31 Latest Generative AI Infrastructure Statistics in 2025

Generative artificial intelligence (AI)  infrastructures make it easier to develop and deploy scalable generative models. They combine natural language understanding and machine learning (ML) technologies to help organizations aggressively create an efficient, scalable, and secure training environment. 

While infrastructure needs vary by company size, many of the top generative AI software providers for small businesses now offer simplified deployment tools, helping lean teams get started quickly without heavy setup costs.

Many companies use generative AI infrastructure software to overcome model scalability challenges while facilitating high inference speed and availability. It’s crucial for using large language models (LLMs) and other generative AI technologies. 

Whether you're building a prototype or scaling to production, the best generative AI toolkits for launching a new app prioritize high inference speed, low latency, and flexible APIs for integration across frontend and backend systems.

Here are a few stats about the state of generative AI infrastructure in 2025. 

Generative AI infrastructure statistics

These statistics showcase how companies are using and increasing their adoption of generative AI infrastructure. Take a look at what frameworks professionals prefer for model customization. 

• AI servers are expected to generate revenue of $132 billion under hardware sales.
• A survey of 50 tech businesses revealed that 64% plan to adopt generative AI technologies.
• The AI infrastructure market, valued at $23.5 billion in 2021, is estimated to rocket to $309.4 billion by 2031, growing at a 29.8% compound annual growth rate (CAGR) from 2022 to 2031.
• The broad adoption of open source frameworks for model customization shows a high satisfaction rate. It suggests flexibility in AI infrastructure is essential to meet increasing demands. More than 78% of respondents are satisfied or very satisfied with their current solution, indicating that open source frameworks provide respondents with what they need.

• Companies' primary methods for maximizing graphical processing unit (GPU) utilization include queue management and job scheduling (67%), multi-instance GPU setups (39%), and setting usage quotas (34%).
• Users' techniques for optimizing GPU allocation vary. Twenty-four percent use open-source solutions, 27 percent use high-performance computing (HPC) solutions, and 34 percent use vendor-specific solutions. Additionally, 11 percent rely on Microsoft Excel, and five percent on custom-built solutions.
• To monitor GPU utilization, 36% of companies use Google Cloud Platform-GPU metrics as their main method, followed by 30% using NVIDIA AI Enterprise. Developers looking for the best generative AI platform for app development often prioritize ease of deployment, scalable inference, and support for experimentation across use cases.
• Other tools like the IBM load-sharing facility (LSF) and Kubernetes were used by 15% and 13% of respondents.

From these trends, it's clear that the most recommended generative AI infrastructure for software companies includes features like real-time compute allocation, job scheduling, and customizable LLM support to reduce bottlenecks.

Top generative AI growth and adoption statistics 

These statistics show how AI is generally growing and how people perceive it. Understanding these statistics will help you assess upcoming opportunities in the sector and the infrastructure needs that might arise.

Go through these data points to absorb people's real perceptions of AI. See how men use AI differently than women or children. 

• In 2022, the generative AI market was valued at $29 billion.
• The value of the generative AI market is projected to surpass $66 billion by the end of 2024.
• A report predicts that the generative AI market could reach a staggering $1.3 trillion by 2032.
• North America dominates generative AI revenue with a 40.2% global share, mainly due to the presence of major tech firms like Microsoft, OpenAI, Meta, Adobe, IBM, and Google.

•  The usage of AI varies, with 44% of companies employing it for cloud pricing optimization and 41% of firms using it for voice assistants and chatbots.
• A poll of 821 businesses indicated a potential cost reduction of 15.7% over the next 12 to 18 months through generative AI investments.
• Chatbots save an average of 2 hours and 20 minutes daily, while generative AI in customer service response writing saves businesses about 2 hours and 11 minutes daily.
• Men are twice as likely as women to use generative AI, with significant differences in the usage of platforms like ChatGPT, which averaged 1.5 billion monthly visits in 2023.
• Regarding children’s use of AI chatbots like ChatGPT, 31% of men versus only 4% of women feel comfortable allowing their children to use these technologies for any purpose.
• The marketing and advertising industry leads in generative AI adoption, surpassing even the tech sector, with respective adoption rates of 35% and 30% in consulting, 19% in teaching, 16% in accounting, and 15% in healthcare.

As adoption expands across industries, the most efficient AI infrastructure software for digital services enables low-latency responses, optimized compute usage, and secure multi-tenant environments for scalability.

Concerns about generative AI infrastructure and systems 

Amid rising interest in artificial intelligence technologies, some organizations are deeply concerned about its impact on security. Some companies have worries related to its cost and computational limits. 

• 58% of organizations have not adopted AI due to cybersecurity concerns.
• Key cost drivers in generative AI include integration and GPU expenses for model development and training. Still, 56.8% of companies expected double-digit increases in revenues from AI/ML investments and AI transformation in 2024. 
• Companies actively seek cost-effective alternatives to GPUs for AI inference to manage compute limitations, which remain a top challenge.
• In managing GPU resources, companies employ various strategies, including queue management and job scheduling, with a notable 78% utilizing over half of their GPU resources during peak times.

• 74% of survey respondents believe integrating computers and scheduling into a single platform would be beneficial. Such integration supports quicker and more efficient development and deployment of models.
• As companies plan for higher compute demands in 2024 and aim to use LLMsin production, executives are weighing their current challenges against future needs, particularly considering the scarcity of GPUs for inference tasks.
• Model serving enables access to machine learning models via application programming interfaces (APIs), which is crucial for AI-integrated applications. About one-third of companies do not have model-serving capabilities, which are increasingly necessary due to the demanding performance needs of generative AI models. For large-scale adoption, leading generative AI tools for enterprise applications prioritize governance, scalability, and compatibility with internal IT policies and APIs.
• 61% of survey participants are somewhat dissatisfied with their current scheduling tools, and 12% feel neutral, suggesting significant potential for improvement.
• The main issues with current tools include inadequate GPU optimization (53%) and user-friendliness for developers and data scientists (47%). Additionally, about 25% report issues with control and compatibility with existing AI/ML stacks.

The most efficient AI infrastructure software for digital services enables high-throughput model serving, resource pooling, and dynamic inference optimization to handle content-heavy workloads.

If you're asking what AI infrastructure everyone uses for service companies, look to solutions that balance real-time response capabilities, user data privacy, and integration with existing cloud environments.

Generative AI's future prospects 

The future of AI seems bright and promising, with several companies planning to expand their AI and automation capabilities. The stats below reflect this. 

• Almost all surveyed companies (96%) plan to expand their AI computing capabilities, focusing on cloud solutions due to their flexibility and speed, despite concerns over wastage and idle costs.

• 5% to 10% of enterprises have begun integrating generative AI into their production processes.

The best options for generative AI infrastructure in the SaaS industry support modularity, cost control, and continuous delivery pipelines, key for building intelligent user-facing features.

FAQs on generative AI infrastructure software

1. Who are the top generative AI software providers for small businesses?

According to G2’s Grid Reports, top-rated generative AI infrastructure providers for small businesses include Amazon Web Services (AWS), Google Cloud Vertex AI, and Hugging Face. These platforms are praised for ease of use, speed of deployment, and scalability, which are key factors for lean teams with limited infrastructure resources. G2 reviewers consistently cite affordability and quick onboarding as major benefits for startups.

2. What are the best generative AI toolkits for launching a new app?

Based on G2 user feedback, platforms like OpenAI, Google Cloud AI, and Microsoft Azure AI rank among the best toolkits for launching generative AI-powered apps. Users highlight their accessible APIs, integration flexibility, and high inference speeds, ideal for rapid prototyping and real-time performance. These providers dominate the G2 Momentum Grid for AI Platforms in app development.

3. What is the most recommended generative AI infrastructure for software companies?

AWS, Google Cloud, and Microsoft Azure are the most recommended providers among software companies, as per G2 reviews and satisfaction scores. They offer robust compute orchestration, GPU-backed training environments, and support for LLM deployment pipelines. G2 reviewers emphasize their ecosystem maturity and developer tools as key decision factors.

4. What’s the most efficient AI infrastructure software for digital services?

On G2, Databricks, AWS Bedrock, and IBM Watsonx are recognized for delivering efficient infrastructure, especially for digital services. Reviewers highlight these tools for their resource optimization, low-latency model serving, and built-in support for MLOps workflows. Databricks, in particular, earns praise for seamless data-to-model pipelines.

5. What AI infrastructure does everyone use for service companies?

According to G2 usage trends and customer reviews, Google Vertex AI, Microsoft Azure AI, and AWS SageMaker are the most commonly used platforms in service-based industries. They support real-time response, hybrid deployments, and integration with popular SaaS systems like Salesforce and Zendesk—key for customer-facing applications.

6. Which generative AI tools are best for enterprise applications?

Enterprises prefer infrastructure with high governance, compliance, and security ratings. Based on G2 feedback, IBM Watsonx, Azure AI, and Google Cloud AI Platform are top performers in the Enterprise AI Infrastructure Grid. Reviewers frequently note strengths in role-based access, monitoring, and policy integration for large-scale AI deployments.

7. What are the best options for generative AI infrastructure in the SaaS industry?

G2 reviewers in the SaaS space consistently recommend Databricks, AWS Bedrock, and OpenAI API for modular infrastructure. These tools score high on integration flexibility, developer-friendliness, and CI/CD compatibility. Their popularity among product teams building intelligent SaaS features places them in the top-right quadrant of G2’s AI Infrastructure Grid.

8. What’s the best generative AI infrastructure for a tech startup?

Startups love tools that are quick to deploy and easy to scale. Hugging Face, Google Vertex AI, and Replicate receive high G2 marks for developer experience, transparent pricing, and community support. According to G2 reviews, these platforms balance performance and cost, making them ideal for early-stage companies building AI-first products.

9. What’s the best generative AI platform for app development?

Top-rated platforms on G2 for app development include OpenAI, Google Cloud’s PaLM API, and Azure AI Studio. These tools support RESTful APIs, fast inference speeds, and scalable deployment options across mobile and web apps. Developers rate them highly for documentation and SDK availability.

10. Which company offers the most reliable AI infrastructure tools?

Based on G2 scores and customer satisfaction ratings, Google Cloud, Microsoft Azure, and Amazon Web Services are seen as the most reliable AI infrastructure providers. They consistently receive high marks for uptime, customer support, and enterprise SLAs, which are critical for mission-critical AI workloads.

It's an AI-enabled future for tech

The demand for AI infrastructure will rise as more companies build and expand the deployment of AI systems in their operations. Presently, there are a few concerns related to costs and security. However, as technology improves, these concerns will likely turn into business opportunities for leaders to address. 

Businesses should continue to evaluate which company offers the most reliable AI infrastructure tools, examining performance benchmarks, integration flexibility, and operational resilience.

Learn more about how AI is influencing everything through these digital trends in 2025.