Cloud Natural Language Processing Market Size & Share 2017 - 2024
Market Size by Product (Rule Based, Statistical, Hybrid), by Deployment Model (Public, Private, Hybrid), by Technology (Recognition, Analytics, Operational), by Application (Information Extraction, Machine Translation, Processing & Visualization, Question Answering), by End Use (BFSI, IT & Telecommunication, Defense, Government Organization, Retail & E-Commerce, Healthcare, Energy & Utility), Industry Analysis Report, Regional Outlook, Growth Potential, & Forecast.
Report ID: GMI2202
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Published Date: November 2017
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Report Format: PDF
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Authors:
Preeti Wadhwani, Aishvarya Ambekar
Cloud Natural Language Processing Market Size
Cloud Natural Language Processing (NLP) Market size was estimated to be over USD 1.5 billion in 2016 and is estimated to grow with around 17% CAGR from 2017 to 2024.
Cloud natural language processing market is anticipated to experience substantial growth owing to the increasing investment in AI technology. AI has emerged as one of the most advanced technologies in the wide range of applications ranging from robotics to machine learning to advanced analytics. The technology assists organizations in extracting powerful insights to drive faster business decisions in e-commerce, marketing, competitive intelligence, product management and several other areas of business to close the gap between insights and action. As AI matures, vendors will shift more towards the technology along with the conventional analytics platform, which is estimated to fuel investments.
The investment landscape is led by the digital native companies and tech giants such as Google, Baidu, Amazon and Apple. They are collectively investing billions of dollars in a wide range of AI applications ranging from robotics, machine learning, virtual assistance technology, autonomous vehicles, natural language and computer vision. Internal investments by the technology companies in R&D for enhancing AI capabilities accounts for a major share in the investment in AI. For instance, Google and Baidu have invested approximately USD 20 billion in AI in 2016.
Cloud Natural Language Processing Market Analysis
The statistical cloud NLP market dominated the global business landscape in 2016 owing to its advanced features and benefits over the conventional methods. Statistical method leverages advanced machine learning algorithms to develop statistical models from bilingual parallel corpora that assist in the precise and rapid translation. Whereas, rule based rule based methods requires human effort to prepare rule and linguistic resources such as syntactic parsers, part of speech taggers, and transfer rules for translation. Furthermore, statistical method is data driven and can handle ambiguity effectively, which makes it an ideal choice for natural language processing solutions.
Public cloud NLP market is analyzed to be the leading deployment model owing to the low cost and scalability offered by the public cloud deployment. On the other hand, hybrid cloud is estimated to witness high growth at over 19% CAGR during the forecast timeline as it offers benefits of both public and private cloud models.
The need for an effective predictive technology and low adoption of the teasdchnology are the major constraints in the cloud NLP market growth. The growing adoption of the technology among media and entertainment, advertisement, and healthcare organizations are estimated to develop myriad growth opportunities for the market.
Recognition technology is estimated to account for major share in the global cloud NLP market owing to wide spread adoption of the image recognition, interactive voice recognition and optical character recognition technology among large and small enterprises for machine translation and information extraction. Furthermore, growing demand of recognition technology among organizations to capture and analyze customer voice for enhancing customer experience and automation is also estimated to back the growth of the recognition technology.
Machine translation is the dominating application as it is the most essential component of the NLP solution that converts text and speech inputs from one language to another. Furthermore, increasing requirement of localizing content into more languages is also estimated to fuel the demand. In addition, need for high speed translation and cost effectiveness is also contributing significantly towards the growth of the cloud NLP market.
BFSI sector is the leading end-user of the cloud NLP market solutions. Financial institutes are leveraging the technology for text mining, cross boarder payments, solving insurance queries, foreign exchange, and many other applications. Furthermore, these solutions are also widely used by financial institutes in contact centers for processing customer voice and documentation. For instance, Citibank utilizes NLP in biometric security applications and for text mining. SAS, Fuji Xerox and Nuance Communications are the major vendors that are catering to this industry.
U.S. is estimated to be the leading regional segment in the global cloud NLP market owing to the presence of the large number of players. Increasing investment in the AI technology is also estimated to be one of the major factor backing the growth of the market. Furthermore, rapid adoption of the smart device is also estimated to contribute significantly towards the growth.
Cloud Natural Language Processing Market Share
Major vendors in the cloud NLP market are
Product launch and strategic acquisition are analyzed to be the most common strategies used by the players to gain share and cater to the need of the market. For instance, in March 2017, Google acquired Kaggle, a data scientist community, to strengthen its position in data science and machine learning. Similarly, in July 2017, Facebook launched new messenger platform 2.1 in the market with several additional features such as built-in natural language processing, payment SDK, and global beta.
Cloud NLP Industry Background
Growing demand of the Big Data and IoT technology is analyzed to be the major factors augmenting the growth of the industry. NLP is widely used with IoT and Big data technology to analyze data and drive useful insights. The growing adoption of these technologies will result in the new type of analytics to drive new business insights, which is estimated to drive the cloud NLP market among various industry sectors.
Research methodology, data sources & validation process
This report draws on a structured research process built around direct industry conversations, proprietary modelling, and rigorous cross-validation and not just desk research.
Our 6-step research process
1. Research design & analyst oversight
At GMI, our research methodology is built on a foundation of human expertise, rigorous validation, and complete transparency. Every insight, trend analysis, and forecast in our reports is developed by experienced analysts who understand the nuances of your market.
Our approach integrates extensive primary research through direct engagement with industry participants and experts, complemented by comprehensive secondary research from verified global sources. We apply quantified impact analysis to deliver dependable forecasts, while maintaining complete traceability from original data sources to final insights.
2. Primary research
Primary research forms the backbone of our methodology, contributing nearly 80% to overall insights. It involves direct engagement with industry participants to ensure accuracy and depth in analysis. Our structured interview program covers regional and global markets, with inputs from C-suite executives, directors, and subject matter experts. These interactions provide strategic, operational, and technical perspectives, enabling well-rounded insights and reliable market forecasts.
3. Data mining & market analysis
Data mining is a key part of our research process, contributing nearly 20% to the overall methodology. It involves analysing market structure, identifying industry trends, and assessing macroeconomic factors through revenue share analysis of major players. Relevant data is collected from both paid and unpaid sources to build a reliable database. This information is then integrated to support primary research and market sizing, with validation from key stakeholders such as distributors, manufacturers, and associations.
4. Market sizing
Our market sizing is built on a bottom-up approach, starting with company revenue data gathered directly through primary interviews, alongside production volume figures from manufacturers and installation or deployment statistics. These inputs are then pieced together across regional markets to arrive at a global estimate that stays grounded in actual industry activity.
5. Forecast model & key assumptions
Every forecast includes explicit documentation of:
✓ Key growth drivers and their assumed impact
✓ Restraining factors and mitigation scenarios
✓ Regulatory assumptions and policy change risk
✓ Technology adoption curve parameter
✓ Macroeconomic assumptions (GDP growth, inflation, currency)
✓ Competitive dynamics and market entry/exit expectations
6. Validation & quality assurance
The final stages involve human validation, where domain experts manually review filtered data to identify nuances and contextual errors that automated systems might miss. This expert review adds a critical layer of quality assurance, ensuring data aligns with research objectives and domain-specific standards.
Our triple-layer validation process ensures maximum data reliability:
✓ Statistical Validation
✓ Expert Validation
✓ Market Reality Check
Trust & credibility
Verified data sources
Trade publications
Security & defense sector journals and trade press
Industry databases
Proprietary and third-party market databases
Regulatory filings
Government procurement records and policy documents
Academic research
University studies and specialist institution reports
Company reports
Annual reports, investor presentations, and filings
Expert interviews
C-suite, procurement leads, and technical specialists
GMI archive
13,000+ published studies across 30+ industry verticals
Trade data
Import/export volumes, HS codes, and customs records
Parameters studied & evaluated
Every data point in this report is validated through primary interviews, true bottom-up modelling, and rigorous cross-checks. Read about our research process →