Deci AI is a deep learning deployment tool. It uses deep learning technology to ensure the creation of AI tools. This means you can use it to build, optimize, and deploy AI models for different environments. The good news is that it works with mobile, edge, and cloud platforms.�
Deci AI uses AutoNAC technology to ensure that the hardware works at its maximum potential. In addition, it will improve the inference performance. The best thing about Deci AI is that it�s now handled by NVIDIA.
Performance Score
A+
Deployment Quality
Reliable and scalable deployment of deep learning on hardware
Interface
Intuitive and beginner-friendly
AI Technology
AutoNAC, Neural architecture search
Purpose of Tool
Build and implement deep learning techniques on different types of hardware
Compatibility
Web-based Interface
Pricing
Two paid plans are available
Who is Using Deci AI?
- AI Researchers and Scientists: They can speed up the research process by enabling faster experimentation and iteration. Also, they can find innovative and efficient model designs.
- Data Scientists and Machine Learning Engineers can enhance model accuracy and efficiency, which leads to better results. This technology also promises to deploy models to various hardware platforms, reducing time to market.
- AI Startups and Small Businesses can use efficient optimization techniques to reduce computational costs, enabling them to launch AI apps faster.
AI Model Building
Low Computing Costs
Optimization & Fine-Tuning
Algorithmic Optimization Engine
Multi-Environment Compatibility
Maximum Hardware Utilization
Short Development Cycles
Better Inference Performance
Is Deci AI Free?
No, you cannot use Deci AI for free. There are two paid plans available, depending on the technology you use. Both of them are annual plans and cost $49,000 and $300,000.
Deci Optimization Basic
- Costs $49,000 a year�
- Runtime optimization�
- Computer vision and deep learning models
AutoNAC
- Costs $300,000 a year�
- Custom deep learning model
Deci AI Pros & Cons
Faster inference on cloud and edge devices.
Increase the production speed.
Maximize the use of hardware.
Simplifies the development of NLP and computer vision models.
Generates NAS algorithms for scalable solutions.
