The global no-code AI platform market is projected to reach a valuation of US$ 4,094.7 million in 2023. The no-code AI platform market is expected to reach US$ 49,481.0 million by 2033 and exhibit growth at a CAGR of 28.3% from 2023 to 2033.
No-code AI platforms are referred to as AI development platforms which provide non-programmers and non-AI experts with the necessary tools that they need to implement AI projects. They can also be implemented by AI practitioners and experts for their projects.
No-code AI platforms may not possess the same ability as AI platforms which require programming and other expertise. But they still serve the important purpose of making use of AI for developing software and projects for a wider group of people and beginners. As per FMI, the no-code AI platform market holds about 16% of the global software development market.
An increasing number of AI companies across the globe is anticipated to bode well for the global market. As the number is increasing, the gap between domain experts and AI experts is also widening. Moreover, in-depth knowledge of AI experts helps domain experts to solve their technology-related issues. No-code AI tools are expected to create new opportunities for domain experts to communicate better and test their ideas with AI experts.
| Attributes | Key Statistics |
|---|---|
| No-code AI Platform Market Estimated Size (2023) | US$ 4,094.7 million |
| Projected Market Valuation (2033) | US$ 49,481.0 million |
| Value-based CAGR (2023 to 2033) | 28.3% |
| Collective Value Share: Top 5 Vendors | Around 35% |
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The no-code AI platform market is projected to expand at 28.3% CAGR between 2023 and 2033. As per FMI, the market expanded at a CAGR of 13% in the historical period from 2018 to 2022.
Growth is attributed to the rapid evolution and implementation of AI and machine learning across the globe. Technologies like automated ML are gaining immense popularity as these solutions are meant for businesses that lack ML expertise. Further, there has also been an increase in the adoption of IoT, edge computing, and data science solutions & services across various industries, which is expected to aid growth.
Urgent Need to Automate Tasks in Organizations to Drive Sales of No-code AI Platforms
Artificial intelligence and machine learning have been implemented in numerous industries and departments such as the human resource department of various companies over the past few years. Further, these technologies are mainly used for automating numerous tasks and developing programs & models to obtain real-time insights about several aspects of a company.
Not every employee in a company that uses AI platforms is aware of the development methods or has the correct technical expertise. To allow such individuals to interact with and develop AI platforms for their work, no-code AI platforms have been an essential tool. It is projected to drive the global market in the forecast period.
Key Players in the United States are Developing AI without Coding
| Country | The United States |
|---|---|
| Market Share % (2022) | 19.3% |
Several companies in the United States provide novel AI solutions and services for building mobile & desktop apps for businesses, automating workflows, and automated communication. Furthermore, many large-scale technology companies such as Neuralink, IBM, Microsoft, and Google are implementing AI for multiple applications in the United States.
Companies in India are inclined toward Low Code Machine Learning
| Country | India |
|---|---|
| Market CAGR % (2023 to 2033) | 32.3% |
Several enterprises in India have implemented AI solutions and services for their business purposes over the past few years. The majority of these companies had to shift to software and mobile application platforms irrespective of their domain because of the growing penetration of the internet in India.
Software development automation and analytics have witnessed high growth across the country over the last few years. Further, AI implementation has also witnessed growth in the education and government sectors. Owing to the aforementioned factors, India’s no-code AI platform market is expected to showcase a high CAGR of 32.3% in the forecast period.
Government in Japan to Deploy No-code Machine Learning in Educational Institutions
| Country | Japan |
|---|---|
| Market Share % (2022) | 4.3% |
Due to the growing aging population in Japan, the workforce in Japan is experiencing a decline. However, several companies in the country have started offering AI solutions and services to enhance workflow. AI is also being developed by financial and chemical manufacturers in Japan, as well as government institutions. The government is focusing on equipping educational institutions with AI platforms. Thus, Japan’s no-code AI platform market is likely to expand at a CAGR of 33.8% in the evaluation period.
Principal Consultant
Demand for No-code AI Tools to Surge among Organizations
| Segment | No-code AI Tools |
|---|---|
| Market Share % (2022) | 64.3% |
The no-code AI tools segment is projected to generate a global market share of more than 64.3% in the assessment period. These tools require programming from users and provide them with compilers, programming assistance, software testing tools, and operation assistance.
Natural Language Processing Technology is Preferred by No-Code AI Tools Users
| Segment | Natural Language Processing |
|---|---|
| Market Share % (2022) | 43.3% |
Natural language processing (NLP) is a technology that is widely implemented in chatbots, language translators, and voice assistants. Chatbots and translators are being widely implemented by companies for their websites and internal use. For instance,
Healthcare Industry is Looking for Advanced No-code AI Builders
AI is being implemented in the healthcare sector to improve healthcare services and operations. Implementation of AI includes algorithms to identify patients’ health conditions, several operations such as booking appointments & filling prescriptions, and patient identification to increase the speed of tasks.
AI implementation can help healthcare workers automate or accelerate tasks that are time-consuming, simple, and require high effort. Also, AI can assist them to improve services for patients. For instance,
No-code AI platform developers are striving to provide several solution development tools to non-programmers. A few others are launching their platforms equipped with various features for multiple operations or specific purposes. For instance,
Google AutoML, Amazon Sagemaker, Microsoft Lobe: 3 No-code AI Platforms at the Forefront
Considering the need for non-AI experts to test their ideas and processes, many companies are now offering easy-to-access platforms. Google, Amazon, and Lobe are among the leading companies in the no-code AI platform space.
Google’s AutoML enables developers who have limited machine learning expertise to build high-quality models that pertain to their businesses. Additionally, Google announced the launch of this product in 2018 and since then, it has become one of the highly preferred platforms for non-AI experts.
The focus of Google is to develop a unique platform that requires minimal technical expertise. Therefore, it is building its product in such a way that it requires less coding. For instance,
The platform is compatible with 22 compliance programs such as PCI, HIPPA, and FedRAMP. It is currently being used by leading companies such as Aurora, AstraZeneca, Celgene, Lenovo, Hyundai, and Roche.
Amazon is also partnering with many artificial intelligence providers to drive innovation. For instance, Observe.AI is using the Amazon Sagemaker to build an intelligence workforce platform. Microsoft Lobe is another key platform in the no-code AI space. The platform offers pre-built project templates such as image classification, object detection, and data classification. Lobe.ai was an individual entity and was bought by Microsoft in 2018.
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| Attribute | Details |
|---|---|
| Estimated Market Size (2023) | US$ 4,094.7 million |
| Projected Market Valuation (2033) | US$ 49,481.0 million |
| Value-based CAGR (2023 to 2033) | 28.3% |
| Forecast Period | 2023 to 2033 |
| Historical Data Available for | 2018 to 2022 |
| Market Analysis | Value (US$ million) |
| Key Regions Covered | North America, Latin America, Europe, South Asia & Pacific, East Asia, and the Middle East & Africa |
| Key Countries Covered | The United States, Canada, Germany, The United Kingdom, France, Italy, Spain, Russia, China, Japan, South Korea, India, Australia & New Zealand, GCC Countries, and South Africa |
| Key Segments Covered | Solution, Technology, Enterprise Size, Industry, and Region |
| Key Companies Profiled | Clarifai Inc; Caspio Inc; Google; Amazon; Microsoft; Akkio Inc; Apteo; Runway; QuickBase Inc; AgilePoint Inc; MonkeyLearn; Levity; Intersect Labs; Apple; DataRobot Inc |
| Report Coverage | Market Forecast, Company Share Analysis, Competition Intelligence, Drivers, Restraints, Opportunities and Threats Analysis, Market Dynamics and Challenges, and Strategic Growth Initiatives |
The United States may witness significant growth in the No-code AI Platform Market.
The increasing demand for AI solutions and the growing popularity of no-code development are expected to drive sales of No-code AI Platforms.
The growing adoption of cloud computing and the increasing availability of open-source AI tools are driving the No-code AI Platform Market.
The market recorded a CAGR of 13% in 2022.
Substantial investment in research and development and the development of new no-code AI platforms may provide growth prospects for the market players.
1. Executive Summary 1.1. Global Market Outlook 1.2. Demand-side Trends 1.3. Supply-side Trends 1.4. Technology Roadmap Analysis 1.5. Analysis and Recommendations 2. Market Overview 2.1. Market Coverage / Taxonomy 2.2. Market Definition / Scope / Limitations 3. Market Background 3.1. Market Dynamics 3.1.1. Drivers 3.1.2. Restraints 3.1.3. Opportunity 3.1.4. Trends 3.2. Scenario Forecast 3.2.1. Demand in Optimistic Scenario 3.2.2. Demand in Likely Scenario 3.2.3. Demand in Conservative Scenario 3.3. Opportunity Map Analysis 3.4. Investment Feasibility Matrix 3.5. PESTLE and Porter’s Analysis 3.6. Regulatory Landscape 3.6.1. By Key Regions 3.6.2. By Key Countries 3.7. Regional Parent Market Outlook 4. Global Market Analysis 2018 to 2022 and Forecast, 2023 to 2033 4.1. Historical Market Size Value (US$ Million) Analysis, 2018 to 2022 4.2. Current and Future Market Size Value (US$ Million) Projections, 2023 to 2033 4.2.1. Y-o-Y Growth Trend Analysis 4.2.2. Absolute $ Opportunity Analysis 5. Global Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Solution 5.1. Introduction / Key Findings 5.2. Historical Market Size Value (US$ Million) Analysis By Solution, 2018 to 2022 5.3. Current and Future Market Size Value (US$ Million) Analysis and Forecast By Solution, 2023 to 2033 5.3.1. No-code AI tools 5.3.1.1. Cloud-Based 5.3.1.2. On-Premises 5.3.2. Services 5.3.2.1. Consulting Services 5.3.2.2. Support and Maintenance Services 5.3.2.3. Training and Education 5.3.2.4. Software Development 5.3.2.5. Managed Services 5.4. Y-o-Y Growth Trend Analysis By Solution, 2018 to 2022 5.5. Absolute $ Opportunity Analysis By Solution, 2023 to 2033 6. Global Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Technology 6.1. Introduction / Key Findings 6.2. Historical Market Size Value (US$ Million) Analysis By Technology, 2018 to 2022 6.3. Current and Future Market Size Value (US$ Million) Analysis and Forecast By Technology, 2023 to 2033 6.3.1. Natural Language Processing (NLP) 6.3.2. Computer Vision 6.3.3. Predictive Analytics 6.4. Y-o-Y Growth Trend Analysis By Technology, 2018 to 2022 6.5. Absolute $ Opportunity Analysis By Technology, 2023 to 2033 7. Global Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Enterprise Size 7.1. Introduction / Key Findings 7.2. Historical Market Size Value (US$ Million) Analysis By Enterprise Size, 2018 to 2022 7.3. Current and Future Market Size Value (US$ Million) Analysis and Forecast By Enterprise Size, 2023 to 2033 7.3.1. Small and Mid-Sized Enterprises (SMEs) 7.3.2. Large Enterprises 7.4. Y-o-Y Growth Trend Analysis By Enterprise Size, 2018 to 2022 7.5. Absolute $ Opportunity Analysis By Enterprise Size, 2023 to 2033 8. Global Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Industry 8.1. Introduction / Key Findings 8.2. Historical Market Size Value (US$ Million) Analysis By Industry, 2018 to 2022 8.3. Current and Future Market Size Value (US$ Million) Analysis and Forecast By Industry, 2023 to 2033 8.3.1. BFSI 8.3.2. IT & Telecom 8.3.3. Retail 8.3.4. Healthcare 8.3.5. Manufacturing 8.3.6. Government 8.3.7. Education 8.3.8. Others 8.4. Y-o-Y Growth Trend Analysis By Industry, 2018 to 2022 8.5. Absolute $ Opportunity Analysis By Industry, 2023 to 2033 9. Global Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Region 9.1. Introduction 9.2. Historical Market Size Value (US$ Million) Analysis By Region, 2018 to 2022 9.3. Current Market Size Value (US$ Million) Analysis and Forecast By Region, 2023 to 2033 9.3.1. North America 9.3.2. Latin America 9.3.3. Western Europe 9.3.4. Eastern Europe 9.3.5. South Asia and Pacific 9.3.6. East Asia 9.3.7. Middle East and Africa 9.4. Market Attractiveness Analysis By Region 10. North America Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country 10.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022 10.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2023 to 2033 10.2.1. By Country 10.2.1.1. USA 10.2.1.2. Canada 10.2.2. By Solution 10.2.3. By Technology 10.2.4. By Enterprise Size 10.2.5. By Industry 10.3. Market Attractiveness Analysis 10.3.1. By Country 10.3.2. By Solution 10.3.3. By Technology 10.3.4. By Enterprise Size 10.3.5. By Industry 10.4. Key Takeaways 11. Latin America Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country 11.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022 11.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2023 to 2033 11.2.1. By Country 11.2.1.1. Brazil 11.2.1.2. Mexico 11.2.1.3. Rest of Latin America 11.2.2. By Solution 11.2.3. By Technology 11.2.4. By Enterprise Size 11.2.5. By Industry 11.3. Market Attractiveness Analysis 11.3.1. By Country 11.3.2. By Solution 11.3.3. By Technology 11.3.4. By Enterprise Size 11.3.5. By Industry 11.4. Key Takeaways 12. Western Europe Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country 12.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022 12.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2023 to 2033 12.2.1. By Country 12.2.1.1. Germany 12.2.1.2. UK 12.2.1.3. France 12.2.1.4. Spain 12.2.1.5. Italy 12.2.1.6. Rest of Western Europe 12.2.2. By Solution 12.2.3. By Technology 12.2.4. By Enterprise Size 12.2.5. By Industry 12.3. Market Attractiveness Analysis 12.3.1. By Country 12.3.2. By Solution 12.3.3. By Technology 12.3.4. By Enterprise Size 12.3.5. By Industry 12.4. Key Takeaways 13. Eastern Europe Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country 13.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022 13.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2023 to 2033 13.2.1. By Country 13.2.1.1. Poland 13.2.1.2. Russia 13.2.1.3. Czech Republic 13.2.1.4. Romania 13.2.1.5. Rest of Eastern Europe 13.2.2. By Solution 13.2.3. By Technology 13.2.4. By Enterprise Size 13.2.5. By Industry 13.3. Market Attractiveness Analysis 13.3.1. By Country 13.3.2. By Solution 13.3.3. By Technology 13.3.4. By Enterprise Size 13.3.5. By Industry 13.4. Key Takeaways 14. South Asia and Pacific Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country 14.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022 14.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2023 to 2033 14.2.1. By Country 14.2.1.1. India 14.2.1.2. Bangladesh 14.2.1.3. Australia 14.2.1.4. New Zealand 14.2.1.5. Rest of South Asia and Pacific 14.2.2. By Solution 14.2.3. By Technology 14.2.4. By Enterprise Size 14.2.5. By Industry 14.3. Market Attractiveness Analysis 14.3.1. By Country 14.3.2. By Solution 14.3.3. By Technology 14.3.4. By Enterprise Size 14.3.5. By Industry 14.4. Key Takeaways 15. East Asia Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country 15.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022 15.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2023 to 2033 15.2.1. By Country 15.2.1.1. China 15.2.1.2. Japan 15.2.1.3. South Korea 15.2.2. By Solution 15.2.3. By Technology 15.2.4. By Enterprise Size 15.2.5. By Industry 15.3. Market Attractiveness Analysis 15.3.1. By Country 15.3.2. By Solution 15.3.3. By Technology 15.3.4. By Enterprise Size 15.3.5. By Industry 15.4. Key Takeaways 16. Middle East and Africa Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country 16.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022 16.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2023 to 2033 16.2.1. By Country 16.2.1.1. GCC Countries 16.2.1.2. South Africa 16.2.1.3. Israel 16.2.1.4. Rest of MEA 16.2.2. By Solution 16.2.3. By Technology 16.2.4. By Enterprise Size 16.2.5. By Industry 16.3. Market Attractiveness Analysis 16.3.1. By Country 16.3.2. By Solution 16.3.3. By Technology 16.3.4. By Enterprise Size 16.3.5. By Industry 16.4. Key Takeaways 17. Key Countries Market Analysis 17.1. USA 17.1.1. Pricing Analysis 17.1.2. Market Share Analysis, 2022 17.1.2.1. By Solution 17.1.2.2. By Technology 17.1.2.3. By Enterprise Size 17.1.2.4. By Industry 17.2. Canada 17.2.1. Pricing Analysis 17.2.2. Market Share Analysis, 2022 17.2.2.1. By Solution 17.2.2.2. By Technology 17.2.2.3. By Enterprise Size 17.2.2.4. By Industry 17.3. Brazil 17.3.1. Pricing Analysis 17.3.2. Market Share Analysis, 2022 17.3.2.1. By Solution 17.3.2.2. By Technology 17.3.2.3. By Enterprise Size 17.3.2.4. By Industry 17.4. Mexico 17.4.1. Pricing Analysis 17.4.2. Market Share Analysis, 2022 17.4.2.1. By Solution 17.4.2.2. By Technology 17.4.2.3. By Enterprise Size 17.4.2.4. By Industry 17.5. Germany 17.5.1. Pricing Analysis 17.5.2. Market Share Analysis, 2022 17.5.2.1. By Solution 17.5.2.2. By Technology 17.5.2.3. By Enterprise Size 17.5.2.4. By Industry 17.6. UK 17.6.1. Pricing Analysis 17.6.2. Market Share Analysis, 2022 17.6.2.1. By Solution 17.6.2.2. By Technology 17.6.2.3. By Enterprise Size 17.6.2.4. By Industry 17.7. France 17.7.1. Pricing Analysis 17.7.2. Market Share Analysis, 2022 17.7.2.1. By Solution 17.7.2.2. By Technology 17.7.2.3. By Enterprise Size 17.7.2.4. By Industry 17.8. Spain 17.8.1. Pricing Analysis 17.8.2. Market Share Analysis, 2022 17.8.2.1. By Solution 17.8.2.2. By Technology 17.8.2.3. By Enterprise Size 17.8.2.4. By Industry 17.9. Italy 17.9.1. Pricing Analysis 17.9.2. Market Share Analysis, 2022 17.9.2.1. By Solution 17.9.2.2. By Technology 17.9.2.3. By Enterprise Size 17.9.2.4. By Industry 17.10. Poland 17.10.1. Pricing Analysis 17.10.2. Market Share Analysis, 2022 17.10.2.1. By Solution 17.10.2.2. By Technology 17.10.2.3. By Enterprise Size 17.10.2.4. By Industry 17.11. Russia 17.11.1. Pricing Analysis 17.11.2. Market Share Analysis, 2022 17.11.2.1. By Solution 17.11.2.2. By Technology 17.11.2.3. By Enterprise Size 17.11.2.4. By Industry 17.12. Czech Republic 17.12.1. Pricing Analysis 17.12.2. Market Share Analysis, 2022 17.12.2.1. By Solution 17.12.2.2. By Technology 17.12.2.3. By Enterprise Size 17.12.2.4. By Industry 17.13. Romania 17.13.1. Pricing Analysis 17.13.2. Market Share Analysis, 2022 17.13.2.1. By Solution 17.13.2.2. By Technology 17.13.2.3. By Enterprise Size 17.13.2.4. By Industry 17.14. India 17.14.1. Pricing Analysis 17.14.2. Market Share Analysis, 2022 17.14.2.1. By Solution 17.14.2.2. By Technology 17.14.2.3. By Enterprise Size 17.14.2.4. By Industry 17.15. Bangladesh 17.15.1. Pricing Analysis 17.15.2. Market Share Analysis, 2022 17.15.2.1. By Solution 17.15.2.2. By Technology 17.15.2.3. By Enterprise Size 17.15.2.4. By Industry 17.16. Australia 17.16.1. Pricing Analysis 17.16.2. Market Share Analysis, 2022 17.16.2.1. By Solution 17.16.2.2. By Technology 17.16.2.3. By Enterprise Size 17.16.2.4. By Industry 17.17. New Zealand 17.17.1. Pricing Analysis 17.17.2. Market Share Analysis, 2022 17.17.2.1. By Solution 17.17.2.2. By Technology 17.17.2.3. By Enterprise Size 17.17.2.4. By Industry 17.18. China 17.18.1. Pricing Analysis 17.18.2. Market Share Analysis, 2022 17.18.2.1. By Solution 17.18.2.2. By Technology 17.18.2.3. By Enterprise Size 17.18.2.4. By Industry 17.19. Japan 17.19.1. Pricing Analysis 17.19.2. Market Share Analysis, 2022 17.19.2.1. By Solution 17.19.2.2. By Technology 17.19.2.3. By Enterprise Size 17.19.2.4. By Industry 17.20. South Korea 17.20.1. Pricing Analysis 17.20.2. Market Share Analysis, 2022 17.20.2.1. By Solution 17.20.2.2. By Technology 17.20.2.3. By Enterprise Size 17.20.2.4. By Industry 17.21. GCC Countries 17.21.1. Pricing Analysis 17.21.2. Market Share Analysis, 2022 17.21.2.1. By Solution 17.21.2.2. By Technology 17.21.2.3. By Enterprise Size 17.21.2.4. By Industry 17.22. South Africa 17.22.1. Pricing Analysis 17.22.2. Market Share Analysis, 2022 17.22.2.1. By Solution 17.22.2.2. By Technology 17.22.2.3. By Enterprise Size 17.22.2.4. By Industry 17.23. Israel 17.23.1. Pricing Analysis 17.23.2. Market Share Analysis, 2022 17.23.2.1. By Solution 17.23.2.2. By Technology 17.23.2.3. By Enterprise Size 17.23.2.4. By Industry 18. Market Structure Analysis 18.1. Competition Dashboard 18.2. Competition Benchmarking 18.3. Market Share Analysis of Top Players 18.3.1. By Regional 18.3.2. By Solution 18.3.3. By Technology 18.3.4. By Enterprise Size 18.3.5. By Industry 19. Competition Analysis 19.1. Competition Deep Dive 19.1.1. Clarifai Inc 19.1.1.1. Overview 19.1.1.2. Product Portfolio 19.1.1.3. Profitability by Market Segments 19.1.1.4. Sales Footprint 19.1.1.5. Strategy Overview 19.1.1.5.1. Marketing Strategy 19.1.2. Caspio Inc 19.1.2.1. Overview 19.1.2.2. Product Portfolio 19.1.2.3. Profitability by Market Segments 19.1.2.4. Sales Footprint 19.1.2.5. Strategy Overview 19.1.2.5.1. Marketing Strategy 19.1.3. Google 19.1.3.1. Overview 19.1.3.2. Product Portfolio 19.1.3.3. Profitability by Market Segments 19.1.3.4. Sales Footprint 19.1.3.5. Strategy Overview 19.1.3.5.1. Marketing Strategy 19.1.4. Amazon 19.1.4.1. Overview 19.1.4.2. Product Portfolio 19.1.4.3. Profitability by Market Segments 19.1.4.4. Sales Footprint 19.1.4.5. Strategy Overview 19.1.4.5.1. Marketing Strategy 19.1.5. Microsoft 19.1.5.1. Overview 19.1.5.2. Product Portfolio 19.1.5.3. Profitability by Market Segments 19.1.5.4. Sales Footprint 19.1.5.5. Strategy Overview 19.1.5.5.1. Marketing Strategy 19.1.6. Akkio Inc 19.1.6.1. Overview 19.1.6.2. Product Portfolio 19.1.6.3. Profitability by Market Segments 19.1.6.4. Sales Footprint 19.1.6.5. Strategy Overview 19.1.6.5.1. Marketing Strategy 19.1.7. Apteo 19.1.7.1. Overview 19.1.7.2. Product Portfolio 19.1.7.3. Profitability by Market Segments 19.1.7.4. Sales Footprint 19.1.7.5. Strategy Overview 19.1.7.5.1. Marketing Strategy 19.1.8. Runway 19.1.8.1. Overview 19.1.8.2. Product Portfolio 19.1.8.3. Profitability by Market Segments 19.1.8.4. Sales Footprint 19.1.8.5. Strategy Overview 19.1.8.5.1. Marketing Strategy 19.1.9. QuickBase Inc 19.1.9.1. Overview 19.1.9.2. Product Portfolio 19.1.9.3. Profitability by Market Segments 19.1.9.4. Sales Footprint 19.1.9.5. Strategy Overview 19.1.9.5.1. Marketing Strategy 19.1.10. AgilePoint Inc 19.1.10.1. Overview 19.1.10.2. Product Portfolio 19.1.10.3. Profitability by Market Segments 19.1.10.4. Sales Footprint 19.1.10.5. Strategy Overview 19.1.10.5.1. Marketing Strategy 19.1.11. MonkeyLearn 19.1.11.1. Overview 19.1.11.2. Product Portfolio 19.1.11.3. Profitability by Market Segments 19.1.11.4. Sales Footprint 19.1.11.5. Strategy Overview 19.1.11.5.1. Marketing Strategy 19.1.12. Levity 19.1.12.1. Overview 19.1.12.2. Product Portfolio 19.1.12.3. Profitability by Market Segments 19.1.12.4. Sales Footprint 19.1.12.5. Strategy Overview 19.1.12.5.1. Marketing Strategy 19.1.13. Intersect Labs 19.1.13.1. Overview 19.1.13.2. Product Portfolio 19.1.13.3. Profitability by Market Segments 19.1.13.4. Sales Footprint 19.1.13.5. Strategy Overview 19.1.13.5.1. Marketing Strategy 19.1.14. Apple 19.1.14.1. Overview 19.1.14.2. Product Portfolio 19.1.14.3. Profitability by Market Segments 19.1.14.4. Sales Footprint 19.1.14.5. Strategy Overview 19.1.14.5.1. Marketing Strategy 19.1.15. DataRobot Inc 19.1.15.1. Overview 19.1.15.2. Product Portfolio 19.1.15.3. Profitability by Market Segments 19.1.15.4. Sales Footprint 19.1.15.5. Strategy Overview 19.1.15.5.1. Marketing Strategy 20. Assumptions & Acronyms Used 21. 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No-code AI Platform Market
