Top 5 NLP Platforms & Comparison in 2025

There are numerous API providers in the chatbot landscape, the majority of them are focusing on Natural Language Processing (NLP) and Natural Language Understanding (NLU). It is the crucial step to decide since it will be handling the most important step in a conversational interface.

Natural Language Platforms (NLP) comparison

Below table is the comparison of these five NLP platforms based on their features. Table is sorted out alphabetically.

For large language model comparisons: 10+ Large Language Model Examples – Benchmark & Use Cases

For comparison of LLMs UI & API features: What is Large Multimodal Models (LMMs)? LMMs vs LLMs

Top 5 NLP platforms

1. Azure Luis

Azure’s Language Understanding (LUIS) is a machine learning-based service that helps developers integrate natural language understanding into applications, bots, and IoT devices. LUIS offers an easy way to create custom models that continuously improve over time. It is best for businesses already within the Microsoft ecosystem.

However, LUIS can be complex to set up, and while it supports various use cases, it does not provide as many pre-built templates or out-of-the-box solutions as other platforms. LUIS is ideal for companies seeking to build custom NLP models for enterprise applications, especially those that require integration with Microsoft Azure and its cloud services.

2. Dialogflow

Dialogflow provides tools for building both text and voice-based conversational agents, with capabilities for speech-to-text and text-to-speech. It is best suited for applications requiring multi-channel support, such as chatbots and virtual assistants that interact with messaging platforms (e.g., Slack, Facebook Messenger) and voice-based systems (e.g., Google Assistant, Alexa). Dialogflow works well at handling structured data like dates and currencies.

However, it lacks deep customization features for fine-tuning models and does not offer as many advanced enterprise integration options compared to some competitors. It’s ideal for developers who need a straightforward, scalable platform with a g0od number of language support (over 20 languages), though its free tier is limited in terms of queries.

3. Lex

Amazon Lex is best suited for developers who want to build conversational interfaces within the AWS ecosystem. It integrates with AWS services like Lambda. Lex supports both text and speech, leveraging the same deep learning technology that powers Alexa.

However, Lex lacks out-of-the-box integrations with third-party platforms and messaging apps compared to others like Dialogflow. While it provides powerful natural language understanding and speech recognition, it may require additional setup for non-AWS developers. It’s ideal for companies focused on building sophisticated conversational interfaces for customer service, support, and virtual assistants.

4. Watson Assistant

Watson Assistant is best known for its use in enterprise environments, providing advanced NLP capabilities for applications ranging from customer support chatbots to virtual assistants. One of Watson Assistant’s main strengths is its easy integration with a variety of platforms, including websites, mobile apps, and social media. However, it lacks some of the intuitive development tools.

It also doesn’t provide as much flexibility in terms of model customization and training as some other services. Watson Assistant excels in industries that need advanced security, multi-language support, and high scalability, making it a strong choice for large organizations.

5. Wit.ai

Wit.ai provides a free NLP platform, which allows commercial use without any request limits (with some restrictions on rate). While it’s a solid option for developers building chatbots and voice assistants, it lacks the enterprise support and integration tools that larger platforms provide. It also doesn’t offer out-of-the-box integrations with popular third-party messaging platforms or enterprise systems. Wit.ai is suitable for developers building simple NLP-based applications and for those who are looking for a no-cost solution. It is particularly strong in speech-to-text and voice-based applications but doesn’t scale as well for complex, high-volume enterprise use cases.

See our article about IBM Watson for more details.

What is the difference between NLP and NLU?

• Natural Language Processing (NLP): In the artificial intelligence (AI) context, NLP is the overarching umbrella that encompasses several disciplines that tackle the interaction between computer systems and human natural languages. From that perspective, NLP includes several sub-disciplines such as discourse analysis, relationship extraction, natural language understanding and a few others language analysis areas. NLP includes:
• Tokenization (breaking down text into words or phrases)
• Part-of-speech tagging
• Named entity recognition (NER)
• Sentiment analysis
• Text generation and translation

Natural Language Understanding (NLU): NLU is a subset of NLP that focuses on reading comprehension and semantic analysis. The combination of NLP and NLU technologies is becoming increasingly relevant on different software areas today including bot technologies. While there are many vendors and platforms focused on NLP-NLU technologies, the following technologies are becoming extremely popular within the bot developer community. NLU includes:

• Intent recognition: Understanding what the user wants to do.
• Entity extraction: Identifying specific pieces of information (e.g., dates, locations, product names).
• Contextual interpretation: Figuring out meaning from ambiguous or unstructured language.

NLP models can handle tasks like:

• text preprocessing
• classification
• summarization

NLU becomes critical when AI needs to interpret and respond intelligently to user queries, especially when the input is ambiguous or lacks structure. Some model include both. S

State-of-the-art models, like OpenAI’s GPT series, Claude, and Anthropic, combine both NLP and NLU elements. These models don’t just generate language (NLP) but also understand nuances, infer meaning, and engage in reasoning (NLU).

To learn more about the differences between NLU and NLP you can read our NLU vs NLP: Main Differences & Use Cases Comparison article.

If you still feel like you need NLP platform selection guide, feel free to check our related article.

And if you have a specific business challenge: