What Is Deep Learning?
Top 10 Natural Language Processing APIs To Use In 2025
Overview
GPT-5, Gemini Ultra, and Grok-3 lead as the most potent and versatile NLP APIs in 2025
Free tools like spaCy and OpenNLP remain popular for their speed, flexibility, and open-source nature
Most companies choose NLP APIs based on system compatibility, scalability, and task complexity
Natural Language Processing, or NLP, is the part of technology that helps machines understand human language. In 2025, it will be used in various places, including schools, offices, customer care, and even mobile apps.
Many companies now provide ready-to-use APIs that help developers add smart language features to websites and applications. Some tools are fast, some are more accurate, and others are simple to use. Below are the top 10 NLP APIs people are using the most this year.
OpenAI GPT- 4 API
GPT-4 is one of the most advanced language models right now. It can write answers, stories, emails, and even help with coding. It understands language well and can be used in many different areas, like education, business, and content creation.
Google Gemini Ultra API
Gemini Ultra is Google's newest language tool. It excels at solving complex tasks, such as lengthy questions and logic problems. It works smoothly with other Google services like Gmail and Docs. Many users say it is faster and more helpful than earlier versions.
xAI Grok-3 API
xAI created Grok-3. It is known for handling complex tasks, especially in maths and reasoning. It is also made to work with social media platforms. It provides smart and quick answers, making it ideal for apps that require fast replies.
Cohere API
AI Language Tools are enhancing everything from chatbots to content moderation systems. Cohere is made for businesses that want speed and control. It helps sort data, write responses, and search through text. It works well with systems like Oracle and Salesforce, which many large companies use.
Spark NLP
Best NLP APIs For Developers offer pre-trained models that simplify complex language processing tasks. Spark NLP is used by many hospitals, banks, and government offices. It can handle large amounts of text and supports many languages. It is built to work with big data tools like Apache Spark, which makes it suitable for heavy tasks.
Also Read: Courses for Learning Natural Language Processing
spaCy
spaCy is a free NLP tool. It is speedy and straightforward. It helps find names, verbs, and other parts of a sentence. Both students and developers use it. It also works with different deep learning tools, which makes it more flexible. Sentiment Analysis APIs help businesses monitor customer feedback in real time with high accuracy.
Apache OpenNLP
OpenNLP is another free option. It is written in Java and provides tools such as sentence detection, part-of-speech tagging, and more. It has been around for years and still gets regular updates. It is helpful for teams that want a stable and open-source system.
Amazon Comprehend
Amazon Comprehend helps find emotions, topics, and languages in texts. It is suitable for reading customer reviews and support chats. It fits well with other Amazon services and is used by many companies that already work with AWS.
Azure Text Analytics
Microsoft's Azure Text Analytics can look at documents, emails, and reviews. It finds main ideas, keywords, and even emotions. It supports many languages and is suitable for companies that use Microsoft tools.
IBM Watson NLU
Watson's NLP service looks at feelings, sentence roles, and the way ideas are connected. It is used in news apps, customer service, and business reports. It provides deep insights and aids in tasks that require more careful language study.
Also Read: Top Natural Language Processing Tools and Libraries for Data Scientists
Choosing the Right One
Each API has its own strengths. Some work well for creating content, while others are more suitable for reading and sorting large amounts of data. Large companies usually pick tools that match the systems they already use, such as those from Amazon or Microsoft. Smaller teams often choose options like spaCy or OpenNLP because they are free and easy to use.
NLP Integration Solutions ensure seamless deployment of language features within enterprise software. The field of Natural Language Processing is growing fast. These APIs are helping apps and websites become more advanced in 2025. They allow machines to read, write, and understand human language more accurately than before.
NLP Brings Interactive Analytics Forward - But What Are The ...
NLP Augmented Analytics is the ability to have a conversation with the system, have it understand your questions, and even anticipate and enrich your queries as it learns about your interests, e.G., "Let me see more of that," or "Compare all three."
For more than twenty-five years, the standard for interactive analytics was Business Intelligence (BI) visualizations such as dashboards, but NLP is the next step in ease-of-use. NLP is a much richer capability with applications far beyond just enhancing analytics. Application of capabilities like Speech Recognition, Machine Translation, Natural Language Generation, Sentiment Analysis, and Automatic Report Generation are applied to extract intelligent information from every kind of data. It is capable of, on an unsupervised basis, sharing exhaustive insights into market sentiments.
NLP has a limiting factor, however. It is not a database or a query processing engine, or a powerful calculation platform. It can understand your question and often provide stunning insight. It can generate a narrative of analysis in words, visualization, or generated speech, but it needs the intelligence of a powerful analytical engine to process the question. NLP can learn. It can extrapolate and provide the ability for you to be more creative, more expansive, and more dynamic in your explorations of data, but it can't count to ten. To complete functionality, an underlying mesh of analytics engines, databases, curated data storage and adequate metadata store to do the heavy lifting.
Requirements of augmented analyticsThe whole gestalt of data analytics is "ask a question, get back an answer, and ask a follow-up question." In a data visualization tool, you click and drag. In NLP augmented analytics, you speak. Ultimately, both generate a query that the underlying analytical engine understands.
This experience of conversing with data depends on underlying query response speed. Suppose you ask a question in natural language, or even via visualization drill-down, and have to go to lunch for an hour before you get the answer. In that case, you have far less opportunity to formulate the response and less inclination to pursue curiosity, knowing you could waste hours. The effectiveness of the conversational paradigm depends on a robust back end. The effect of this is even more pronounced in augmented analytics when speaking and listening to your data.
In a conversational mode, natural language processing accepts questions in your spoken language and directs the system to process your questions and even enrich your queries. This is the long-awaited next step of analytics ease-of-use.
augmented analytics goes beyond that to include reaching out to data beyond the database, stored in other forms, and performing a much broader range of analytics, including statistics, data science, AI (machine learning and neural networks), and presenting the answers not just in interactive visualizations, but also in spoken language, and all manner of publication.
To power augmented analytics, the underlying analytical processing engine must:
How much non-productive time do we spend with the mouse, a necessary component of a GUI? If you're like most people, you spend many hours a day pushing your mouse around, positioning the point to highlight something to copy, and getting too much or not enough and having to start over, or typing with both hands and having to stop to pick up the mouse to move it.
For NLP-enhanced business analytics, the conversation may be, "Run the latest pricing analysis push the results to my phone." The critical thing to remember is that the computer does not understand what you are saying. It can process it and answer but make no mistake -- it's all done with math.
Organizations that offer NLP capabilities don't have to start from scratch. There are open-source Python libraries that software can integrate with, such as spaCy, textacy, or neuralcrret; SparkNLP (From John Snow Labs) and a few in other languages such as CoreNLP in Java.
I recently did a proof of concept using Pavlov, an open-source library. To complete the picture, I added a data source. Now, we have a complete picture of how NLP Augmented Analytics works in practice.
What happened here is that the system correctly interpreted "that capacity constraint" and "that component" from understanding the context of the conversation. That may seem simple, but it requires the application of some very serious AI, in particular, a Recurrent Neural Network (RNN). RNN is a neural sequence model, an architecture specifically designed to address previous inputs. RNNs sometimes fail to converge on a solution, so an alternative model, Long Short-Term Memory (LSTM), is added.
Unlike feedforward neural networks, RNNs can use their internal state (memory) to process input sequences, like the iterative query above. This makes them applicable to tasks such as unsegmented, connected handwriting recognition, or speech recognition.
Everyday examples that are a little more complicated:
"Show me the sales of accessories."
"Remind me at three o'clock to call Venessa."
"Give me a four-frame visualization of Sales of Accessories: multi-line, Pie, by month stacked column, and sales range."
"Did my propensity model finish?" Not yet.
"Take out Tires and Tubes from that other one."
"Send to Joe with heading Sales of Accessories."
How did the NLP know that I was asking about sales and accessories in a particular version? Because it learns your pattern, and it learns patterns in context from potentially millions of queries. Perhaps this dialogue happened at the end of the month, and the NLP assumed the most current analysis was needed. Notice the comment, "Remind me at three o'clock to call Venessa." It is an out-of-context request, but when it's followed by "Give me a four-frame visualization of sales of accessories: multi-line, Pie, by month stacked column, and sales range," it makes a clean context switch. This may be the essential aspect of Augmented Analytics.
Augmented Analytics does require some training on your part, too. You need to learn how to phrase your conversation so the meaning is not ambiguous. This will get easier over time as the software learns your phrasing things, but you need to help initially. Consider this:
"Hey Siri, I'm bleeding bad. Can you call me an ambulance?"
"Neil, from now on, I'll call you 'Neil an Ambulance,' OK?
This example is from 2016. Apple, getting hundreds of complaints about these misunderstandings, continuously improves the application with advances in NLP. If you spoke this request today, just the tone of your voice would clue Siri that you needed an ambulance.
To do that, Siri not only needs to understand that you need an ambulance, but it also needs to have the ability to dial your phone and communicate with the hospital.
In data analytics, NLP must communicate with data to resolve your question. Once the NLP processing has figured out what the question is, it must provide an answer. What does it need to do?
NLP Augmented Analytics is poised for some giant leaps forward. NLP technology has grown from a handful of trained models (used for Transfer Learning) to dozens. I learned at the NLP Summit in October 2020 that each of these NLP subject areas has improved in accuracy in just four years from 50% to 90%:
It can read complex text in many languages and sense your emotions from your voice's tenor or even your written questions. In addition to parsing your questions, comprehending what you ask, it can do all the hard work you would have done in the past - attaching the needed data sources, framing the queries in the dialect of those sources, and publishing the result in a format you prefer. It can even ask you follow-up questions.
NLP will become more and more common in corporate analytics applications.
If you're like me, you've been waiting for Conversational AI since the Jetsons. Augmented Analytics goes a step beyond Conversational and Search. Communicating your request to the resources that can perform complex queries and models is the end of annoying user interfaces.
Text Analytics Based Cloud NLP Industry Insights, 2018 To 2027 ...
DUBLIN, May 5, 2020 /PRNewswire/ -- The "Global Text Analytics Based Cloud NLP Market Outlook 2027" report has been added to ResearchAndMarkets.Com's offering.
The global text analytics based cloud NLP market is anticipated to record a CAGR of 24.13% over the forecast period, i.E. 2019-2027.
Factors such as rising need for the analysis and prediction of consumer buying behavior especially in the retail sector that helps businesses to personalize discounts and other schemes to its customers, coupled with the multiplying utilization of pre-trained NLP models by application developers to develop applications that can perform tasks such as sentiment analysis and text classification are anticipated to promote significantly towards the growth of the global text analytics based cloud NLP market.
The global text analytics based cloud NLP market consists of various segments that are segmented by component, type, deployment model, technology, organization size, end user and by region. The Public segment, forming a sub-segment of the deployment model, which had a market value of USD 270.64 million in the year 2018, is anticipated to hold the largest market share in 2027 by registering a CAGR of 24.09% during the forecast period. Additionally, the Analytics segment, which is a sub-segment of the technology segment is anticipated to register the highest CAGR throughout the forecast period.
Based on region, the global text analytics based cloud NLP market is segmented into North America, Europe, Asia Pacific, Latin America and Middle East & Africa. The text analytics based cloud NLP market in Middle East & Africa is anticipated to gain a modest market share of 6.5% by attaining a CAGR of 23.86% throughout the forecast period. The growth can be attributed to the growing usage of AI based NLP applications in various verticals along with the growing investments for NLP applications, driven by the wide range of applications, such as automated customer agents, IT automation and prevention systems among others.
Some of the key industry leaders in the global text analytics based cloud NLP market are Inbenta Technologies Inc., IBM, Microsoft Corporation, Amazon Web Services, Inc., Lexalytics, Inc., Aylien Ltd., Softweb Solutions Inc., Google LLC and Linguamatics.
Key Topics Covered
Global Text Analytics based Cloud NLP Market, 2018-2027
1. Introduction1.1. Market Definition1.2. Market Segmentation
2. Research Methodology2.1. Variables (Dependent and Independent)2.2. Multi Factor Based Sensitivity Model
3. Executive Summary
4. Assessments Of Use Cases In Text Analytics Based Cloud Natural Language Processing
5. Study On Role Of NLP In Text Analytics
6. Study On Text Analytics Process
7. Assessment of Applications of Natural Language Processing
8. Market Dynamics8.1. Drivers8.2. Challenges8.3. Opportunities8.4. Trends
9. Global Text Analytics based Cloud NLP Market Outlook9.1. Market Size and Forecast, 2018-20279.1.1. By Value (USD Million)
By Component
By Type
By Deployment Model
By Technology
By Organization Size
By End-User
By Region
Companies Mentioned
For more information about this report visit https://www.Researchandmarkets.Com/r/2w7kws
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SOURCE Research and Markets
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