Our Top Picks

H2O AI Logo
H2O.AI
  • Deployment: Windows
  • Customer service: Phone during business hours
  • Diverse machine learning features in multiple platforms
H2O.ai is a company with the stated mission of "democratizing" AI, meaning making AI tools available to companies in a wide variety of industries. Its services are aimed at enterprise-level businesses, and it offers several distinct platforms. There's the automatic machine learning tool H20-3, which advertises as open-source and scalable. There's H2O Driverless AI, which automates model testing and feature tuning for data scientists. And there's H2O Q, which provides building blocks to make AI apps that can deliver automatic insights and predictions. These products are praised by clients for their usefulness in prototyping and building workflows and ease of deployment and use.

Pros & Cons

Pros

  • Powerful machine learning, prototyping, and app building tools
  • High-quality predictive modeling
  • Driverless AI automates machine learning for data scientists

Cons

  • H2O's Driverless AI tool is proprietary and not open source
  • Many features of its platforms are most effective for people with statistics and machine learning backgrounds

Summary

  • Starting Price
    • Custom Quote
  • Free Trial/Demo
    21 Days Free Trial
    Offers Demo
  • Deployment
    Installed - Windows
  • Support Options
    Business Hours
  • Machine Learning
    Yes
Features
  • Deployment: Windows
  • Customer service: Phone during business hours
  • Diverse machine learning features in multiple platforms
IBMSPSS Logo
IBMSPSS
  • Deployment: Windows
  • Customer service: 24/7 (live rep), online
  • Machine learning-driven data analysis
IBM deals in a whole range of AI products. One of its most venerable offerings is SPSS software, a competitive presence in the AI market after almost 20 years, which focuses on statistical analysis and sees use by organizations in education, healthcare, market research, retail, and even government. Leveraging an extensive library of machine learning algorithms and text analysis tools, it advertises extensive integrations with apps and big data suites. SPSS is promoted as scalable and compatible with operations of all sizes and users of all skill levels. In line with that mission, it offers suites designed for enterprise applications, service packages designed for students and offered at student discounts, and various solutions tailored for academic institutions and data scientists alike.

Pros & Cons

Pros

  • Fast data analysis usable by nonexperts
  • User-friendly data export to a variety of formats
  • Handles large data sets

Cons

  • SPSS subscriptions tend to be expensive

Summary

  • Starting Price
    • Subscription - Starting at $99.00* per authorized user per month
    • Perpetual and Term Licenses - custom quote
  • Free Trial/Demo
    30 Days Free Trial
    Offers Demo
  • Deployment
    Installed - Windows
  • Support Options
    24/7 (Live Rep)
    Online
  • Machine Learning
    Yes
Features
  • Deployment: Windows
  • Customer service: 24/7 (live rep), online
  • Machine learning-driven data analysis
Infosys Nia Logo
Infosys Nia
  • Deployment: Web-based, cloud, SaaS
  • Customer service: Online
  • Machine learning workbench for expert use
A product of EdgeVerve, Infosys Nia is an AI platform that advertises itself as the solution to several common problems faced by companies trying to implement AI: trouble with moving from experimentation to production, struggling to derive insights from documentation, and difficulty with managing data. Nia provides a machine learning toolkit built for data scientists, a set of APIs called Nia Vision for image and document analysis, conversational AI and natural language processing, and a Cognitive Search tool for querying data sets with machine learning-driven subtlety and detail. Nia Knowledge, meanwhile, is built to organize data into "ontology-based" visual representations designed to facilitate knowledge management across large enterprises.

Pros & Cons

Pros

  • Advanced machine learning tools
  • NLP and chatbot functionality

Cons

  • No installed deployment options

Summary

Infosys Nia Logo
Infosys Nia
  • Starting Price
    • Custom Quote
  • Free Trial/Demo
    Offers Demo
  • Deployment
    Web-Based, Cloud, SaaS
  • Support Options
    Online
  • Machine Learning
    Yes
Features
  • Deployment: Web-based, cloud, SaaS
  • Customer service: Online
  • Machine learning workbench for expert use
Infrrd Logo
Infrrd
  • Deployment: Web-based, cloud, SaaS
  • Customer service: Online
  • High-volume ML-powered data extraction
Infrrd is an AI specialized in template-free data extraction, which is a step beyond simple optical character recognition or OCR. Supported by a stack of AI technologies including machine learning and natural language processing, it provides intelligent processing of documents that enables the extraction of data from a wide variety of documents, covering handwriting, logos, symbols, and rubber stamps through complex images, graphs, and tables. This kind of direct optical processing, designed for use by enterprises, eliminates the need for manual data entry, and functions as a self-service tool for business users that doesn't require them to be data scientists.

Pros & Cons

Pros

  • Sophisticated approach to data extraction
  • Designed for the nonexpert user
  • Deals with high volumes of data

Cons

  • Not designed for nonenterprise users or small business
  • Pricing starts high at $2,000 per month

Summary

  • Starting Price
    • Custom Quote
  • Free Trial/Demo
    Offers Demo
  • Deployment
    Web-Based, Cloud, SaaS
  • Support Options
    24/7 (Live Rep)
  • Machine Learning
    Yes
Features
  • Deployment: Web-based, cloud, SaaS
  • Customer service: Online
  • High-volume ML-powered data extraction
TensorFlow Logo
TensorFlow
  • Deployment: Mac, Windows, web-based, cloud, SaaS
  • Customer service: Phone during business hours
  • Open source machine learning (ML) library
Advertised as an end-to-end machine learning platform, TensorFlow is the back end for a well-known deep learning API called Keras. It offers experimentation for research, model building, and deployment in a wide range of settings, including in the cloud and on-device. Its applications use minimal amounts of Python or Javascript code to leverage deep neural network functionality, provide rapid prototyping, and enable built-in optimization for training purposes. Built for researchers, data scientists, and app developers, it offers a Lite library for mobile compatibility and includes graph management, image management, and event handling features.

Pros & Cons

Pros

  • Powerful ML model building and deployment on multiple platforms
  • TensorFlow Lite offers mobile compatibility
  • Wide range of integrations available

Cons

  • A resource hog that can crash on low-performance GPUs

Summary

TensorFlow Logo
TensorFlow
  • Starting Price
    • Custom Quote
  • Free Trial/Demo
    Offers Demo
  • Deployment
    Installed - Mac
    Installed - Windows
    Web-Based, Cloud, SaaS
  • Support Options
    Business Hours
  • Machine Learning
    Yes
Features
  • Deployment: Mac, Windows, web-based, cloud, SaaS
  • Customer service: Phone during business hours
  • Open source machine learning (ML) library

How We Chose the Best Artificial Intelligence Software

Artificial intelligence (AI) is transforming the way the world does business. It offers a vast array of labor-saving tools for analyzing data, predicting outcomes, and creating automatic tasks and processes. There are plenty of companies in this space offering a variety of tools and features, and it can be difficult to tell how they really stack up against one another. We used three key metrics to evaluate the best AI software.

AI Features

Features like machine learning, predictive analytics, and speech recognition are some of the most important factors in making an artificial intelligence tool competitive.

Integrations

Various kinds of tools and software suites are often used in combination with AI. While it's helpful when software comes with its own application programming interface (API) that makes it possible to custom-build integrations with those tools, really competitive AI software should also come with pre-built integrations that don't require the user to be a coding expert.

Reports

To make it possible to measure and track the performance of AI software, it's important for it to have detailed reporting functions whose output is easy to read and organize.