make it challenging for new users to get started or troubleshoot issues effectively.

What recommendations would you give to someone considering this product?

If you're considering Apache SINGA, make sure you have a solid understanding of deep learning and distributed systems, take time to explore the available examples, and be prepared to rely on external resources or community forums due to its limited documentation.

Pros

Continually Improving Product
Reliable
Performance Enhancing
Enables Productivity

SALOME U.

Role: Public Sector
Industry: Government
Involvement: End User of Application

Validated Review

Verified Reviewer

Submitted Mar 2025

Solid Foundation for Deep Learning

Likeliness to Recommend

10 /10

What differentiates Apache SINGA from other similar products?

Its focus on distributed deep learning with a flexible, modular architecture that supports both traditional and emerging AI models, particularly for large-scale data processing.

What is your favorite aspect of this product?

Its modular architecture, which allows for customization and flexibility in building and scaling deep learning models.

What do you dislike most about this product?

The steep learning curve and lack of a more intuitive user interface for easier model development and management.

What recommendations would you give to someone considering this product?

Be prepared for a steep learning curve, and ensure you have a strong understanding of deep learning concepts

Pros

Reliable
Performance Enhancing
Enables Productivity
Client Friendly Policies

Success O.

Role: C-Level
Industry: Technology
Involvement: Business Leader or Manager

Validated Review

Verified Reviewer

Submitted Nov 2024

Easy to use, but a bit pricey

Likeliness to Recommend

10 /10

What differentiates Apache SINGA from other similar products?

It supports both synchronous and asynchronous training, giving developers flexibility based on the use case

What is your favorite aspect of this product?

favorite aspect of Apache SINGA is its on-demand computation graph.

What do you dislike most about this product?

its smaller community and ecosystem compared to more established frameworks

What recommendations would you give to someone considering this product?

I recommend starting with small projects to explore its modular design and on-demand computation graph capabilities

Pros

Helps Innovate
Continually Improving Product
Reliable
Performance Enhancing

How We Deliver

Apache SINGA

What is Apache SINGA?

Company Details

Need Assistance?

Apache SINGA Ratings

96 Likeliness to Recommend

100 Plan to Renew

92 Satisfaction of Cost Relative to Value

Emotional Footprint Overview

+90 Net Emotional Footprint

How much do users love Apache SINGA?

Pros

Feature Ratings Average rating of all product features, some of which may not be shown below

Ensembling 'Ability to combine ML models for best results.

Model Monitoring and Management Ability to monitor model use and performance in production, incl. identifying models for refresh and/or replacement.

Data Ingestion Ability to access data from various sources on-prem and in the cloud and of various type: structured, unstructured, and semi-structured, real-time, etc.

Data Pre-Processing Ability to wrangle or pre-process data to ready it for model training.

Openness and Flexibility Integration with open-source and 3P resources (languages, frameworks, libraries, etc.)

Data Labeling Ability to annotate/ label data to create a training data set.

Feature Engineering Ability to extract and build features from raw data.

Data Exploration and Visualization Ability to visually manipulate, interact with, and explore data, and perform basic descriptive statistics and pattern detection.

Model Training Ability to train a model with a range of data sets that you provide.

Performance and Scalability Systems performance (time) and scalability end-to-end: from data load and processing to model creation and deployment.

Algorithm Recommendation Ability to recommend a best ML algorithm to use based on dataset characteristics.

Vendor Capability Ratings Average rating of all vendor capabilities, some of which may not be shown below

Ease of Data Integration Use this data to determine whether the product will cause headaches or make data integration easy.

Ease of Implementation Successfully implementing new software is necessary to realize its full value and promote end user adoption. This data indicates whether or not the product is easy to implement.

Availability and Quality of Training Effective and readily available training enables users to get the most out of the software you've chosen. Use this section to make sure your vendor's training programs and materials measure up.

Usability and Intuitiveness End user learning curves cost the organization money. Pay attention to your end users' technical ability to determine how important UX is in your purchase.

Breadth of Features Users prefer feature rich software that enables them to perform diverse series of tasks. This data expresses user satisfaction with the product's breadth of features.

Business Value Created Software needs to create value for employees, customers, partners, and, ultimately, shareholders. This data expresses user satisfaction - or lack thereof - with the product's business value.

Product Strategy and Rate of Improvement Vendors who don't stay on top of emerging needs and trends won't enable you to meet your business goals. Use this data to separate innovators from imposters.

Quality of Features Feature quality is just as important as quantity. Use this data to determine if this product will do what you're purchasing it to do, easily, intuitively, reliably, and effectively.

Vendor Support The importance of vendor support will vary for each organization depending on internal capabilities, but there will always be issues that only the vendor can resolve.

Ease of Customization Don't get bogged down in a difficult customization; use this data to make sure you can easily achieve the functionality you need for your particular situation.

Ease of IT Administration This data indicates whether IT personnel will be able to resolve issues and perform configurations efficiently and effectively.

Apache SINGA Reviews

Adebayo F.

A Powerful Framework with Great Potential

Likeliness to Recommend

Pros

SALOME U.

Solid Foundation for Deep Learning

Likeliness to Recommend

Pros

Success O.

Easy to use, but a bit pricey

Likeliness to Recommend

Pros

Title

Feature Ratings

Ensembling

Model Monitoring and Management

Data Ingestion

Data Pre-Processing

Openness and Flexibility

Data Labeling

Feature Engineering

Data Exploration and Visualization

Model Training

Performance and Scalability

Algorithm Recommendation

Vendor Capability Ratings

Ease of Data Integration

Ease of Implementation

Availability and Quality of Training

Usability and Intuitiveness

Breadth of Features

Business Value Created

Product Strategy and Rate of Improvement

Quality of Features

Vendor Support

Ease of Customization

Ease of IT Administration