AutoNAC

AutoNAC, the Neural Architecture Search engine at the core of the Deci AI platform, is an AI-driven tool that automatically generates efficient deep learning models tailored to specific performance targets and hardware constraints. Launched in March 2025, the platform is designed for AI professionals and enterprises—particularly those in automotive, smart retail, public sector, smart manufacturing, and academic research—who need to scale AI initiatives without proportionally increasing cloud infrastructure costs. It addresses the fundamental challenge of balancing high accuracy with fast inference speeds and manageable compute costs, making it relevant for teams building real-time applications like autonomous vehicle perception systems, video analytics for public safety, and predictive maintenance in manufacturing. The platform fits within broader AI agent and automation workflows, providing the underlying model efficiency required for production deployment at scale.

The Deci platform includes five integrated components: AutoNAC (Neural Architecture Search Engine), SuperGradients™ PyTorch Training Library, Infery Optimization & Inference Engine SDK, DataGradients™ Dataset Analyzer, and flexible deployment options for on-premise, cloud, or edge environments. AutoNAC uses AI to search for and generate optimal model architectures given a task and hardware constraint, a form of neural architecture search that automates what would otherwise be manual trial-and-error. SuperGradients is a comprehensive PyTorch library for training and fine-tuning, designed to reduce development time. Infery applies proprietary acceleration techniques to enhance inference performance, while DataGradients analyzes dataset characteristics to improve training efficiency. According to the company, the platform can increase deep learning model inference performance by 3x to 15x while preserving accuracy, and reduce compute costs by up to 80%.

In the competitive landscape of AI model optimization, AutoNAC positions itself against other neural architecture search tools and model acceleration platforms, though specific named competitors are not provided in the sources. The platform's emphasis on balancing accuracy with speed and cost targets enterprises that are moving from prototype to production, particularly in computer vision and generative AI. The Deci AI platform is built to compete with general-purpose deep learning frameworks and optimization tools by offering an end-to-end solution that covers the entire lifecycle from architecture search to deployment. However, the market also includes alternatives like gradient boosting methods (XGBoost, LightGBM, CatBoost) for small tabular datasets, which typically outperform deep learning architectures in those specific scenarios.

The honest trade-offs with AutoNAC include its pricing model—contact for pricing, with a reported launch price starting at $3,000, which may be prohibitive for individual developers or small startups. The platform's focus on deep learning means it is not suitable for traditional machine learning tasks like small tabular data, where gradient boosting methods are more effective. Additionally, the platform requires a certain level of expertise in PyTorch and deep learning workflows, potentially limiting adoption by teams without dedicated ML engineers. Finally, while the platform supports flexible deployment, the actual performance gains (3x-15x) depend heavily on the specific model architecture and hardware, meaning results may vary significantly across use cases.

Contact for Pricing; a reported launch price starts at $3,000 per the VentureBeat source.