Top 15 Artificial Intelligence Platforms in 2024

Whether you know it or not, there’s an AI tool for every task you can possibly imagine. 

At the very least, AI can help you build the tools you need to get the job done.

Too good to be true? I thought so, too. Keeping this in mind, I’ve explored 15 different AI platforms to help you find the one that best fits your needs.

Whether you’re simply exploring AI tools or looking to tackle a specific problem, you’ll find your match here. 😉

Let’s have a look!

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What Should You Look for In an AI Platform? 

While testing AI platforms, I discovered that choosing one isn’t a straightforward ‘shopping’ task. I had to consider several aspects to map the platform’s AI capabilities to my goals to check whether I could use it to its full potential.

Here is the gist:

• Be clear about the type of machine learning algorithm the AI platform uses. It could be supervised, semi-supervised, unsupervised, or reinforcement. This determines how your ML model evolves
• Ensure that the AI platform is able to reduce the manual workload on you or your employees depending on the use case
• Make sure the machine learning algorithms provide an output that is readable by humans
• Consider how long an ML algorithm takes to learn your working patterns and processes. This is the time it will take to produce accurate outputs
• Check how long it will take to integrate the AI platform with your systems and implement it—whether your business can afford the downtime 

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The 15 Best AI Platforms to Use in 2024

I tested 15 of the most popular AI-backed tasking platforms in 2024 and compiled a list of their best features, limitations, prices, and reviews. Here we go:

1. ClickUp (Best for AI automation, data processing & project management)

ClickUp is a versatile project management platform that combines the power of docs, chats, AI, and goals into a centralized digital space.

ClickUp Brain, the platform’s inbuilt AI feature, has to be the most efficient work AI I have tested so far. This is the world’s first neural network that connects your tasks, documents, team members, and company knowledge base through AI.

It allows users like me to create custom workflows, automate repetitive tasks, and streamline work processes. By setting up triggers and rules, I can automate actions like sending notifications, assigning tasks, updating statuses, and more.

This not only saves time but reduces the risk of human error and ensures tasks are completed consistently. With ClickUp Brain, you can transform your project management from manual to automated, increasing productivity.

Need an update on the project status or documentation? Just ask the AI! With its AI Knowledge Manager capabilities, ClickUp Brain can pull data for you from anywhere in the workplace. Plus, I no longer worry about writing emails or creating project documentation from scratch because the AI writer does the job for me!

From AI-powered search to end-to-end project management, ClickUp Brain offers all my preferred features in a nice package. 🎁

ClickUp best features

• Utilize Brain to gain visibility on action items in ClickUp Tasks to organize your workload 
• Automate your tasks, stand-ups, action items, and progress updates using ClickUp Automation
• Collaborate with your team to efficiently brainstorm and implement ideas using ClickUp Whiteboards
• Summarize chat threads and text from ClickUp Docs such as meeting notes, wikis, articles, etc.
• Transcribe voice notes and video clips created using ClickUp Clips
• Customize your workflows and simplify them through the 1,000+ integrations with tools such as Slack, Google Drive, GitHub, Dropbox, and many more

ClickUp limitations

• ClickUp Brain is a part of the ClickUp ecosystem and is not available as a standalone tool

ClickUp pricing

• Free Forever
• Unlimited: $7/month per user
• Business: $12/month per user
• Enterprise: Contact for pricing
• ClickUp Brain: Add to any paid plan for $7 per member per month

ClickUp ratings and reviews

• G2: 4.7/5 (9,000+ reviews)
• Capterra: 4.6/5 (4,000+ reviews)

2. OpenAI ChatGPT (Best for customer support and internal operations)

OpenAI’s ChatGPT is best known in the AI industry for its quick generative capabilities. I have been using this tool to get help with writing, using AI and machine learning, ideating, brainstorming, and other tasks that this generative AI was designed for.

One feature that I found really interesting in ChatGPT was the voice conversation option that appears in the mobile app. By putting your headphones on and tapping on the headphone icon on the app, you can ‘talk’ to ChatGPT!

OpenAI ChatGPT best features

• Explore ideas, write and edit content easily
• Translate text to different languages
• Generate code and debug faults
• Analyze your data using ChatGPT to spot patterns and trends faster

OpenAI ChatGPT limitations

• Being trained on large language models, ChatGPT is not free from bias. This may impact your outputs
• This AI is purely generative and can only perform limited tasks

OpenAI ChatGPT pricing

• Free
• Plus: $20/month per user
• Team: $30/month per user
• Enterprise: Contact sales

OpenAI ChatGPT ratings and reviews

• G2: 4.7/5 (583 reviews)
• Capterra: 4.6/5 (57 reviews)

3. DataRobot (Best for building and deploying predictive models)

DataRobot is an award-winning AI platform for building data science and machine learning products. I tested it to build a predictive analytics model since it specializes in generative and predictive AI for businesses. Data Robot features a guided workflow that helps non-technical users like me through the entire model-building process.

This tool comes in handy when I want to create custom AI applications that integrate seamlessly with my systems and other applications. The platform makes it easy for me to connect, automate, and optimize my business processes through simple integrations.

DataRobot best features

• Explores and tests various algorithms and hyperparameters to find the best-performing model for your specific task
• Automates manual steps involved in model building reducing the time and effort required
• Offers features that help you understand how your models make predictions, improving transparency and trust

DataRobot limitations

• DataRobot is computationally resource-extensive, which can slow systems down
• There are limitations to dataset sizing which is capped at 100 GB for all combines and 11 GB per dataset—I found this restrictive for larger projects

DataRobot pricing

• Custom pricing

DataRobot ratings and reviews

• G2: 4.4/5 (20+ reviews)
• Capterra: N/A (less than 20 reviews)

4. TensorFlow (Best suited for NLP applications)

TensorFlow is an end-to-end platform for artificial intelligence. My team tested it to create ML models that can run in any environment. TensorFlow is particularly helpful in accurately analyzing relational data with the help of its graph neural networks.

Because of its analytical power, TensorFlow is ideal for advanced research applications. Many organizations use TensorFlow to build recommendation systems (think Netflix movie recommendations) using reinforcement learning models to enhance customer satisfaction with their platforms.

TensorFlow best features

• Build and train complex neural networks easily with a high-level API called Keras 
• Monitor and analyze the training process of machine learning models using the TensorBoard visualization tool
• Optimize TensorFlow graphs for faster execution on diverse hardware on the XLA compiler

TensorFlow limitations

• The platform is relatively tougher to learn as compared to other tools
• You need stronger GPUs or TPUs to work with TensorFlow as it is very resource-intensive

TensorFlow pricing

• TensorFlow is open-source and free to download and install

TensorFlow ratings and reviews

• G2: 4.5/5 (60+ reviews)
• Capterra: 4.6/5 (100+ reviews)

5. Microsoft Azure (Best for AI-powered business solutions)

Microsoft Azure is a cloud computing platform, and AI is a major part of it. I found it helpful for developing AI-backed business solutions (like chatbots and virtual assistants) since it is a scalable and flexible platform and offers a wide toolset.

Overall, it is an excellent all-rounder with a collection of AI functionalities. You can use its open and multimodal models for innovation, leverage RAG (Retrieval Augmented Generation) for contextual development of AI bot interactions, and use the LLMs to manage your AI lifecycle better.

Microsoft Azure best features

• Build, train, and deploy machine learning models using Azure Machine Learning 
• Use built-in AI models for ready tasks like computer vision, speech recognition, and more, which Azure Cognitive Services provides 
• Build and deploy chatbots using Azure Bot Service 
• Analyze big data easily using Azure Data Lake Analytics

Microsoft Azure limitations

• While it’s an excellent AI platform, MS Azure is dependent on the Microsoft ecosystem
• Certain Azure AI services are region-specific, which might impact global business operations

Microsoft Azure pricing

• Custom pricing

Microsoft Azure ratings and reviews

• G2: 4.4/5 (2,000+ reviews)
• Capterra: 4.6/5 (1,500+ reviews)

6. Google Cloud AI (Best for integrating AI into existing apps)

Google Cloud AI is a cloud-based AI machine learning platform, which I have been using to source pre-trained AI models for tasks such as image recognition and NLP. It is particularly sophisticated when plugged in with Vertex AI because it eliminates the technical configuration bit of using AI.

This tool is a great fit for people who want to quickly summarize large documents or set up a chat app powered by RAG. I also tried it to deploy image-processing pipelines that are backed by preconfigured AI/ML modules.

Google Cloud AI best features

• Use AutoML, which is a suite of AI tools, to build custom machine-learning models with minimal coding
• Leverage Dialogflow to build conversational interfaces like chatbots
• Apply Vision AI, a set of APIs, to image and video analysis, facial recognition, etc.
• Understand and process human language for sentiment analysis using NLP-enabled tools to 

Google Cloud AI limitations

• Users find the vendor lock-in limiting as it traps you in the Google systems and makes it difficult to migrate

Google Cloud AI pricing

• Custom pricing

Google Cloud AI ratings and reviews

• G2: N/A (less than 20 reviews)
• Capterra: N/A (less than 20 reviews)

7. Vertex AI (Best for fraud detection and predictive maintenance)

Vertex AI is a managed machine learning platform by Google Cloud. I use it to build, deploy, and scale my ML models. The tool provides smooth integrations into my systems when I use Google Cloud Services.

Vertex AI has recently been enhanced with Google’s Gemini models, expanding its capabilities further with generative tasks as well. As a new customer, you receive $300 in free credits which will allow you try Vertex AI with full liberty. You can use these credits anywhere on Google Cloud products.

Vertex AI’s best features

• Automate the building and training of ML models on tabular data using AutoML Tables
• Achieve easy integration with other Google services like Dataflo and Cloud Storage using MLOps integrations
• Manage model performance, drift detection, and retraining using readily available, dedicated tools

Vertex AI limitations

• The model interpretability is not transparent, making it difficult to understand the decision-making process of ML models
• Vertex AI is part of the Google franchise, so it is dependent on the Google ecosystem

Vertex AI pricing

• Custom pricing

Vertex AI ratings and reviews

• G2: 4.3/5 (430+ reviews)
• Capterra: N/A (less than 20 reviews)

8. H2O.ai (Best for on-premises deployment)

H2O.ai is a handy, open-source, distributed machine learning platform that’s ideal for managing big data. It has helped me create applications like image recognition and fraud detection through diverse algorithms (like deep learning and gradient boosting).

With the latest Danube3-4B release, the platform draws from a training base of 6 million tokens, helping you achieve a stellar accuracy of 80% (on a 10-shot HellaSwag Benchmark). This update is aimed at democratizing NLP capabilities to the masses.

H2O.ai best features

• Scale efficiently across multiple machines using distributed machine learning
• Use faster processing and speedy algorithms with in-memory computing
• Automate handling missing values, creating interaction terms, and more with automatic feature engineering

H2O.ai limitations

• While H2O.ai is relatively easy to use, working with complex models on this platform can get tricky
• The platform creates complexities with scaling larger datasets

H2O.ai pricing

• Custom pricing

H2O.ai ratings and reviews

• G2: 4.5/5 (20+ reviews)
• Capterra: N/A

9. IBM Watson Assistant (Best for self-service customer support)

IBM Watson Assistant is a conversational AI platform you can use to build genAI assistants specifically for customer self-service stations. It can help you design natural and fluid chatbots for internal applications and front-end customer assistance.

The conversation builder has a drag-and-drop interface, which I consider crucial for defining user-friendliness for any AI platform. Additionally, the fact that this platform is augmented with RAG makes it that much more accurate and effective.

IBM Watson Assistant best features

• Use out-of-the-box LLMs, NLPs, NLUs (refer to this AI glossary to understand these terms), and Intelligent Context Gathering to improve contextual understanding of conversations
• Leverage RAG for accurate and updated conversations
• Customize model training that allows your business to use its own training data on IBM Watson Assistant models

IBM Watson Assistant limitations

• The performance of the assistant created using this platform is significantly impacted by the quality of training data

IBM Watson Assistant pricing

• Custom pricing

IBM Watson Assistant ratings and reviews

• G2: 4.4/5 (300+ reviews)
• Capterra: N/A (less than 20 reviews)

10. Amazon Sagemaker (Best for scaling large models)

Amazon Sagemaker is an AI platform with fully managed cloud infrastructure, workflows, and tools to make your work easier. It has helped me build, train, and easily deploy ML models for a variety of use cases like fraud detection and reinforcement learning.

What I like best about Amazon Sagemaker is that it provides support for governance requirements that simplify access controls and enhance transparency over your ML projects. This tool is quite handy for building your own foundation models and finetuning them.

Amazon Sagemaker best features

• Use no-code tools (for non-professionals) and IDEs (for data scientists) to work easily regardless of expertise
• Enhance auditability and transparency with MLOps practices and their standardization
• Build foundation models from scratch for additional revenue streams

Amazon Sagemaker limitations

• Even with the pay-as-you-go model, the costs can escalate quickly for large-scale projects

Amazon Sagemaker pricing

• Custom pricing

Amazon Sagemaker ratings and reviews

• G2: 4.2/5 (38 reviews)
• Capterra: N/A (less than 20 reviews)

11. PyTorch (Best for R&D and computer vision)

PyTorch is a framework you can use to build deep learning models for applications like NLP. I have successfully used it to make image recognition apps and generative models using dynamic computation graphs.

As a production-ready platform, it lets you transition easily between immediate execution (Eager mode) and compiled execution (Graph mode) using TorchScript, which I think is quite efficient. Additionally, the platform uses a torch-distributed backend to scale and optimize the distributed training and performance.

PyTorch best features

• Make changes to network structure on the fly using Dynamic Computational Graph 
• Speed up LLM training using the strong GPU support in PyTorch
• Leverage a rich library of pre-built deep learning modules to build solutions
• Get access to easy support through a robust community and extensive documentation

PyTorch limitations

• The dynamic computational graph has an inefficient memory management
• It has a steeper learning curve and takes time to understand and use

PyTorch pricing

• Custom pricing

PyTorch ratings and reviews

• G2: 4.6/5 (21 reviews)
• Capterra: N/A

12. Keras (Best for rapid prototyping)

Keras is a high-level API that I have used to develop apps based on NLP and generative models. It has helped me create prototypes easily because of its simple interface.

Keras focuses on debugging speed, maintainability, deployability, and code elegance.

I like that my codebase is comparatively smaller when I’m working with Keras—it helps with readability and makes my iterations easier. Additionally, its XLA compilations make the models run faster with JAX and TensorFlow, and they are easier to deploy across various surfaces.

Keras best features

• Accelerate the development process using pre-trained models like VGG, ResNet, and InceptionNet
• Integrate with popular vision libraries like TensorBoard using visualization tools 
• Run the modular framework on top of various deep learning engines like Theano

Keras limitations

• The backend frameworks significantly influence Keras’s capabilities, importing backend issues into the output
• Users say it may create problems for developing customized architectures

Keras pricing

• The platform is an open-source AI and is free to download and install

Keras ratings and reviews

• G2: 4.6/5 (60+ reviews)
• Capterra: 4.6/5 (40+ reviews)

13. SAP Hana Cloud (Best for processing large datasets)

SAP Hana Cloud is a database management system that has helped me build and launch data applications at a large scale. I have used it for real-time data analysis, fraud detection, predictive analytics, and real-time reporting.

This multimodal DBMS (Database management system) provides a limitless database for all kinds of workloads. You can use it to build intelligent apps that are capable of utilizing generative AI and context awareness. 

SAP Hana Cloud best features

• Achieve efficient exploration and analysis of data using the intelligent data discovery feature
• Analyze complex network relationships using graph processing features 
• Forecast future business trends, identify patterns, and more using predictive analytics

SAP Hana Cloud limitations

• SAP Hana Cloud can be expensive when it comes to large-scale deployments
• The vendor lock-in can be pretty strong on this platform, making migration tough

SAP Hana Cloud pricing

• Custom pricing

SAP Hana Cloud ratings and reviews

• G2: 4.3/5 (500+ reviews)
• Capterra: 4.5/5 (40+ reviews)

14. Alteryx Intelligence Suite (Best for data preparation and blending)

Alteryx Intelligence Suite is a powerful platform for data analytics, combining the capabilities of data preparation, blending, and ML. I have used its AI solutions to create predictive models, analyze large datasets, and get actionable insights.

This tool is particularly handy in extracting data from PDFs and images using Google Tesseract OCR. In fact, whenever I worked with unstructured or semi-structured data, Alteryx was able to extract it easily without requiring me to code anything. 

Alteryx Intelligence Suite best features

• Use AutoML to build predictive applications with minimal manual involvement
• Build apps for object detection, image classification, OCR, and more using computer vision
• Use predictive analytics for forecasting, classification, regression, and more

Alteryx Intelligence Suite limitations

• This platform requires exhaustive computational resources for large datasets
• Users faced integration challenges that required technical expertise with complex infrastructures

Alteryx Intelligence Suite pricing

• Custom pricing

Alteryx Intelligence Suite ratings and reviews

• G2: N/A 
• Capterra: N/A 

15. Claude (Best for generative tasks)

Claude is a large natural language processing model that can be used for generating human-quality text, writing creative content, and translating text. It has helped me write emails and answer questions.

This model has been trained using Constitutional AI to make it safe, more accurate, and secure. The best part is that it allows me to create a team account when I want to collaborate on projects. Additionally, I can add my own resources, knowledge, or articles to Claude’s research or background work to give depth to the results.

Claude best features

• Perform sophisticated reasoning tasks beyond pattern recognition
• Code in various programming languages using code generation capabilities
• Analyze any data in photos, text, graphs, and more using vision analysis

Claude limitations

• It depends heavily on training data, which may incorporate inaccuracies in output

Claude pricing

• Free
• Pro: $20/month per user
• Team: $25/month per user

Claude ratings and reviews

• G2: 4.7/5 (23 reviews)
• Capterra: N/A (less than 20 reviews)

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Bringing AI Into The Workforce: ClickUp

Organizational workspaces have evolved dramatically. Where employees used to always carry a notepad and pencil, now an AI assistant accompanies them everywhere to record and take necessary notes, generate insights, create custom apps, and automatically schedule meetings.

In this blog, I’ve shared 15 AI tools to help you cover all the internal business functions. With these, I could execute a wide range of tasks, from AI workflow automation to content generation.

But as an all-rounder, I  recommend ClickUp Brain. It truly connects different aspects of my workspace together and reduces turnaround times for executing tasks. If you are trying to build seamless workflows, ClickUp should be at the top of your list.

Make ClickUp your go-to tool for productivity enhancement today—sign up for free.