Edge AI Software Market to Reach USD 8.2 Billion by 2030, Driven by 5G and IoT Expansion

The global Edge AI Software Market is projected to grow significantly in the coming years, generating an estimated USD 1,459.0 million in revenue in 2024 and reaching USD 8,218.0 million by 2030, with a robust CAGR of 33.4% during 2024–2030. This impressive expansion is primarily driven by the rapid adoption of 5G networks, increasing deployment of IoT devices, and rising demand for real-time data processing in various industry verticals and applications.

Businesses across sectors are increasingly adopting edge computing and AI technologies to optimize operational workflows and harness actionable insights from edge-generated data. The integration of edge AI with cloud computing further strengthens this trend by enabling efficient processing closer to data sources, reducing latency, and enhancing decision-making—particularly in areas such as autonomous vehicles, industrial automation, and smart cities.

Moreover, the proliferation of e-commerce, social media platforms, and cloud-based edge ecosystems has expanded the market’s opportunities, creating a fertile environment for innovation and adoption of edge AI solutions globally.

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Key Insights

• The Edge AI Software Market exhibits strong growth potential, with total revenues expected to increase more than fivefold from 2024 to 2030, underpinned by major technological trends and widespread digital transformation initiatives across industries.
• 5G network adoption is a critical trend driving market expansion by enhancing connectivity, bandwidth, and real-time communication among edge devices—supporting up to 1 million devices per square kilometer and enabling more frequent AI model updates.
• The IoT ecosystem’s expansion is a primary market driver, with the number of connected devices forecast to rise from approximately 15.9 billion in 2023 to over 32.1 billion by 2030, increasing edge data volumes that need rapid, localized AI processing.
• Data quality management remains a notable challenge due to issues like inconsistency, limited storage on edge devices, synchronization complexities, and bandwidth constraints, all of which can affect AI model performance.
• In terms of components, the solutions segment is the largest revenue contributor in 2024, accounting for around 60% of market share, driven by foundational software like AI algorithms, platforms, SDKs, and application-specific edge solutions.
• The services category is projected to grow at a faster pace (35% CAGR) through 2030, reflecting demand for customization, optimization, and continuous support for edge AI deployments.
• Cloud-based deployment dominates the market with an approximate 70% share in 2024, benefiting from scalability, flexibility, and strong computational capabilities to manage edge-generated data.
• Among data types, video and image analytics lead market share (about 40% in 2024) due to their prevalence in security, automotive, and retail applications requiring real-time visual insights.
• Manufacturing end users are expected to grow rapidly, supported by Industry 4.0 initiatives such as predictive maintenance and process optimization, while healthcare also shows strong CAGR due to the rise of AI-enabled diagnostics and monitoring applications.
• North America holds the largest regional market share in 2024 due to mature technology infrastructure, early AI adoption, and strong 5G rollouts, whereas Asia-Pacific is forecast to grow fastest, driven by industrial modernization and IoT adoption.
• The fragmented market landscape reflects participation from a diverse set of technology providers including Microsoft, IBM, NVIDIA, AWS, Edge Impulse, Hewlett Packard Enterprise, Intel, Oracle, and others introducing innovations and strategic partnerships to capture market share.
• Recent industry developments include global partnerships and AI portfolio expansions by key players like Advantech and NVIDIA, Hewlett Packard Enterprise’s launch of GenAI-native solutions, and collaborative initiatives between Akamai Technologies and Neural Magic to accelerate deep learning at the edge.