The neural architecture search (NAS) market is expected to show steady growth in the forecasted timeframe.
The market for neural architecture search (NAS) is growing rapidly, as companies are increasingly looking for a faster and more efficient way to design deep learning models. With the demand for optimised neural network models on the rise across all sectors like healthcare, autonomous driving, and edge AI, NAS is quickly establishing itself as a key enabler of next-generation AI development. In addition to its ability to effectively design models faster while improving model performance and cost of computing, NAS has drawn strong interest from investors. The growth of the NAS market is supplemented by advancements in one-shot and hardware-aware NAS, mitigating constraints of real-world deployment. As the demand for AI continues to scale, NAS is established to be a leading enabler of scalable AI development.
The neural architecture search (NAS) market is segmented by:
By Type: The NAS market is segmented by type into automated NAS platforms, customised NAS solutions, open-source NAS frameworks, one-shot NAS tools, and hardware-aware NAS. Open-source NAS tools provide researchers and developers with a widely adopted way of consuming NAS due to their customisation and flexibility. Structures such as auto-Keras, Microsoft NNI, and DARTS allow users the flexibility of modifying search spaces and optimization methods without having to depend on proprietary solutions. Open-source NAS tools allow users the ability to use various search strategies and are continuously updated with community engagement that facilitates rapid innovation. Open-source solutions have also been the basis for supporting groundbreaking work in academia and industry, especially when integrating NAS to support new domains such as speech-based recognition, NLP, and edge devices. The transparency and flexibility of open-source solutions appeal especially to research institutions as well as academic-based AI startups.
By Deployment: The NAS market is segmented into cloud and on-premises.
By Application: The NAS market is segmented by application into computer vision, natural language processing (NLP), speech recognition, generative models (GANS), and graph neural networks (GNNS). Computer vision is one of the earliest and most mature application areas of NAS. Notably, NASNet, EfficientNet, and AmoebaNet were created using NAS to automate and optimise image classification architectures. The NAS-generated models also offer greater or equal accuracy and efficiency over (manually designed) counterparts. For example, in the field of real-world example deployment, such as autonomous vehicles, security surveillance, and medical imaging, the improvements or enhancements to performance that NAS offers while lowering (and increasing efficiency) latency and computational cost show continued promise. The vision-based NAS field has also expanded to object detection and image segmentation, where multi-objective optimisation, such as accuracy vs inference time, is important.
By End-User: The NAS market is segmented by end-user into IT & telecom, automotive, healthcare, manufacturing, and academia & research. In healthcare, NAS is used for designing efficient computational models for medical imaging, genomics, and disease prediction. For example, in radiology, NAS is used to automate the generation of CNN architectures for classifying anomalies in an MRI or a CT scan image database with very high prediction accuracy. Its use offers improvements over the higher demands of diagnostic quality in less time for model training and tuning. In addition, NAS can be used to create edge-computing models for portable devices or real-time models suitable for telehealth medicine delivery. Toward this goal, the precision, reproducibility, and optimisation flexibility provided by NAS provide many deployable options to meet the demands of healthcare AI systems as data and accuracy needs increase.
Region: Geographically, the market is expanding at varying rates depending on the location. North America is fortunate to have both industry experts, such as Google, Microsoft, and Facebook, as well as academia, driving development to incubate innovation and deploy at scale.
1. Rise of Zero-Shot NAS
Zero-shot NAS is fast becoming a game-changing trend because it employs existing learned predictors or proxies to estimate the model performance, eliminating the demand for costly training of the architectures while enabling drastic reductions in search time and computation. This, in turn, further indicates that NAS does not have to be a costly endeavour, especially for low-resource areas like edge devices.
Hardware-Aware NAS for Edge AI: With AI being drawn out of the cloud and demand for AI continuing to rise on smartphones, drones, and IoT devices, NAS can begin to tailor architectures against specific hardware constraints, e.g., latency, power, memory, and so forth. Hardware-aware NAS achieves architectures that both perform well and can be feasibly deployed on edge and embedded systems.
Drivers:
Increasing Demand for Automated Model Design: With the growing adoption of AI across industries, there is also a growing demand for automation in the design of high-performing neural networks. NAS makes this possible, as it automates a significant, manual, and time-consuming task in AI development algorithm engineering. With the emergence of NAS, both experts and non-experts can now design optimised models that outperform expertly hand-crafted alternatives. As the development of AI is democratised, R&D timelines are shortened, and barriers to entry are lowered for startups and enterprises alike. It is no surprise that tech giants like Google (AutoML) and Microsoft (NNI) are investing heavily in this area, which speaks to the commercial viability and scalability of NAS.
Growing Importance of Edge AI and Hardware Optimisation: As the demand for edge computing increases, so does the need for edge AI models to be accurate, lightweight and energy efficient. NAS can generate hardware-aware architectures that can help optimise models based on constraints stemming from latency, memory, or power. These constraints are especially important for various sectors, e.g,. autonomous vehicles, mobile devices, and wearables, where limitations are paramount. NAS provides the ability to deploy real-time on-device AI without compromising accuracy. The innovation of research, e.g., Once-for-All NAS and FBNet, demonstrates that hardware-adaptive NAS is becoming a pivotal component of scalable AI solutions for edge environments.
Challenges:
High Computational Cost: NAS can require extreme amounts of GPU hours, especially when using traditional methods like reinforcement learning. This effectively makes NAS an unreachable tool for organisations that do not have a large-scale computing infrastructure. Certain methods may easily rack up thousands of GPU hours.
Lack of Standardised Benchmarks: One issue with NAS is the difficulty in comparing competing NAS algorithm development due to differing evaluation metrics. NAS has inconsistent search spaces, making it impossible to reproduce the same architectures, as well as very varied performance measurements in papers, making the assessment of the same NAS algorithm across studies very difficult.
North America: In North America, currently, most applications of NAS (e.g. Healthcare, autonomous driving, cloud computing, etc) can be found in Canada and the USA. However, in jurisdictions without NAS frameworks like the UK or significant AI research hubs, it will likely take time for technology to reach the same point. North America is fortunate to have both industry experts, such as Google, Microsoft, and Facebook, as well as academia, driving development to incubate innovation and deploy at scale.
The neural architecture search (NAS) market is competitive, with a mix of established players and specialised innovators driving its growth.
September 2025: AutoML Conference organized the NAS Unseen Data Competition 2025, promoting development of advanced Neural Architecture Search algorithms capable of designing high-performance neural networks for previously unseen datasets.
September 2025: Researchers proposed CoLLM-NAS, a collaborative large-language-model-driven neural architecture search framework combining multiple LLM agents to guide architecture discovery and significantly improve NAS efficiency and model performance.
May 2025: Researchers introduced SEAL, a new NAS-driven framework designed for incremental learning environments, enabling dynamic neural network expansion while maintaining stability and improving accuracy across sequential machine-learning tasks.
| Report Metric | Details |
|---|---|
| Forecast Unit | Billion |
| Study Period | 2021 to 2031 |
| Historical Data | 2021 to 2024 |
| Base Year | 2025 |
| Forecast Period | 2026 – 2031 |
| Segmentation | Type, Deployment, Application, End-User |
| Geographical Segmentation | North America, South America, Europe, Middle East and Africa, Asia Pacific |
| Companies |
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