No Code AI Platform Market Size, Growth, Share & Trends Report 2030

Name: No Code AI Platform Market Size, Growth, Share & Trends Report 2030
Creator: Mordor Intelligence
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No Code AI Platform Market Size and Share

Market Overview

Study Period	2019 - 2030
Market Size (2025)	USD 4.06 Billion
Market Size (2030)	USD 10.43 Billion
Growth Rate (2025 - 2030)	20.78% CAGR
Fastest Growing Market	Asia Pacific
Largest Market	North America
Market Concentration	Low
Major Players *Disclaimer: Major Players sorted in no particular order Image © Mordor Intelligence. Reuse requires attribution under CC BY 4.0.

No Code AI Platform Market (2025 - 2030) — Image © Mordor Intelligence. Reuse requires attribution under CC BY 4.0.

No Code AI Platform Market Analysis by Mordor Intelligence

The No-Code AI Platform market size stood at USD 4.06 billion in 2025 and is forecast to reach USD 10.43 billion by 2030, registering a 20.78% CAGR over the period. Strong enterprise demand for faster application delivery, growing citizen-developer programs, and steady advances in generative AI engines continued to lift adoption across industries. North America remained the largest regional hub, supported by deep venture capital pools and mature cloud infrastructure, while Asia-Pacific advanced as the fastest-growing arena on the back of government-backed digital transformation initiatives. Platform vendors deepened their marketplaces with pre-built models, helping firms with limited data-science talent jump-start AI projects. Heightened regulatory attention around model governance, however, reinforced a preference for private-cloud and on-premises deployments within healthcare and financial services.

Key Report Takeaways

By component, platforms and solutions led with 68% revenue in 2024, while services are projected to expand at a 31% CAGR through 2030.
By technology, predictive and prescriptive analytics held 51% of the No-Code AI Platform market share in 2024; multimodal generative AI is expected to climb at a 46% CAGR to 2030.
By data modality, image and video processing applications are poised to grow at 38% CAGR from 2025-2030, outpacing text-centric use cases.
By deployment mode, on-premises and private cloud options accounted for 57% of the No-Code AI Platform market size in 2024 as regulated sectors prioritized data sovereignty.
By enterprise size, SMEs are forecast to grow at 40% CAGR through 2030, narrowing the usage gap with large enterprises that held 64% share in 2024.
By vertical, BFSI maintained a 23% revenue lead in 2024, while healthcare is projected to post the fastest 37% CAGR through 2030.
By geography, North America maintained a 38% revenue lead in 2024, while Asia-Pacific is projected to post the fastest 33% CAGR through 2030.

Global No Code AI Platform Market Trends and Insights

Drivers Impact Analysis

Driver	(~) % Impact on CAGR Forecast	Geographic Relevance	Impact Timeline
Rising adoption of citizen-developer initiatives in BFSI	+3.5%	North America and Europe	Medium term (2-4 years)
Generative-AI add-ons boosting platform stickiness	+3.2%	Global	Short term (≤ 2 years)
Surging demand for verticalized no-code AI	+2.8%	North America, Europe, Asia-Pacific	Medium term (2-4 years)
Edge-optimised no-code ML for IoT fleets	+2.4%	Asia-Pacific, North America	Long term (≥ 4 years)
Expansion of AI marketplaces within platforms	+2.1%	Europe, North America, Asia-Pacific	Medium term (2-4 years)
Venture funding shift to hyper-specific use cases	+0.7%	Global	Short term (≤ 2 years)

Source: Mordor Intelligence

Rising Adoption of Citizen-Developer Initiatives in BFSI

Major banks scaled internal programs that let business users build compliance and customer-experience workflows without coding, cutting manual review effort by up to 90%.^{[1]Lucinity, “7 Compliance and FinCrime Trends for 2025,” lucinity.com} Budget ownership shifted from IT to line managers, accelerating project throughput and enlarging the addressable No-Code AI Platform market. Early movers reported faster loan-underwriting cycles and improved fraud-detection accuracy. Union Foncière de France demonstrated cost savings near 30% after rolling out a no-code sales-engagement engine. Such results encouraged regulators to view low-code tooling as a valid path toward operational resilience, further reinforcing adoption.

Generative-AI Add-ons Boosting Platform Stickiness

Vendors embedded multimodal large-language and vision models into drag-and-drop studios, enabling users to turn plain-language prompts into fully formed workflows. Microsoft Copilot Studio surpassed 230,000 organisational tenants during 2025, underscoring the retention uplift tied to generative extensions. Retailers leveraged these add-ons to generate marketing copy and imagery, while manufacturers applied them to swift design iterations. Each new capability kept customers within the same platform, curbing churn and enlarging subscription footprints across the No-Code AI Platform market.

Surging Demand for Verticalized No-Code AI

Hospitals, insurers, and industrial plants increasingly sought domain-specific templates that embed sector regulations and vocabulary. John Snow Labs released specialised medical language models tuned for clinical-note automation, illustrating the appeal of purpose-built solutions. Buyers valued shorter deployment times and certified compliance artifacts over generic toolkits, lifting ASPs and opening white-space for niche vendors within the No-Code AI Platform market.

Edge-Optimized No-Code ML for IoT Fleets

Latency-sensitive use cases in manufacturing and utilities drove interest in edge-ready workflows. Edge Impulse logged more than 118,000 projects on its tiny-ML service, showing an appetite for low-power inference pipelines that run without cloud links. Companies used no-code dashboards to compress and deploy models onto asset-monitoring sensors, enabling real-time anomaly alerts while satisfying data-sovereignty rules. This evolution expanded the reach of the No-Code AI Platform market into brownfield operational environments.

Restraints Impact Analysis

Restraint	(~) % Impact on CAGR Forecast	Geographic Relevance	Impact Timeline
Limited custom model governance frameworks	-2.4%	Global (focus on Europe)	Medium term (2-4 years)
Persistent “shadow-IT” security concerns in regulated sectors	-1.6%	North America, Europe	Short term (≤ 2 years)
Vendor lock-in tied to proprietary AutoML pipelines	-1.1%	Global	Long term (≥ 4 years)
Data-residency challenges for cross-border deployments	-0.7%	Europe, Asia-Pacific	Medium term (2-4 years)

Source: Mordor Intelligence

Limited Custom Model Governance Frameworks

Rapid citizen-developer growth outpaced internal review processes, creating blind spots in traceability and bias control. The FAICP framework urged firms to embed lifecycle checkpoints tailored to no-code assets, yet adoption remained uneven. In Europe, the impending AI Act mandated rigorous documentation for high-risk systems, raising compliance costs and slowing some purchase decisions inside the No-Code AI Platform market.

Persistent “Shadow-IT” Security Concerns in Regulated Sectors

Employees sometimes bypassed official procurement to trial external AI tools, broadening the threat surface. Academic work linked shadow-AI incidents to data-exposure risks, especially where sensitive financial or health records were involved. Financial institutions consequently tightened identity and access controls, delaying deployments until platforms delivered granular policy enforcement and audit logs.

Segment Analysis

By Component: Services Outpace Platform Growth

Services revenue accelerated on a projected 31% CAGR for 2025-2030, even though platforms held 68% share of the No-Code AI Platform market in 2024. Clients sought specialised support for model optimisation, cost governance, and organisational change. The introduction of FinOps-style engagements addressed the issue of runaway compute bills during experimentation phases. MIT Professional Education’s 12-week certificate, centred on practical low-code implementation, underlined the growing demand for workforce upskilling.^{[2]MIT Professional Education, “No-Code AI and Machine Learning,” professional.mit.edu}

The consulting layer expanded beyond initial setup into managed-lifecycle services covering continuous validation and sector-specific compliance. This shift pushed many vendors to launch partner ecosystems, offering pre-certified consultancies to de-risk projects. The trend increased average contract values and fortified recurring revenue streams within the No-Code AI Platform market.

No Code AI Platform Market: Market Share by Component — Image © Mordor Intelligence. Reuse requires attribution under CC BY 4.0.

By Technology: Multi-modal GenAI Disrupts Analytics Dominance

Predictive and prescriptive analytics commanded a 51% share in 2024, yet multimodal generative AI is forecast to rise at a 46% CAGR. Google’s Gemini framework, capable of fusing text, image, and audio input, illustrated the widening toolkit available to non-technical builders. The No-Code AI Platform market size for generative functions is expected to expand sharply as firms embed AI-driven content production into marketing and design workflows.

Business users now compose applications with natural-language prompts, skipping SQL and Python entirely. Start-ups that integrate vector databases and retrieval-augmented generation gained traction, particularly for customer-service knowledge bots. This shift lowered time-to-value and anchored the No-Code AI Platform market around conversational experiences rather than dashboard-centric analytics.

By Data Modality: Image/Video Processing Accelerates Innovation

Text kept a 35% share in 2024, but computer-vision workloads are projected to grow at 38% CAGR to 2030. Retailers used visual AI to automate shelf monitoring, and hospitals trialled imaging triage tools. Super Annotate placed the multimodal AI segment value at USD 1.2 billion for 2023, with growth above 30% through 2032. No-code studios bundled pre-trained object-detection blocks, allowing domain experts to refine models using small, labelled datasets.

Tabular and time-series inputs remained crucial for IoT predictive maintenance. Vendors augmented these pipelines with low-latency embedding layers, letting builders mix sensor feeds and camera frames inside a single workflow. Such convergence lifted the practical ceiling on use-case complexity across the No-Code AI Platform market.

No Code AI Platform Market: Market Share by Data Modality — Image © Mordor Intelligence. Reuse requires attribution under CC BY 4.0.

By Deployment Mode: Private Cloud Prioritises Security

Private-cloud and on-premise instances accounted for 57% of the No-Code AI Platform market size in 2024 amid heightened regulatory oversight. HPE introduced a turnkey private-cloud stack that promised same-day deployment of GPU clusters, appealing to banks and hospitals seeking data-sovereignty controls.

Public-cloud SaaS grew off a smaller base at 34% CAGR, helped by simplified subscription models. Red Hat OpenShift AI showcased hybrid orchestration that kept sensitive data local while bursting inference jobs to the public cloud during peaks. This flexibility widened deployment choices and removed a key barrier for cost-sensitive adopters inside the No-Code AI Platform market.

By Enterprise Size: SMEs Embrace AI Democratization

Large enterprises held 64% revenue in 2024, yet SMEs are set to expand at 40% CAGR through 2030, propelled by template libraries and consumption-based pricing. Appt.dev projected that no-code tooling would power 70% of new business apps by 2025, with small firms responsible for most deployments. Fast prototyping enabled a real estate agency to assemble a full property management suite in three weeks, highlighting the agility benefits.

For incumbents, no-code studios trimmed IT backlogs and unlocked shadow budgets inside functional departments. The cost parity between low-code and custom development tipped decisively in favour of simplified tooling, broadening the No-Code AI Platform market addressable base.

No Code AI Platform Market: Market Share by Enterprise Size — Image © Mordor Intelligence. Reuse requires attribution under CC BY 4.0.

By Industry Vertical: Healthcare Leads Transformative Adoption

Healthcare is projected to post a 37% CAGR, overtaking early BFSI leaders. The US Department of Health and Human Services laid out a strategic blueprint that encouraged AI for clinical documentation and public-health forecasting. Hospitals adopted speech-to-text triage and imaging anomaly detection via drag-and-drop components, reducing administrative burden.

Retailers used recommendation engines and fraud-detection pipelines, while manufacturers embedded real-time quality control on production lines. Governments trialled citizen-service chatbots. Energy utilities leveraged forecasting templates to balance grids. This breadth illustrated the horizontal reach of the No-Code AI Platform market and its fit for regulated and unregulated sectors alike.

Geography Analysis

North America retained 38% revenue share in 2024, anchored by a rich vendor ecosystem and robust venture funding that reached USD 204 billion, half of which targeted AI start-ups.^{[3]Silicon Valley Bank, “State of the Markets H1 2025,” svb.com} Financial-services leaders used citizen-developer programs to compress release cycles, while public-sector agencies piloted low-code portals for constituent engagement. Mature cloud and data-centre resources shortened onboarding times, reinforcing the region’s primacy within the No-Code AI Platform market.

Asia-Pacific delivered the fastest growth, projected at 33% CAGR through 2030. China ramped up AI hardware grants, while India’s IT-services majors packaged low-code accelerators for export and domestic clients. Kingdee International reported four consecutive years of low-code platform leadership in China and highlighted wide enterprise uptake of its AI assistants. Government incentives and smartphone ubiquity helped local SMEs leapfrog legacy software stages.

Europe emphasised privacy and ethical oversight. The forthcoming AI Act steers platform roadmaps toward explainable-AI dashboards and audit trails. Vendors responded with region-specific compliance packs and federated-learning options to sidestep cross-border data transfers. Nordic countries remained early adopters, while Southern Europe focused on public-sector digital-service upgrades, collectively growing the No-Code AI Platform market despite tighter rules.

No Code AI Platform Market CAGR (%), Growth Rate by Region — Image © Mordor Intelligence. Reuse requires attribution under CC BY 4.0.

Competitive Landscape

The vendor mix stayed moderately fragmented as enterprise software giants expanded suites and vertical start-ups carved niche footholds. Microsoft deepened Copilot Studio integrations with Dynamics workflows, locking in customers seeking unified user experiences. C3AI partnered with Google Cloud Marketplace to push no-code generative modules to a wider base.

Specialists emphasised industry-specific toolkits. John Snow Labs focused on healthcare NLP, while Edge Impulse targeted embedded sensors. Strategic alliances flourished: Citizens Financial Group opened a Hyderabad centre with Cognizant to co-develop banking AI powered by Neuro® platform components.^{[4]Citizens Financial Group, “Partnership with Cognizant,” news.cognizant.com}

Ecosystem building grew critical. Vendors launched model registries and revenue-sharing marketplaces, enabling third-party developers to monetise templates. This flywheel attracted domain partners and expanded functional coverage, intensifying rivalry yet improving client choice inside the No-Code AI Platform market.

No Code AI Platform Industry Leaders

DataRobot, Inc.
Dataiku SAS
H2O.ai Inc.
RapidMiner Inc.
BigML Inc.
*Disclaimer: Major Players sorted in no particular order

No Code AI Platform Market Concentration — Image © Mordor Intelligence. Reuse requires attribution under CC BY 4.0.

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Recent Industry Developments

May 2025: Microsoft unveiled GitHub Copilot evolution and low-code Copilot customization features at Build 2025, noting 15 million developers on Copilot and 230,000 tenant adoptions of Copilot Studio.
May 2025: John Snow Labs won the 2025 Oracle Excellence Award for its medical reasoning LLM and updated Generative AI Lab.
May 2025: HPE launched Private Cloud AI for turnkey secure deployments.
April 2025: Citizens Financial Group and Cognizant opened a Global Capability Center in Hyderabad to accelerate AI-driven banking solutions.

Table of Contents for No Code AI Platform Industry Report

1. INTRODUCTION

1.1 Study Assumptions and Market Definition
1.2 Scope of the Study

2. RESEARCH METHODOLOGY

3. EXECUTIVE SUMMARY

4. MARKET LANDSCAPE

4.1 Market Overview
4.2 Market Drivers
- 4.2.1 Rising Adoption of Citizen-Developer Initiatives in BFSI (North America and Europe)
- 4.2.2 Generative-AI Add-ons Boosting Platform Stickiness (Global)
- 4.2.3 Surging Demand for Verticalised No-Code AI (Healthcare, Industrials)
- 4.2.4 Edge-Optimised No-Code ML for IoT Fleets (Asia and Oceania)
- 4.2.5 Expansion of AI Marketplaces within Platforms (Europe)
- 4.2.6 Venture Funding Shift from General-Purpose to Hyper-Specific Use-Cases (Global)
4.3 Market Restraints
- 4.3.1 Limited Custom Model Governance Frameworks
- 4.3.2 Persistent “Shadow-IT” Security Concerns in Regulated Sectors
- 4.3.3 Vendor Lock-In due to Proprietary AutoML Pipelines
- 4.3.4 Data-Residency Challenges for Cross-Border Deployments
4.4 Value Chain Analysis
4.5 Regulatory Outlook
4.6 Technological Outlook
4.7 Porter’s Five Forces Analysis
- 4.7.1 Bargaining Power of Suppliers
- 4.7.2 Bargaining Power of Buyers
- 4.7.3 Threat of New Entrants
- 4.7.4 Threat of Substitutes
- 4.7.5 Intensity of Competitive Rivalry
4.8 Assessment of Macroeconomic Factors

5. MARKET SIZE AND GROWTH FORECASTS (VALUE)

5.1 By Component
- 5.1.1 Platforms (Solution Suites)
- 5.1.2 Services (Implementation, Training, Support)
5.2 By Technology
- 5.2.1 Natural Language Processing
- 5.2.2 Computer Vision
- 5.2.3 Predictive and Prescriptive Analytics
- 5.2.4 Multi-modal / Generative AI
5.3 By Data Modality
- 5.3.1 Text Data
- 5.3.2 Image/Video Data
- 5.3.3 Tabular and Time-Series Data
5.4 By Deployment Mode
- 5.4.1 Cloud
- 5.4.2 On-premises / Private Cloud
5.5 By Enterprise Size
- 5.5.1 Large Enterprises
- 5.5.2 Small and Medium-sized Enterprises (SMEs)
5.6 By Industry Vertical
- 5.6.1 BFSI
- 5.6.2 IT and Telecom
- 5.6.3 Healthcare and Life Sciences
- 5.6.4 Retail and E-commerce
- 5.6.5 Energy and Utilities
- 5.6.6 Government and Public Sector
- 5.6.7 Manufacturing and Industrial
- 5.6.8 Others (Education, Media, etc.)
5.7 By Geography
- 5.7.1 North America
- 5.7.1.1 United States
- 5.7.1.2 Canada
- 5.7.1.3 Mexico
- 5.7.2 Europe
- 5.7.2.1 Germany
- 5.7.2.2 United Kingdom
- 5.7.2.3 France
- 5.7.2.4 Italy
- 5.7.2.5 Rest of Europe
- 5.7.3 Asia-Pacific
- 5.7.3.1 China
- 5.7.3.2 Japan
- 5.7.3.3 South Korea
- 5.7.3.4 Taiwan
- 5.7.3.5 India
- 5.7.3.6 Rest of Asia-Pacific
- 5.7.4 South America
- 5.7.4.1 Brazil
- 5.7.4.2 Argentina
- 5.7.4.3 Rest of South America
- 5.7.5 Middle East and Africa
- 5.7.5.1 Middle East
- 5.7.5.1.1 Saudi Arabia
- 5.7.5.1.2 United Arab Emirates
- 5.7.5.1.3 Turkey
- 5.7.5.1.4 Rest of Middle East
- 5.7.5.2 Africa
- 5.7.5.2.1 South Africa
- 5.7.5.2.2 Rest of Africa

6. COMPETITIVE LANDSCAPE

6.1 Market Concentration
6.2 Strategic Moves
6.3 Market Share Analysis
6.4 Company Profiles {(includes Global level Overview, Market level overview, Core Segments, Financials as available, Strategic Information, Market Rank/Share for key companies, Products and Services, and Recent Developments)}
- 6.4.1 DataRobot Inc.
- 6.4.2 Dataiku SAS
- 6.4.3 H2O.ai Inc.
- 6.4.4 RapidMiner Inc.
- 6.4.5 BigML Inc.
- 6.4.6 Alteryx Inc.
- 6.4.7 Peltarion AB
- 6.4.8 Akkio Inc.
- 6.4.9 Levity AI GmbH
- 6.4.10 Clarifai Inc.
- 6.4.11 Cogniflow Inc.
- 6.4.12 Obviously AI Inc.
- 6.4.13 MonkeyLearn Inc.
- 6.4.14 Runway AI Inc.
- 6.4.15 Edge Impulse Inc.
- 6.4.16 Pecan AI Ltd.
- 6.4.17 Anodot Ltd.
- 6.4.18 dotData Inc.
- 6.4.19 Noogata Technologies Ltd.
- 6.4.20 Abacus.AI Inc.
- 6.4.21 Falkonry Inc.
- 6.4.22 Obviously AI Inc.
- 6.4.23 Akkio Inc.
- 6.4.24 Lobe AI (Stand-alone Brand)
- 6.4.25 Fairly AI

7. MARKET OPPORTUNITIES AND FUTURE OUTLOOK

7.1 White-space and Unmet-need Assessment

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Global No Code AI Platform Market Report Scope

No-code AI tools enable automation via user-friendly plug-and-play or drag-and-drop interfaces. These intuitive ML platforms effectively harness the time, value, and knowledge trade-off, empowering users without AI coding expertise to enhance daily operations and address business challenges.

no code ai platform market is segmented by component (platform, and services), by enterprise size (large enterprises, and SMES), by deployment (on-premise, and cloud), by end users (IT & telecom, BFSI, retail & e-commerce, healthcare & lifesciences, government, and others) and by geography (North America, Europe, Asia Pacific, Latin America, Middle East And Africa). the market size and forecasts are provided in terms of value (USD) for all the above segments.

By Component	Platforms (Solution Suites)
	Services (Implementation, Training, Support)
By Technology	Natural Language Processing
	Computer Vision
	Predictive and Prescriptive Analytics
	Multi-modal / Generative AI
By Data Modality	Text Data
	Image/Video Data
	Tabular and Time-Series Data
By Deployment Mode	Cloud
	On-premises / Private Cloud
By Enterprise Size	Large Enterprises
	Small and Medium-sized Enterprises (SMEs)
By Industry Vertical	BFSI
	IT and Telecom
	Healthcare and Life Sciences
	Retail and E-commerce
	Energy and Utilities
	Government and Public Sector
	Manufacturing and Industrial
	Others (Education, Media, etc.)

By Geography	North America	United States
		Canada
		Mexico

	Europe	Germany
		United Kingdom
		France
		Italy
		Rest of Europe

	Asia-Pacific	China
		Japan
		South Korea
		Taiwan
		India
		Rest of Asia-Pacific

	South America	Brazil
		Argentina
		Rest of South America

	Middle East and Africa	Middle East	Saudi Arabia
			United Arab Emirates
			Turkey
			Rest of Middle East

		Africa	South Africa
			Rest of Africa

By Component

Platforms (Solution Suites)

Services (Implementation, Training, Support)

By Technology

Natural Language Processing

Computer Vision

Predictive and Prescriptive Analytics

Multi-modal / Generative AI

By Data Modality

Text Data

Image/Video Data

Tabular and Time-Series Data

By Deployment Mode

Cloud

On-premises / Private Cloud

By Enterprise Size

Large Enterprises

Small and Medium-sized Enterprises (SMEs)

By Industry Vertical

BFSI

IT and Telecom

Healthcare and Life Sciences

Retail and E-commerce

Energy and Utilities

Government and Public Sector

Manufacturing and Industrial

Others (Education, Media, etc.)

By Geography

North America	United States
	Canada
	Mexico
Europe	Germany
	United Kingdom
	France
	Italy
	Rest of Europe
Asia-Pacific	China
	Japan
	South Korea
	Taiwan
	India
	Rest of Asia-Pacific
South America	Brazil
	Argentina
	Rest of South America

Middle East and Africa	Middle East	Saudi Arabia
		United Arab Emirates
		Turkey
		Rest of Middle East

	Africa	South Africa
		Rest of Africa