Ai Based Recipe Personalization Platforms Market
AI-Based Recipe Personalization Platforms Market Forecasts to 2032 – Global Analysis By Type (Health-Targeted Meal Plans, Allergen & Diet Restriction Substitution, Flavor & Preference Personalization, Family & Batch Meal Scaling, Grocery List & Shopping Integration and Restaurant Menu Personalization), Deployment, Technology, End User and By Geography.
According to Stratistics MRC, the Global AI-Based Recipe Personalization Platforms Market is accounted for $2.9 billion in 2025 and is expected to reach $5.1 billion by 2032 growing at a CAGR of 8.3% during the forecast period. AI-Based Recipe Personalization Platforms are digital services that use machine learning algorithms to generate or modify recipes to precisely match an individual user's dietary needs, health goals, taste preferences, cooking skill level, and available ingredients. They integrate data from fitness trackers, health records, and user input to optimize nutritional content and flavor. The platform’s purpose is to simplify meal planning, encourage healthier eating habits, reduce food waste, and provide a highly tailored culinary experience.
According to Virtue Market Research, AI recipe generators use NLP and machine learning to tailor meals based on ingredients, dietary restrictions, and taste preferences, enhancing convenience and health outcomes.
Market Dynamics:
Driver:
Rising consumer demand for customized meal experiences
Fueled by the growing desire for individualized culinary choices, AI-based recipe personalization platforms are transforming how consumers plan and prepare meals. Increasing awareness of nutritional diversity, dietary restrictions, and flavor preferences drives adoption across demographics. Enhanced AI algorithms now tailor recipes based on calorie needs, allergies, and cultural tastes. Moreover, social media trends promoting unique meal experiences amplify engagement. Spurred by digital lifestyle shifts, personalized cooking solutions enhance user satisfaction. Consequently, customization remains a core driver of market growth.
Restraint:
Limited data interoperability across food and nutrition databases
The lack of standardized data frameworks across global food and nutrition systems hampers the accuracy of AI-driven recipe recommendations. Inconsistent ingredient labeling, regional measurement variations, and incomplete nutrition datasets limit model precision. Even with advanced algorithms, fragmented data reduces platform scalability and interoperability. Additionally, integration challenges with third-party APIs slow development efficiency. Spurred by data silos, users may experience inconsistencies in recipe generation. Hence, limited data harmonization remains a key market restraint hindering widespread adoption.
Opportunity:
Integration with voice assistants and smart kitchen appliances
Propelled by the expanding IoT ecosystem, AI-based recipe platforms are increasingly integrating with smart home devices for hands-free culinary guidance. Voice-enabled assistants like Alexa and Google Home enhance accessibility, simplifying real-time cooking interactions. Meanwhile, smart ovens, mixers, and nutrition scales allow precise execution of AI-generated recipes. This interconnected environment promotes convenience and user engagement. Fueled by the rise of connected homes, cross-device synchronization enables immersive cooking experiences. Therefore, smart integration offers vast growth opportunities for market expansion.
Threat:
Algorithmic bias leading to inconsistent recipe outcomes
Bias within AI training datasets can cause inaccurate or culturally skewed recipe recommendations. Over-reliance on limited data sources may overlook regional cuisine diversity and ingredient availability. Such inconsistencies erode user trust and diminish personalization accuracy. Moreover, biased algorithms can misrepresent nutritional values or dietary suitability. Spurred by lack of transparency in AI model design, regulatory scrutiny is increasing. Consequently, algorithmic fairness and data diversity have become critical threats to platform reliability and consumer adoption.
Covid-19 Impact:
The pandemic reshaped consumer behavior, accelerating home cooking trends and digital recipe engagement. Lockdowns prompted widespread use of AI-based meal planning tools as households sought healthier, cost-efficient dining solutions. Simultaneously, supply chain disruptions led users to depend on adaptive recipe platforms for ingredient substitutions. Fueled by remote lifestyle patterns, cooking became a wellness-oriented activity. Post-pandemic, sustained interest in home dining and nutrition tracking continues to drive market expansion. Thus, COVID-19 acted as a major catalyst for digital culinary innovation.
The health-targeted meal plans segment is expected to be the largest during the forecast period
The health-targeted meal plans segment is expected to account for the largest market share during the forecast period, resulting from rising consumer focus on dietary wellness and preventive nutrition. Fueled by growing demand for diabetes-friendly, low-carb, and protein-optimized recipes, platforms increasingly emphasize health-driven personalization. AI systems analyze biomarkers and dietary goals to deliver adaptive recommendations. Moreover, collaborations with nutritionists and wellness apps enhance precision. Spurred by global wellness trends, health-targeted personalization drives the segment’s dominance in market value.
The b2b restaurant integrations segment is expected to have the highest CAGR during the forecast period
Over the forecast period, the B2B restaurant integrations segment is predicted to witness the highest growth rate, propelled by rapid digital transformation in the food service industry. Restaurants are adopting AI recipe personalization to deliver unique, customer-specific menus and optimize ingredient sourcing. Fueled by rising expectations for experiential dining, AI integration supports menu innovation and waste reduction. Additionally, partnerships with food delivery platforms strengthen value chains. Hence, the segment’s expansion is reinforced by data-driven culinary customization and operational efficiency.
Region with largest share:
During the forecast period, the Asia Pacific region is expected to hold the largest market share, attributed to growing smartphone penetration and digital food culture across China, Japan, India, and South Korea. Spurred by urbanization and changing dietary lifestyles, consumers increasingly favor AI-enabled meal personalization apps. Regional investments in smart kitchen technology further boost adoption. Additionally, rising disposable incomes and culinary experimentation accelerate market maturity. Supported by local innovation ecosystems, Asia Pacific continues to lead global market dominance.
Region with highest CAGR:
Over the forecast period, the North America region is anticipated to exhibit the highest CAGR associated with its strong technological infrastructure and early adoption of AI-driven consumer platforms. Fueled by robust integration of IoT-enabled kitchen devices and AI analytics, users increasingly demand hyper-personalized cooking experiences. Major players invest heavily in machine learning models to enhance taste prediction and dietary alignment. Furthermore, partnerships with restaurants and retail brands enrich product ecosystems. Consequently, North America emerges as the fastest-growing innovation hub.
Key players in the market
Some of the key players in AI-Based Recipe Personalization Platforms Market include Whisk, Yummly, SideChef, Innit Inc., Edamam LLC, Spoonacular, Cookpad Inc., Tasty, Foodvisor, Nutrino Health Ltd., EatLove, Noom Inc., PlateJoy, Bitesnap, Mealime, KitchenPal, and FitMenCook.
Key Developments:
In May 2025, Edamam launched an upgraded version of its Nutrition Analysis API, improving accuracy in dietary tagging and allergen detection. The update supports real-time recipe personalization for food delivery and wellness platforms.
In April 2025, Innit expanded its Food Intelligence Platform to support over 2 million product scores and personalized nutrition insights. The update includes AI-driven grocery planning and automated cooking instructions tailored to health conditions and dietary goals.
In March 2025, Yummly enhanced its AI capabilities by launching Yummly Smart Meal Planner, which uses dietary goals, cooking time, and pantry items to generate weekly meal plans. It also added voice-controlled cooking instructions compatible with smart kitchen devices.
In January 2025, Cookpad expanded its global recipe-sharing community by launching localized AI-curated content in Southeast Asia. The platform now supports multilingual recipe generation and ingredient substitution based on regional availability.
Types Covered:
• Health-Targeted Meal Plans
• Allergen & Diet Restriction Substitution
• Flavor& Preference Personalization
• Family & Batch Meal Scaling
• Grocery List & Shopping Integration
• Restaurant Menu Personalization
Deployments Covered:
• Mobile App Integration
• API & SDK Licensing
• Embedded Appliance Software
• Cloud-Based SaaS Platforms
• B2B Restaurant Integrations
• White-Label Solutions
Technologies Covered:
• Machine Learning & Recommendation Engines
• Natural Language Processing (NLP)
• Computer Vision
• Predictive Nutrition Algorithms
• Integration with Food IoT & Appliances
End Users Covered:
• Individual Consumers
• Health & Nutrition Apps
• Meal Kit Providers
• Restaurants & Cloud Kitchens
• Grocery Retailers
• Healthcare & Dietician Services
Regions Covered:
• North America
o US
o Canada
o Mexico
• Europe
o Germany
o UK
o Italy
o France
o Spain
o Rest of Europe
• Asia Pacific
o Japan
o China
o India
o Australia
o New Zealand
o South Korea
o Rest of Asia Pacific
• South America
o Argentina
o Brazil
o Chile
o Rest of South America
• Middle East & Africa
o Saudi Arabia
o UAE
o Qatar
o South Africa
o Rest of Middle East & Africa
What our report offers:
- Market share assessments for the regional and country-level segments
- Strategic recommendations for the new entrants
- Covers Market data for the years 2024, 2025, 2026, 2028, and 2032
- Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
- Strategic recommendations in key business segments based on the market estimations
- Competitive landscaping mapping the key common trends
- Company profiling with detailed strategies, financials, and recent developments
- Supply chain trends mapping the latest technological advancements
Free Customization Offerings:
All the customers of this report will be entitled to receive one of the following free customization options:
• Company Profiling
o Comprehensive profiling of additional market players (up to 3)
o SWOT Analysis of key players (up to 3)
• Regional Segmentation
o Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
• Competitive Benchmarking
o Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances
Table of Contents
1 Executive Summary
2 Preface
2.1 Abstract
2.2 Stake Holders
2.3 Research Scope
2.4 Research Methodology
2.4.1 Data Mining
2.4.2 Data Analysis
2.4.3 Data Validation
2.4.4 Research Approach
2.5 Research Sources
2.5.1 Primary Research Sources
2.5.2 Secondary Research Sources
2.5.3 Assumptions
3 Market Trend Analysis
3.1 Introduction
3.2 Drivers
3.3 Restraints
3.4 Opportunities
3.5 Threats
3.6 Technology Analysis
3.7 End User Analysis
3.8 Emerging Markets
3.9 Impact of Covid-19
4 Porters Five Force Analysis
4.1 Bargaining power of suppliers
4.2 Bargaining power of buyers
4.3 Threat of substitutes
4.4 Threat of new entrants
4.5 Competitive rivalry
5 Global AI-Based Recipe Personalization Platforms Market, By Type
5.1 Introduction
5.2 Health-Targeted Meal Plans
5.3 Allergen & Diet Restriction Substitution
5.4 Flavor& Preference Personalization
5.5 Family & Batch Meal Scaling
5.6 Grocery List & Shopping Integration
5.7 Restaurant Menu Personalization
6 Global AI-Based Recipe Personalization Platforms Market, By Deployment
6.1 Introduction
6.2 Mobile App Integration
6.3 API & SDK Licensing
6.4 Embedded Appliance Software
6.5 Cloud-Based SaaS Platforms
6.6 B2B Restaurant Integrations
6.7 White-Label Solutions
7 Global AI-Based Recipe Personalization Platforms Market, By Technology
7.1 Introduction
7.2 Machine Learning & Recommendation Engines
7.3 Natural Language Processing (NLP)
7.4 Computer Vision
7.5 Predictive Nutrition Algorithms
7.6 Integration with Food IoT & Appliances
8 Global AI-Based Recipe Personalization Platforms Market, By End User
8.1 Introduction
8.2 Individual Consumers
8.3 Health & Nutrition Apps
8.4 Meal Kit Providers
8.5 Restaurants & Cloud Kitchens
8.6 Grocery Retailers
8.7 Healthcare & Dietician Services
9 Global AI-Based Recipe Personalization Platforms Market, By Geography
9.1 Introduction
9.2 North America
9.2.1 US
9.2.2 Canada
9.2.3 Mexico
9.3 Europe
9.3.1 Germany
9.3.2 UK
9.3.3 Italy
9.3.4 France
9.3.5 Spain
9.3.6 Rest of Europe
9.4 Asia Pacific
9.4.1 Japan
9.4.2 China
9.4.3 India
9.4.4 Australia
9.4.5 New Zealand
9.4.6 South Korea
9.4.7 Rest of Asia Pacific
9.5 South America
9.5.1 Argentina
9.5.2 Brazil
9.5.3 Chile
9.5.4 Rest of South America
9.6 Middle East & Africa
9.6.1 Saudi Arabia
9.6.2 UAE
9.6.3 Qatar
9.6.4 South Africa
9.6.5 Rest of Middle East & Africa
10 Key Developments
10.1 Agreements, Partnerships, Collaborations and Joint Ventures
10.2 Acquisitions & Mergers
10.3 New Product Launch
10.4 Expansions
10.5 Other Key Strategies
11 Company Profiling
11.1 Whisk
11.2 Yummly
11.3 SideChef
11.4 Innit Inc.
11.5 Edamam LLC
11.6 Spoonacular
11.7 Cookpad Inc.
11.8 Tasty
11.9 Foodvisor
11.10 Nutrino Health Ltd.
11.11 EatLove
11.12 Noom Inc.
11.13 PlateJoy
11.14 Bitesnap
11.15 Mealime
11.16 KitchenPal
11.17 FitMenCook
List of Tables
1 Global AI-Based Recipe Personalization Platforms Market Outlook, By Region (2024-2032) ($MN)
2 Global AI-Based Recipe Personalization Platforms Market Outlook, By Type (2024-2032) ($MN)
3 Global AI-Based Recipe Personalization Platforms Market Outlook, By Health-Targeted Meal Plans (2024-2032) ($MN)
4 Global AI-Based Recipe Personalization Platforms Market Outlook, By Allergen & Diet Restriction Substitution (2024-2032) ($MN)
5 Global AI-Based Recipe Personalization Platforms Market Outlook, By Flavor& Preference Personalization (2024-2032) ($MN)
6 Global AI-Based Recipe Personalization Platforms Market Outlook, By Family & Batch Meal Scaling (2024-2032) ($MN)
7 Global AI-Based Recipe Personalization Platforms Market Outlook, By Grocery List & Shopping Integration (2024-2032) ($MN)
8 Global AI-Based Recipe Personalization Platforms Market Outlook, By Restaurant Menu Personalization (2024-2032) ($MN)
9 Global AI-Based Recipe Personalization Platforms Market Outlook, By Deployment (2024-2032) ($MN)
10 Global AI-Based Recipe Personalization Platforms Market Outlook, By Mobile App Integration (2024-2032) ($MN)
11 Global AI-Based Recipe Personalization Platforms Market Outlook, By API & SDK Licensing (2024-2032) ($MN)
12 Global AI-Based Recipe Personalization Platforms Market Outlook, By Embedded Appliance Software (2024-2032) ($MN)
13 Global AI-Based Recipe Personalization Platforms Market Outlook, By Cloud-Based SaaS Platforms (2024-2032) ($MN)
14 Global AI-Based Recipe Personalization Platforms Market Outlook, By B2B Restaurant Integrations (2024-2032) ($MN)
15 Global AI-Based Recipe Personalization Platforms Market Outlook, By White-Label Solutions (2024-2032) ($MN)
16 Global AI-Based Recipe Personalization Platforms Market Outlook, By Technology (2024-2032) ($MN)
17 Global AI-Based Recipe Personalization Platforms Market Outlook, By Machine Learning & Recommendation Engines (2024-2032) ($MN)
18 Global AI-Based Recipe Personalization Platforms Market Outlook, By Natural Language Processing (NLP) (2024-2032) ($MN)
19 Global AI-Based Recipe Personalization Platforms Market Outlook, By Computer Vision (2024-2032) ($MN)
20 Global AI-Based Recipe Personalization Platforms Market Outlook, By Predictive Nutrition Algorithms (2024-2032) ($MN)
21 Global AI-Based Recipe Personalization Platforms Market Outlook, By Integration with Food IoT & Appliances (2024-2032) ($MN)
22 Global AI-Based Recipe Personalization Platforms Market Outlook, By End User (2024-2032) ($MN)
23 Global AI-Based Recipe Personalization Platforms Market Outlook, By Individual Consumers (2024-2032) ($MN)
24 Global AI-Based Recipe Personalization Platforms Market Outlook, By Health & Nutrition Apps (2024-2032) ($MN)
25 Global AI-Based Recipe Personalization Platforms Market Outlook, By Meal Kit Providers (2024-2032) ($MN)
26 Global AI-Based Recipe Personalization Platforms Market Outlook, By Restaurants & Cloud Kitchens (2024-2032) ($MN)
27 Global AI-Based Recipe Personalization Platforms Market Outlook, By Grocery Retailers (2024-2032) ($MN)
28 Global AI-Based Recipe Personalization Platforms Market Outlook, By Healthcare & Dietician Services (2024-2032) ($MN)
Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.
List of Figures
RESEARCH METHODOLOGY
We at ‘Stratistics’ opt for an extensive research approach which involves data mining, data validation, and data analysis. The various research sources include in-house repository, secondary research, competitor’s sources, social media research, client internal data, and primary research.
Our team of analysts prefers the most reliable and authenticated data sources in order to perform the comprehensive literature search. With access to most of the authenticated data bases our team highly considers the best mix of information through various sources to obtain extensive and accurate analysis.
Each report takes an average time of a month and a team of 4 industry analysts. The time may vary depending on the scope and data availability of the desired market report. The various parameters used in the market assessment are standardized in order to enhance the data accuracy.
Data Mining
The data is collected from several authenticated, reliable, paid and unpaid sources and is filtered depending on the scope & objective of the research. Our reports repository acts as an added advantage in this procedure. Data gathering from the raw material suppliers, distributors and the manufacturers is performed on a regular basis, this helps in the comprehensive understanding of the products value chain. Apart from the above mentioned sources the data is also collected from the industry consultants to ensure the objective of the study is in the right direction.
Market trends such as technological advancements, regulatory affairs, market dynamics (Drivers, Restraints, Opportunities and Challenges) are obtained from scientific journals, market related national & international associations and organizations.
Data Analysis
From the data that is collected depending on the scope & objective of the research the data is subjected for the analysis. The critical steps that we follow for the data analysis include:
- Product Lifecycle Analysis
- Competitor analysis
- Risk analysis
- Porters Analysis
- PESTEL Analysis
- SWOT Analysis
The data engineering is performed by the core industry experts considering both the Marketing Mix Modeling and the Demand Forecasting. The marketing mix modeling makes use of multiple-regression techniques to predict the optimal mix of marketing variables. Regression factor is based on a number of variables and how they relate to an outcome such as sales or profits.
Data Validation
The data validation is performed by the exhaustive primary research from the expert interviews. This includes telephonic interviews, focus groups, face to face interviews, and questionnaires to validate our research from all aspects. The industry experts we approach come from the leading firms, involved in the supply chain ranging from the suppliers, distributors to the manufacturers and consumers so as to ensure an unbiased analysis.
We are in touch with more than 15,000 industry experts with the right mix of consultants, CEO's, presidents, vice presidents, managers, experts from both supply side and demand side, executives and so on.
The data validation involves the primary research from the industry experts belonging to:
- Leading Companies
- Suppliers & Distributors
- Manufacturers
- Consumers
- Industry/Strategic Consultants
Apart from the data validation the primary research also helps in performing the fill gap research, i.e. providing solutions for the unmet needs of the research which helps in enhancing the reports quality.
For more details about research methodology, kindly write to us at info@strategymrc.com
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