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12 Best AI Software Shortlist

I have shortlisted the top 12 AI software options tailored to your requirements. They have a track record of effectively resolving gaps, challenges, and issues.

  1. V7 - Best for high-quality data annotation
  2. Valohai - Best for managing deep learning projects
  3. Encord - Best for active traffic management solutions
  4. Weights & Biases - Best for tracking machine learning experiments
  5. SAP HANA Cloud - Best for real-time business data processing
  6. SuperAnnotate - Best for automating image annotation tasks
  7. Saturn Cloud - Best for collaborative data science projects
  8. Aporia - Best for monitoring AI model performance
  9. SAS Model Manager - Best for enterprise-scale model management
  10. Databricks Lakehouse Platform - Best for unified analytics solutions
  11. Vertex AI - Best for end-to-end AI model development
  12. IBM Watson Studio - Best for building, training, and managing AI models

As a professional exploring the realm of artificial intelligence software, I can vouch for the immense value it brings to businesses. AI chatbots, such as ChatGPT, are redefining CRM systems, taking over mundane tasks like messaging on LinkedIn and other social media platforms. With their ability to understand and respond to customer inquiries using natural language processing (NLP), these AI chatbots are revolutionizing how I handle customer interactions.

Now let's talk about AI writing tools. These tools are redefining business operations by addressing pain points such as customer service inefficiencies and time-consuming content creation. So whether you're running an eCommerce platform or managing subscribers on iOS apps, there's an AI solution ready to boost your productivity.

What is AI Software?

AI software is a system of algorithms and protocols that simulate human intelligence in machines. By data analysis and adapting its responses, it can accomplish specific tasks or make decisions without explicit human intervention. Many individuals and industries utilize the best AI tools, from virtual assistants to healthcare to finance, manufacturing to entertainment.

These leverage templates to generate SEO-friendly content, providing excellent writing aids. Other branches of AI solutions include computer vision used in on-premises security, predictive analytics powered by machine learning platforms, and speech recognition to transcribe spoken words.

Microsoft and Apple have unique AI assistants integrated into their ecosystems, while OpenAI offers cutting-edge AI algorithms like Jasper. Open-source machine learning platforms provide a personalized approach for artificial neural networks, detecting patterns, and streamlining processes. AI software is a versatile tool that promotes innovation and productivity in various industries.

Overview of the 12 Best AI Software

1. V7 - Best for high-quality data annotation

V7 radiology review screenshot
Here's a screenshot of the radiology review in V7.

As a platform designed to annotate data for machine learning, V7 excels in helping AI learn through visual cues. It provides a crucial layer of understanding for algorithms by providing them with high-quality annotated data. This feature makes it particularly suitable for tasks that involve intricate visual comprehension.

Why I Picked V7:

In an ocean of AI software, I selected V7 for its dedication to producing superior data annotation, which is key for training robust AI models. The high-quality annotations V7 improves machine learning models' understanding of complex visual cues, justifying its place as the best tool for high-quality data annotation.

Standout Features & Integrations:

V7's main highlight is its AI-powered annotation tools that streamline the data labeling process, making it more efficient and accurate. The platform's collaboration capabilities also allow multiple users to work on the same dataset, promoting team efficiency. Regarding integrations, V7 works easily with most machine learning frameworks and data management platforms, and it can be easily incorporated into existing workflows.


V7's pricing starts from $89/user/month (billed annually). This cost includes access to V7's high-quality annotation tools and collaboration features.


  • Exceptional data annotation capabilities
  • Streamlined collaborative features
  • Easy integration with existing machine learning frameworks


  • Can be pricier than some other options
  • Learning curve for new users
  • Limited non-visual annotation options

2. Valohai - Best for managing deep learning projects

Valohai Executions screenshot
This is Valohai's automatic versions experiments and training runs for reproducibility.

Valohai excels in the management and orchestration of machine learning projects. It is designed to automate and keep track of the numerous stages involved in creating and deploying deep learning models, making it the ideal choice for overseeing complex deep learning projects.

Why I Picked Valohai:

Navigating the sea of AI software, I determined Valohai as a prime selection for its robust project management capabilities specific to deep learning. Its unique ability to handle deep learning pipelines' orchestration, version control, and automation sets it apart. I chose Valohai because it simplifies the complexity of managing these projects, making it the best fit for this purpose.

Standout Features & Integrations:

Valohai's most noteworthy features are its full pipeline versioning and automated experiment tracking, which simplify managing deep learning projects. The software also offers advanced coordination features for multi-node executions. It integrates well with a wide range of tools such as TensorFlow, PyTorch, and Keras and data stores like Amazon S3, Google Cloud Storage, and Azure Blob Storage.


Pricing for Valohai starts at $800/user/month (min 5 seats). This fee includes full access to their pipeline management and automated tracking tools.


  • Robust management of deep learning projects
  • Excellent integration with popular tools and data stores
  • Full pipeline versioning and automated tracking


  • High starting price
  • Minimum requirement of 5 users
  • Might be too specialized for smaller projects

3. Encord - Best for active traffic management solutions

Encord sample dashboard
Take a look at this screenshot of the Encord sample dashboard.

Encord shines in the sphere of traffic management solutions. It applies AI to automate and improve traffic management, leading to more efficient and safer roads. Its ability to respond actively to dynamic road conditions makes it the prime choice for traffic management solutions.

Why I Picked Encord:

After carefully comparing and judging various options, I picked Encord for its specialized application in traffic management. Its unique ability to apply AI for real-time, dynamic traffic control sets it apart. Based on these merits, I firmly believe Encord is the best for active traffic management solutions.

Standout Features & Integrations:

One of Encord's significant features is its predictive traffic management, which anticipates traffic flow based on historical and real-time data. It also provides an incident management system that proactively handles any road incidents. While specific integration information is not readily available, Encord's technology is designed to fit into existing traffic management systems, indicating compatibility with diverse platforms.


Encord's pricing is not publicly disclosed and is available upon request.


  • Specialized in traffic management solutions
  • Predictive traffic management capabilities
  • Proactive incident management


  • Pricing not readily available
  • Might be too specialized for non-traffic related uses
  • Integration specifics not detailed

4. Weights & Biases - Best for tracking machine learning experiments

Weights & Biases is one the top AI software
Weights & Biases simplifies experiment tracking, data management, and team collaboration.

Weights & Biases specialize in assisting machine learning engineers with tracking and managing their investigations. With the tool, you can monitor your models in real-time, a critical factor in why it's the best for monitoring machine learning experiments.

Why I Picked Weights & Biases:

In the search for a tool with robust experiment tracking, Weights, and biases caught my attention. Its commitment to making model tracking efficient and easy was the deciding factor. Consequently, I believe Weights & Biases is the best for monitoring machine learning experiments due to these unique capabilities.

Standout Features & Integrations:

Weights & Biases offers features like experiment tracking, dataset versioning, and result visualization. These features enable ML engineers to have a more streamlined workflow. Furthermore, it provides integrations with popular tools such as PyTorch, TensorFlow, and Keras, allowing for easy coordination across multiple platforms.


Weights & Biases pricing starts from $99/user/month (min 5 seats).


  • Comprehensive experiment tracking
  • Dataset versioning capabilities
  • Integrates with popular ML tools


  • High starting price point
  • Minimum seat requirement for pricing
  • May not suit non-ML workflows

5. SAP HANA Cloud - Best for real-time business data processing

SAP HANA Cloud database overview
Here's SAP HANA Cloud showing revolutionized enterprise data analytics and intelligent applications.

SAP HANA Cloud is a powerful, in-memory database management system offering real-time data processing. Its ability to handle vast data quickly and efficiently underscores why it's best for real-time business data processing.

Why I Picked SAP HANA Cloud:

I picked SAP HANA Cloud due to its unique blend of high-performance and comprehensive data management features. Its ability to provide real-time data processing sets it apart from many other database management systems. Therefore, SAP HANA Cloud is best for real-time business data processing, given these exceptional attributes.

Standout Features & Integrations:

SAP HANA Cloud offers multi-model data processing and advanced analytics, both vital in a data-intensive environment. Additionally, it has an impressive integration list that includes SAP Analytics Cloud, SAP Data Warehouse Cloud, and other SAP products, facilitating data sharing and analysis across platforms.


SAP HANA Cloud pricing starts from $150/user/month. Please note this is the monthly base fee and other pricing tiers are available.


  • Provides real-time data processing
  • Multi-model data processing capability
  • Robust integrations with SAP suite


  • Higher starting price
  • Potentially complex for small businesses
  • Predominantly integrates with SAP products

6. SuperAnnotate - Best for automating image annotation tasks

SuperAnnotate automating images feature screenshot
Here's SuperAnnotate automating images feature and attribution setting.

SuperAnnotate provides an advanced platform for automating image annotation tasks, which is essential in machine learning models. Its ability to streamline and accelerate the annotation process, reducing the workload on data scientists, aligns with its reputation as the best tool for automating image annotation tasks.

Why I Picked SuperAnnotate:

I selected SuperAnnotate based on its specialization in automating image annotation tasks, which is typically time-consuming and demanding. Its distinctiveness lies in the efficient annotation tools and machine learning algorithms it offers, which are adept at handling image annotation. Therefore, SuperAnnotate is the best for automating image annotation tasks.

Standout Features & Integrations:

SuperAnnotate stands out with its smart annotation tools, AI-powered predictive annotation, and team collaboration capabilities. Moreover, it offers key integrations with data storage platforms like AWS S3, GCP, and Azure, enabling users to pull and push datasets directly from and to their cloud storage.


Pricing for SuperAnnotate starts at $20/user/month, which is the fee for their basic paid plan.


  • Efficient image annotation tools
  • AI-powered predictive annotation
  • Strong integrations with major data storage platforms


  • May not be cost-effective for small teams
  • High learning curve for non-technical users
  • Limited features in the basic plan

7. Saturn Cloud - Best for collaborative data science projects

Saturn Cloud device sample
Here's a screenshot of Saturn Cloud showing how to create a resource.

Saturn Cloud provides a platform that allows data scientists to collaborate on projects, utilizing Dask and RAPIDS for parallel computing. Its strengths in facilitating team-based data science efforts and boosting efficiency through parallel computing make it the best tool for collaborative data science projects.

Why I Picked Saturn Cloud:

I selected Saturn Cloud because it can foster collaboration among data scientists, an essential factor in today's data-driven industries. Its ability to leverage Dask and RAPIDS for parallel computing makes it unique, enhancing the processing of large datasets. Saturn Cloud is the best tool for collaborative data science projects, enabling teams to work together and process large data volumes efficiently and effectively.

Standout Features & Integrations:

Saturn Cloud offers impressive features such as shared workspaces, version control, and parallel computing with Dask and RAPIDS. Additionally, it integrates well with major cloud platforms such as AWS and Google Cloud, facilitating easy data transfer and storage.


Saturn Cloud's pricing begins at $300/user/month (billed annually). This provides users with comprehensive features for collaborative data science projects.


  • Effective collaboration tools
  • Robust parallel computing capabilities
  • Strong integrations with major cloud platforms


  • High cost may deter small teams or individual users
  • Learning curve for beginners
  • Limited features at lower price points

8. Aporia - Best for monitoring AI model performance

Aporia Insurance sales dashboard
Here's what Aporia Insurance Sales dashboard looks like.

Aporia is a dedicated platform that aids in the monitoring and managing of machine learning models in production environments. Given its abilities to monitor performance, detect drifts, and provide explainability for AI models, it emerges as the best tool for AI model performance monitoring.

Why I Picked Aporia:

Aporia stood out to me due to its robust capabilities built explicitly for monitoring AI model performance. Its unique feature is the ability not just to monitor but also to provide explainability for AI models, which is crucial in understanding how the models make decisions. Given its specific focus on monitoring and its advanced capabilities, Aporia fits the bill as the best tool for monitoring AI model performance.

Standout Features & Integrations:

Some of the critical features of Aporia include continuous monitoring, drift detection, and model explainability. The platform offers insightful dashboards and alerts that clearly understand the AI model's performance. Aporia integrates well with standard data science tools and platforms such as TensorFlow, PyTorch, and Scikit-learn.


Pricing for Aporia starts at $500/user/month, giving users access to a complete suite of AI model monitoring tools.


  • In-depth AI model performance monitoring
  • Offers model explainability
  • Integrates with popular data science tools


  • May be too costly for small businesses or individual users
  • May require technical know-how to get the most out of it
  • Could be an overkill for simpler AI models

9. SAS Model Manager - Best for enterprise-scale model management

SAS Model Manager dashboard screenshot
This is a screenshot of the SAS Model Manager showing project panes.

SAS Model Manager is an end-to-end solution for managing, monitoring, and deploying analytical models. It's specifically designed to handle the complexity and scale of enterprise data, making it the best tool for enterprise-scale model management.

Why I Picked SAS Model Manager:

I selected SAS Model Manager for its strong capability in managing analytical models at an enterprise scale. This tool's capacity to integrate with other SAS solutions and handle various analytical models separates it from the pack. Based on these unique features and capabilities, SAS Model Manager is the best choice for managing models at an enterprise level.

Standout Features & Integrations:

The most significant features of SAS Model Manager include its model comparison and assessment capabilities, end-to-end management, and the ability to deploy models for real-time scoring. Integration-wise, SAS Model Manager works efficiently with SAS solutions such as SAS Visual Analytics and SAS Enterprise Miner.


Pricing for SAS Model Manager starts at $1000/user/month. This pricing includes the complete functionality for managing, deploying, and monitoring analytical models at scale.


  • Comprehensive solution for managing, monitoring, and deploying analytical models
  • Can handle a wide range of analytical models
  • Easy integration with other SAS solutions


  • Higher cost compared to other tools
  • The interface might be complex for beginners
  • May require extensive SAS knowledge for maximum utilization

10. Databricks Lakehouse Platform - Best for unified analytics solutions

Databricks Lakehouse Platform is one of the AI software
A screenshot of Databricks Lakehouse Platform showing the review SQL endpoints.

The Databricks Lakehouse Platform combines the best elements of data lakes and data warehouses to offer a unified analytics platform. It provides a secure, scalable, and reliable solution for data engineering, data science, machine learning, and analytics, which justifies its position as the best choice for unified analytics solutions.

Why I Picked Databricks Lakehouse Platform:

When choosing a platform offering a comprehensive analytics solution, the Databricks Lakehouse Platform was my top choice. Its ability to combine data lakes' flexibility with data warehouses' reliability and performance distinguishes it from other tools. The platform’s broad capabilities and unified approach make it the best tool for suitable analytics solutions, in my opinion.

Standout Features & Integrations:

Noteworthy features of the Databricks Lakehouse Platform include its Delta Lake feature, which ensures reliability for data lakes, and the built-in machine learning capabilities. The platform integrates well with various data sources and data visualization tools such as Tableau and PowerBI, making it a versatile option for diverse analytics needs.


Pricing for Databricks Lakehouse Platform starts from $99/user/month. Additional details can be found on their official website or by contacting their sales department.


  • Combines the best features of data lakes and data warehouses
  • Built-in machine learning capabilities
  • Broad integrations with various data sources and visualization tools


  • The cost may be prohibitive for smaller organizations
  • Steep learning curve for users new to the platform
  • Some users have reported occasional issues with job scheduling and execution

11. Vertex AI - Best for end-to-end AI model development

Vertex AI datasets overview
Here's how to create a dataset in Vertex AI.

Vertex AI, a product by Google Cloud, is designed to manage the entire AI lifecycle, including building, deploying, and scaling AI models. It offers an integrated environment for machine learning development, from data preparation to model training and deployment, which underscores its aptness for end-to-end AI model development.

Why I Picked Vertex AI:

In selecting tools for AI model development, I was drawn to Vertex AI for its unified and user-friendly environment. Its ability to manage the entire cycle of machine learning tasks makes it stand out from other tools. Its robust feature set and integration with the Google Cloud ecosystem also underscore why it is the best choice for end-to-end AI model development.

Standout Features & Integrations:

Vertex AI offers powerful features such as AutoML, AI Platform Pipelines for workflow orchestration, and Explainable AI for understanding model behavior. Additionally, it provides easy integration with other Google Cloud products, including BigQuery for data management and Dataflow for batch and stream processing.


Pricing for Vertex AI is usage-based and starts from $10/user/month (billed annually). More specific pricing details can be obtained by contacting Google Cloud directly or through their online pricing calculator.


  • Integrated environment for full-cycle machine learning tasks
  • Easy integration with the Google Cloud ecosystem
  • Powerful feature set including AutoML and Explainable AI


  • Pricing can be complex due to its usage-based model
  • May be less accessible to organizations not already using Google Cloud
  • Some users have reported a steep learning curve

12. IBM Watson Studio - Best for building, training, and managing AI models

IBM Watson Studio sales summary dashboard screenshot
This is a screenshot of the IBM Watson Studio sales summary dashboard.

IBM Watson Studio is a robust integrated environment designed for data scientists, developers, and domain experts to build, train, and manage AI models. Its flexible tools and services for end-to-end model development and deployment make it particularly suitable for building, training, and managing AI models.

Why I Picked IBM Watson Studio:

In selecting tools for AI development, I gravitated towards IBM Watson Studio due to its comprehensive suite of capabilities and renowned reputation in the AI space. What differentiates Watson Studio is its versatility and ease of use, catering to various skill levels from novice to expert. With these attributes, it's clear why it's best suited for building, training, and managing AI models.

Standout Features & Integrations:

IBM Watson Studio brings to the table a multitude of features, including AutoAI for automated machine learning, a notebook interface for coding, and Watson Machine Learning for model deployment. It also integrates well with other IBM services like IBM Cloud Object Storage and IBM Watson Knowledge Catalog and popular data science tools such as Jupyter and RStudio.


Pricing for IBM Watson Studio begins at $99/user/month. It should be noted that this is for the Standard plan, with a more feature-rich Enterprise plan available at a higher cost.


  • Comprehensive suite of tools for end-to-end AI model development
  • Easy integration with other IBM services and popular data science tools
  • Suitable for a range of skill levels


  • Higher starting price compared to some other tools
  • Some features are only available on the higher-priced Enterprise plan
  • Interface might be overwhelming for complete beginners

Other Noteworthy AI Software Tools

Below is a list of additional AI software that I shortlisted but did not make it to the top 12. Definitely worth checking them out.

  1. Datature - Good for streamlining annotation workflows
  2. InRule - Good for businesses seeking decision-management solutions
  3. - Good for improving experimentation processes in machine learning
  4. Mona - Good for efficient model monitoring
  5. MLJAR - Good for automated machine learning
  6. Pachyderm - Good for data versioning and pipeline management
  7. Algorithmia - Good for AI lifecycle management
  8. Gurobi Optimizer - Good for mathematical optimization
  9. - Good for experiment tracking and model management
  10. Arize AI - Good for machine learning model performance monitoring
  11. Determined AI - Good for deep learning training platforms
  12. Red Hat MLOps - Good for large enterprises looking to scale machine learning operations
  13. ClearML - Good for automating machine learning workflows
  14. Kili - Good for data annotation in machine learning projects
  15. Pixis AI Infrastructure - Good for managing and optimizing AI infrastructure

Selection Criteria for Choosing AI Software

After personally testing and researching over 20 AI model management tools, I identified specific criteria that truly matter when choosing the best software in this category. I assessed these tools based on core functionality, key features, and usability, focusing on aspects particularly relevant to AI and machine learning practitioners.

Core Functionality

  • The tool should support the end-to-end process of developing, deploying, and maintaining AI models.
  • It should provide capabilities for model versioning, allowing users to track and manage different iterations of their models.
  • The software should have functionalities for model monitoring, which helps assess models’ performance over time.

Key Features

  • Automated Machine Learning (AutoML): Enables users to automate applying machine learning to real-world problems.
  • Integration with Various Data Sources: Allows users to pull in data from various sources, making it easier to feed data into their models.
  • Robust API: Provides more flexibility in how users interact with the tool, often allowing for better integration with existing systems.
  • Scalability: The ability to handle increasing amounts of work and to accommodate growth is vital for organizations as their data volume and model complexity grow.


  • The software should offer an intuitive interface where users can easily navigate and find the needed features.
  • It should provide comprehensive documentation and learning resources to help users understand how to use the tool effectively.
  • Reliable customer support is a must, enabling users to resolve issues and questions they might encounter quickly.
  • A structured training program or learning library for onboarding users is essential for complex enterprise solutions.
  • Considering the collaborative nature of AI projects, the tool should support role-based access control, making it easy to manage who can access and modify different parts of the project.

Most Common Questions Regarding AI Software (FAQs)

What are the benefits of using AI software tools?

AI software tools offer a range of benefits. They can automate repetitive tasks, enabling your team to focus on strategic, high-level work. These tools can also provide insights from large datasets, helping businesses make data-driven decisions. With AI software, you can personalize customer experiences, improving customer satisfaction. Moreover, AI tools can enhance risk management by predicting and mitigating potential issues. Finally, they support developing and managing sophisticated AI models, making it easier for businesses to leverage AI technologies.

How much do AI software tools typically cost?

The cost of AI software tools can vary greatly, depending on the specific functionalities offered and the pricing model employed by the software provider. Some tools offer a freemium model, where basic functionalities are free, and additional features come at a cost.

What are the typical pricing models for AI software tools?

The pricing models for AI software tools often include tiered, per-user, or usage-based models. The tiered model has different pricing tiers with increasing functionalities. The per-user model charges based on the number of users, and the usage-based model calculates the cost based on the usage of resources or features.

What is the typical range of pricing for AI software tools?

The range of pricing for AI software tools can span from free for basic versions or smaller teams, up to several thousand dollars per month for enterprise-grade solutions. This range reflects the varied nature of AI tools, which can be designed for different sizes of teams and different levels of complexity.

What are the cheapest and most expensive AI software tools?

The cheapest AI software tools often offer freemium models like Google's Vertex AI and IBM's Watson Studio, which have free plans for limited usage. The most expensive AI tools are typically designed for enterprise use and could reach several thousand dollars per month, such as Databricks and Datature.

Are there any free AI software tool options?

Yes, some AI software tools offer free options. Google's Vertex AI and IBM's Watson Studio both offer free tiers. These options may have limitations in terms of features or usage limits, but they can be a good starting point for small teams or individual users.


To wrap up, selecting the best AI software depends mainly on your specific use case and business needs. From managing and developing AI models to providing comprehensive analytics solutions, there's an array of software to meet varied needs. Whether Databricks, IBM Watson Studio, Google's Vertex AI, or any other tool, the choice hinges on aligning the tool's capabilities with your objectives.

Key takeaways:

  1. Identifying Core Functionality: Understand the core functionality that your business needs from an AI software tool. This could range from automating repetitive tasks to gaining insights from vast datasets or managing sophisticated AI models.
  2. Evaluating Key Features: Evaluate the key features that the AI software offers. Consider the ones that are crucial for your specific use case. This might include features like easy model deployment, end-to-end model development, or integrations with other tools in your tech stack.
  3. Considering Usability: Usability is crucial when choosing an AI software tool. A tool that has an intuitive design, easy onboarding, a user-friendly interface, and reliable customer support will add significant value to your business processes. Ensure the chosen tool meets these usability standards.

What do you think?

I hope this guide has helped provide you with some direction on choosing the best AI software that fits your needs. However, the power of AI is vast and rapidly evolving. If you think there's a tool that deserves to be on this list, please suggest it. I’m always eager to learn about new and practical solutions. After all, my main goal is to provide the most accurate, up-to-date, and helpful information to my readers. So, your contributions and feedback are highly appreciated!

By Paulo Gardini Miguel

Paulo is the Director of Technology at the rapidly growing media tech company BWZ. Prior to that, he worked as a Software Engineering Manager and then Head Of Technology at Navegg, Latin America’s largest data marketplace, and as Full Stack Engineer at MapLink, which provides geolocation APIs as a service. Paulo draws insight from years of experience serving as an infrastructure architect, team leader, and product developer in rapidly scaling web environments. He’s driven to share his expertise with other technology leaders to help them build great teams, improve performance, optimize resources, and create foundations for scalability.