Visiongain has published a new report entitled Generative AI Models Market Report 2025-2035 (Including Impact of U.S. Trade Tariffs): Forecasts by Organisation Size (Large Enterprises, Small & Medium Enterprises (SMEs)), by Deployment Mode (Cloud-based, On-premise, Edge Deployment), by Component (Software/Model Framework, Training & Fine-tuning Services, Inference-as-a-Service (IaaS), Consulting & Integration), by Application (Text Generation, Image & Video Generation, Audio & Speech Generation, Code Generation, Other), by Model Type (Large Language Models (LLMs), Multimodal Models, Diffusion Models, Generative Adversarial Networks (GANs), Transformer Models, Variational Autoencoders (VAEs)), by End-users (IT & Telecom, Healthcare & Life Sciences, BFSI, Retail & E-commerce, Automotive, Media & Entertainment, Manufacturing, Gaming, Government & Defence, Education, Other) AND Regional and Leading National Market Analysis PLUS Analysis of Leading Companies.

The global generative AI model market is estimated at US$68.1 billion in 2025 and is projected to grow at a CAGR of 36.2% during the forecast period 2025-2035.

Impact of US Trade Tariffs on the Global Generative AI Model Market   

Trade tariffs—particularly those targeting China and other major exporters of semiconductors and electronics—are reshaping the global supply chain for generative AI. Models rely on high-performance infrastructure such as GPUs, TPUs, servers, and networking equipment, much of which is produced in regions affected by U.S. trade restrictions. Tariffs on Chinese-made components, alongside export controls on advanced chips like NVIDIA’s A100 and H100, have disrupted supply chains and elevated costs for AI developers worldwide.

These constraints are driving two parallel shifts. Western companies are diversifying manufacturing bases to markets such as Vietnam, Mexico, and India to mitigate supply risk. In parallel, Chinese firms are accelerating domestic chip innovation and developing independent large language models to reduce reliance on U.S. technologies. The result is an emerging bifurcation in the global AI ecosystem—between Western models built on U.S.-aligned infrastructure and parallel ecosystems evolving in China, Russia, and parts of the Global South.

For U.S.-based players, tariffs are exerting upstream cost pressures on hardware, which could translate into higher cloud AI pricing and increased capex for training and inference. Yet, they also create incentives for domestic manufacturing, supported by initiatives such as the CHIPS and Science Act. Over the long term, the industry may experience greater regionalisation of model development, fragmented data governance regimes, and a reconfiguration of compute and data infrastructure shaped by geopolitical dynamics.

Proliferation of Custom Foundation Models and Open-Source Frameworks Accelerates Generative AI Adoption

As generative AI matures, the market is shifting toward customised foundation models tailored to industry-specific needs. Players such as Cohere, Anthropic, and Mistral AI are developing both open and proprietary models designed for applications in finance, healthcare, and legal services—sectors where accuracy, compliance, and contextual understanding are critical.

At the same time, open-source initiatives are accelerating innovation and accessibility. Meta’s LLaMA-3 and Stability AI’s Stable Diffusion have lowered barriers to entry, enabling developers and start-ups to experiment, adapt, and scale at significantly reduced cost. This openness is cultivating a dynamic ecosystem where commercial and community-driven innovation reinforce each other, broadening adoption and expanding the scope of generative AI applications.

How will this Report Benefit you?

Visiongain’s 486-page report provides 141 tables and 240 charts/graphs. Our new study is suitable for anyone requiring commercial, in-depth analyses for the generative AI model market, along with detailed segment analysis in the market. Our new study will help you evaluate the overall global and regional market for generative AI model. Get financial analysis of the overall market and different segments including organization size, deployment mode, component, application, model type, and end-user, and capture higher market share. We believe that there are strong opportunities in this fast-growing generative AI model market. See how to use the existing and upcoming opportunities in this market to gain revenue benefits in the near future. Moreover, the report will help you to improve your strategic decision-making, allowing you to frame growth strategies, reinforce the analysis of other market players, and maximise the productivity of the company.

What are the Current Market Drivers?

Rapid Advancements in Computing Infrastructure Accelerate Scalable Deployment of Generative AI

The rapid evolution of hardware accelerators—GPUs, TPUs, and purpose-built AI chips—has become a foundational driver of generative AI adoption. Leaders such as NVIDIA, with its H100 and GH200 chips, and AMD are delivering the computational capacity required for both training and inference at scale.

Equally important, cloud platforms like Amazon Bedrock, Microsoft Azure OpenAI Service, and Google Cloud Vertex AI are democratising access by offering enterprise-ready infrastructure without the need for heavy upfront investment. Together, these hardware and cloud advances are eliminating traditional scalability bottlenecks, enabling organisations of all sizes to deploy generative AI solutions more quickly and cost-effectively.

Integration with Business Productivity and Consumer Tools Expands Generative AI Adoption and Market Reach

Generative AI is moving from niche applications into the core of enterprise and consumer workflows, rapidly expanding its reach and indispensability. Microsoft has embedded GPT-powered Copilots across Word, Excel, and Teams, fundamentally reshaping productivity through AI-driven writing, analysis, and collaboration. Google has followed suit by infusing Gemini into Workspace, enabling smart document drafting, email summarisation, and predictive spreadsheet insights.

On the consumer front, platforms such as Snapchat, Spotify, and Duolingo are harnessing generative AI to deliver hyper-personalised experiences that deepen engagement. This widespread integration across both enterprise and consumer ecosystems not only validates commercial viability but also accelerates mainstream adoption, cementing generative AI as a foundational layer in digital transformation.

Where are the Market Opportunities?

Vertical-Specific Generative AI Solutions Unlock Significant Market Opportunities

A major opportunity in generative AI lies in building domain-tailored models designed to meet the specialised requirements of industries such as healthcare, legal services, finance, marketing, and education. Emerging players like Hippocratic AI (focused on medical decision support) and Pymetrics (HR and workforce assessments) illustrate how customised systems can deliver greater accuracy, contextual relevance, and regulatory compliance than general-purpose LLMs.

As enterprises prioritise precision and trust in sensitive or highly regulated environments, demand is shifting toward industry-specific co-pilots, assistants, and automation tools. This movement signals a new wave of adoption where depth of capability, not breadth, becomes the competitive differentiator—positioning vertical-specialised AI as a key growth frontier for the sector.

Integration with Enterprise Software Ecosystems Creates Large-Scale Monetisation Pathways for Generative AI

A significant growth opportunity lies in the seamless integration of generative AI into core enterprise applications such as CRMs, ERPs, and productivity platforms. Leading vendors are already moving in this direction: Microsoft has rolled out Copilot across Office 365, Salesforce has launched Einstein GPT within its CRM suite, and SAP is embedding generative AI into its enterprise ecosystem. These integrations enable enterprises to automate workflows, generate reports, summarise meetings, and personalise customer engagement at scale.

For generative AI providers, this trend creates powerful monetisation pathways. By partnering with established enterprise software companies or offering API-based solutions that plug directly into digital environments, vendors can unlock recurring revenue streams while embedding themselves deeper into enterprise workflows. The result is a mutually reinforcing dynamic: enterprise software becomes smarter and more valuable, while generative AI vendors gain access to vast installed customer bases.

Competitive Landscape

The major players operating in the generative AI model market are Alphabet Inc.

Amazon Web Services, Inc., qBotica, Centific, Cognigy, Fortinet, Inc., IBM, LivePerson, Musarubra US LLC, Microsoft, NVIDIA Corporation, OpenAI, Scale AI, Inc., TELUS Digital, and Together AI. These major players operating in this market have adopted various strategies comprising M&A, collaborations, investment in R&D, regional business expansion, partnerships, and new product launch.

Recent Developments

• On 23^rd June 2025, Centific partnered with Virtue AI to enhance responsible AI-by-design with real-time model monitoring using Virtue Guard. Enables faster and safer GenAI development while improving compliance and reducing operational risks.
• On 17^th June 25, Hexagon unveiled AEON, a humanoid robot built with NVIDIA AI & robotics software for industrial use. AEON supports tasks like reality capture, manipulation, inspection, and digital twin creation. Planned deployment spans industries like manufacturing, logistics, aerospace, and automotive.
• On 13^th June 25, NVIDIA and Deutsche Telekom announced the world’s first industrial AI cloud to serve European manufacturers. It will support AI-driven manufacturing applications like simulation, digital twins, and robotics. The cloud facility will be based in Germany to foster sovereign AI infrastructure.

Notes for Editors

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