Edge AI Software | A $8.91B Market by 2030 | How R...

CHICAGO, June 25, 2025 /PRNewswire/ — The Edge AI Software Market is projected to be valued at USD 1.95 billion in 2024 and reach USD 8.91 billion by 2030, growing at a CAGR of 28.8% according to a new report by The Research Insights, poised for growth driven by an aging population worldwide. As demands grow for more reliable systems, edge AI software has become a cornerstone in critical applications, delivering speed and enhanced data control. The report runs an in-depth analysis of market trends, key players, and future opportunities. In general, the Edge AI Software Market growth of 28.8% comprises a vast array of Offering, Data Type, Vertical, and Geography which are expected to register strength during the coming years.

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Market Overview and Growth Trajectory:

Edge AI Software Market Growth: According to an exhaustive report by The Research Insights, the Edge AI Software Market is experiencing significant growth. Edge AI software is undergoing tremendous growth due to a unique confluence of factors reshaping how data is analysed and actioned at the edge. One factor is the proliferation of IoT devices, which generate enormous amounts of real-time data that must be processed and acted on at the edge to optimize performance and minimize reliance on cloud infrastructure. The need for data privacy and security in sectors such as healthcare and finance has led to the rise of edge AI software that enables real-time, on-device processing of sensitive data. Another factor is the increasing prevalence of edge AI in autonomous systems like drones, smart vehicles, and robots, where real-time decisions are essential. The market is also being fuelled by advancements in lightweight machine learning models and AI chipsets that enable powerful processing in hardware-constrained environments. The growing need for scalable, intelligent edge solutions that support predictive maintenance, surveillance, and energy optimization in emerging smart cities and industrial automation projects is also fuelling demand. Together, these factors are reshaping edge AI software from a niche innovation into a powerful technology for the next generation of intelligent systems.

Proliferation of IoT Devices and Edge Computing Infrastructure:
The proliferation of IoT devices in industries around the world is among the primary factors driving the Edge AI software market. Billions of IoT devices ranging from manufacturing sensors to smart cameras to wearables to autonomous vehicles generate large volumes of data at the edge, which must be processed in real time for applications like predictive maintenance, anomaly detection, and intelligent automation. Transmitting this data to centralized systems results in latency, bandwidth constraints, and privacy issues. Edge AI software empowers these devices to perform real-time processing of this data using embedded machine learning (ML) models to improve response times and operational efficiency. Industries that leverage edge computing for latency-sensitive applications like these will continue to drive demand for robust and scalable Edge AI software platforms.

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Demand for Real-Time, Low-Latency Decision-Making:
Modern applications increasingly demand near-instantaneous processing of data for critical decision-making, a capability that cloud-based AI is not always able to provide. For example, in industries such as healthcare, autonomous vehicles, retail, and smart cities, even milliseconds can mean the difference between success and failure. In autonomous vehicles, on-device AI inference is necessary for interpreting sensor data and making driving decisions within microseconds. Similarly, in retail, edge AI powers real-time analysis of customer behaviour, inventory tracking, and theft prevention. Edge AI software overcomes latency and connectivity challenges by enabling data processing on or near the data source. This allows for real-time analytics and rapid decision-making, which is essential in bandwidth-constrained or remote environments where consistent connectivity to the cloud may not be possible.

Advancements in AI Model Optimization and Hardware Acceleration:
Advancements in AI model compression techniques (such as quantization and pruning) and specialized hardware (edge GPUs, TPUs, and NPUs) have made it increasingly feasible to run complex AI algorithms on edge devices. Edge AI software platforms have adapted to support lightweight models that can run efficiently within the computational constraints of edge hardware. These platforms typically include tools for model optimization, device orchestration, and secure deployment. An ecosystem of AI accelerators and low-power computing devices such as NVIDIA Jetson, Google Coral, and Intel Movidius has also emerged, enabling a wider array of edge AI use cases. As the software adapts to take advantage of this powerful hardware, it further fuels adoption and opens new commercial and industrial opportunities for edge-based AI solutions.

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Geographical Insights:

North America held a significant share in the global market with a revenue share of more than 30.0% in 2023. The demand for edge AI solutions has increased in North America owing to the increased adoption of IoT and smart devices in the region. These smart devices demand real-time processing of data, but cloud communication for these devices is not practical. This has led to the demand for edge AI software, which processes the data at the device level and does not require the use of cloud. Also, due to the increased advancements in the field of AI and machine learning, advanced models can now be executed efficiently on edge devices with less computing power. Hence, the edge AI application spectrum is expanding in various industries.

On the other hand, the APAC edge AI software market is projected to witness a notable growth rate during the forecast period. Due to the increasing focus on smart city projects in the region, countries such as India, China, and Singapore have been investing in projects related to intelligent infrastructure, which requires real-time data processing from multiple sources, including traffic systems, energy grids, and surveillance. Smart cities can reduce energy consumption, reduce crime rates, and control traffic flow, thereby leading to a sustainable and efficient cityscape.

Global Edge AI Software Market Segmentation and Geographical Insights:

Based on Offering, the edge AI software market is divided into, Solutions, and Services. The solutions segment dominated the market in 2023, accounting for over 76.0% share of global revenue.
Based on Data Type, the edge AI software market is divided into, Audio Data, Mobile Data, Sensor Data, Biometric Data, Speech Recognition, Video and Image Recognition, and Others. The video and image recognition segment dominated revenue growth in 2023, driven by escalating concerns over security and surveillance.
Based on Vertical, the edge AI software market is divided into, BFSI, Government & Public Sector, Healthcare & Life Sciences, IT & Telecommunications, Energy & Utilities, Manufacturing, Automotive, and Others. The IT & telecommunications segment dominated the Edge AI software market in 2023, driven by several key factors.
The Edge AI Software Market is segmented into five major regions: North America, Europe, Asia Pacific, Latin America, and Middle East & Africa.

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Key Players and Competitive Landscape:

The Global Edge AI Software Market is characterized by the presence of several major players, including:

Amazon Web Services
Edge Impulse Inc.
Google
IBM Corporation
Intel Corporation
Kyndryl Inc.
Microsoft
NVIDIA Corporation
Qualcomm Technologies, Inc.
Siemens

These companies are adopting strategies such as new product launches, joint ventures, and geographical expansion to maintain their competitive edge in the market.

Global Edge AI Software Market Recent Developments and Innovations:

In April 2025, Sutherland and Google Cloud announced an expanded partnership to accelerate enterprise-wide adoption of generative AI. The two companies will work together to integrate Google Cloud’s Gemini models, Vertex AI, and Customer Engagement Suite with Sutherland’s applied AI capabilities to power the entire customer lifecycle, from strategy to operations and interactions.
In March 2025, IBM and NVIDIA teamed up to accelerate AI adoption with integration of NVIDIA’s AI and data platform technologies into IBM’s watsonx AI and data platform. The two companies will leverage NVIDIA’s GPUs, AI Enterprise software, and networking solutions to extend IBM’s AI inferencing, training, and enterprise AI.
In February 2025, Anduril and Microsoft partnered to accelerate the U.S. Army’s Integrated Visual Augmentation System (IVAS). Anduril will be responsible for production and development and will use Microsoft Azure as its preferred cloud for AI technology. Together, the companies will bring soldiers enhanced augmented reality, which can increase combat effectiveness and battlefield decision making by adding AI-enabled situational awareness.
In February 2025, Mercedes-Benz and Google Cloud announced an extended partnership to advance the MBUX Virtual Assistant with new conversational AI capabilities. Leveraging Google Cloud’s Automotive AI Agent powered by Gemini on Vertex AI, the assistant will offer more contextual and personalized answers to questions related to navigation and points of interest, powered by Google Maps Platform.

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Frequently Asked Questions (FAQs):

What is the forecasted market size of the Edge AI Software Market in 2030?
The forecasted market size of the Edge AI Software Market is USD 8.91 billion in 2030.
Who are the leading players in the Edge AI Software Market?
The key players in the Edge AI Software Market include, Amazon Web Services; Edge Impulse Inc.; Google; IBM Corporation; Intel Corporation; Kyndryl Inc.; Microsoft; NVIDIA Corporation; Qualcomm Technologies, Inc.; and Siemens
What are the major drivers for the Edge AI Software Market?
The proliferation of Internet of Things (IoT) devices, growing need for real-time decision-making, and advancements in artificial intelligence (AI) and machine learning (ML) are key contributors to this market growth.
Which is the largest region during the forecasted period in the Edge AI Software Market?
The North American market has emerged as a leader in the global AI software industry, boasting a significant revenue share of over 30.0%.
Which is the largest segment, by offering, during the forecasted period in the Edge AI Software Market?
The solutions segment dominated the market, accounting for over 76.0% share of global revenue.

Conclusion:

The Edge AI software market is rapidly expanding as more industries seek low-latency, real-time processing of data closer to its source. The convergence of artificial intelligence with edge computing empowers devices to perform sophisticated analysis and decision-making without relying on centralized cloud infrastructure. This is driving adoption in sectors such as automotive, manufacturing, retail, and smart cities where response times, bandwidth efficiency, and data privacy are crucial. The proliferation of IoT devices and the need for distributed intelligence are driving the development of scalable, lightweight AI models optimized for edge environments. Edge AI software is increasingly being used for applications such as predictive maintenance, autonomous vehicles, personalized in-store experiences, and intelligent surveillance. To maximize the benefits of edge intelligence, ensuring model accuracy, strong cybersecurity, and interoperability with existing systems is essential. As Edge AI matures, it plays a key role in enabling more resilient, adaptive, and intelligent systems aligned with the broader goals of digital transformation and Industry 4.0.

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The report from The Research Insights, therefore, provides several stakeholders— technology vendors, device manufacturers, cloud service providers, telecom operators, regulatory bodies, and end-user enterprises—with valuable insights into how to successfully navigate this evolving market landscape and unlock new opportunities.

With projected growth to US$ 8.91 billion by 2030, the Global Edge AI Software Market represents a significant opportunity for AI startups, academic researchers, open-source communities, independent developers, and niche software vendors, can position themselves for success in this dynamic and evolving market landscape.

Check out more related studies published by The Research Insights:

Edge AI Market: The Global Edge AI Market is expected to reach at USD 66.47 billion by 2030, according to a new report by The Research Insights. It is projected to expand at a CAGR of 21.7% during the forecast period. This rapid growth can be attributed to the increasing need for real-time data processing and analysis at the network’s edge, driven primarily by the demands of various industries such as healthcare, manufacturing, and telecommunications.
Edge AI Accelerators Market: The Global Edge AI Accelerators Market is expected to reach at USD 38,445.8 million by 2030, according to a new report by The Research Insights. It is projected to expand at a CAGR of 30.7% during the forecast period, driven primarily by the rising need for real-time data processing.
Edge Analytics Market – The global edge analytics market is poised to experience significant growth, with a projected value of USD 7.27 billion in 2022. The compound annual growth rate (CAGR) is anticipated to reach approximately 25.3% from 2023 to 2030. This surge can be attributed to the increasing proliferation of IoT devices, such as sensors and connected devices, which are generating massive amounts of data at the edge. As a result, organizations are leveraging edge analytics to process and analyze data closer to its source, enabling them to extract valuable insights and make faster decisions.
Edge Computing Market – The global edge computing market is poised to experience tremendous growth, with an anticipated valuation of USD 16.45 billion by the end of 2023. This rapid expansion is projected to occur at a remarkable compound annual growth rate (CAGR) of 36.9% from 2024 to 2030. Edge computing introduces a new layer of complexity for organizations, empowering multiple stakeholders to manage their IT infrastructures, networking, software development, traffic distribution, and service management.

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