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The global embedded software market size was valued at USD 20.7 billion in 2024 and is projected to grow at a CAGR of 9.6% between 2025 and 2034. Embedded software is essential in IoT applications as it manages device operations, controls sensors, supports connectivity, and ensures data security.
For instance, in smart homes, devices like thermostats, security cameras, and lights use embedded systems to function independently and communicate with other smart devices through cloud platforms or local networks. Similarly, in industrial IoT (IIoT), embedded systems are used to operate machines, collect sensor data, and support predictive maintenance, all of which require reliable software and real-time decision-making.
Report Attribute | Details |
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Base Year: | 2024 |
Embedded Software Market Size in 2024: | USD 20.7 Billion |
Forecast Period: | 2025 – 2034 |
Forecast Period 2025 – 2034 CAGR: | 9.6% |
2025 – 2034 Value Projection: | USD 50.5 Billion |
Historical Data for: | 2021 – 2024 |
No. of Pages: | 180 |
Tables, Charts & Figures: | 200 |
Segments covered: | Operating Systems, Functions, Application, Development Approach |
Growth Drivers: |
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Pitfalls & Challenges: |
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For instance, in October 2024, Analog Devices introduced CodeFusion Studio, a comprehensive embedded software development environment designed to streamline the development process for intelligent edge applications. This new platform is built on Microsoft’s open-source Visual Studio Code and represents Analog Devices' first integrated suite of software and security solutions.
Similarly, embedded systems are essential in industrial automation, as they control machinery, manage processes, and collect data from sensors to support decision-making. For example, in a smart factory, embedded software operates robotic arms, automated guided vehicles (AGVs), and machine tools. These systems use embedded software for real-time monitoring and decision-making, ensuring efficient and seamless operations. Additionally, embedded systems are crucial for predictive maintenance, where sensor data from machines is analyzed to predict potential failures, reducing downtime and improving productivity.