The Deep Learning Platform

Production-Grade Performance, Faster.

Simplify and accelerate the development of computer vision, Generative AI, and NLP applications with advanced tools to build, optimize, and deploy accurate and highly efficient models.

Gain Unparalleled
Performance in No Time

Achieve accuracy & runtime performance that outperform SoTA models for any use case and inference hardware.

Shorten Time to

Reach production faster with automated tools. No more endless iterations and dozens of different libraries.

Scale Inference
Cost Efficiently

Maximize hardware utilization to enable new use cases on resource-constrained devices or cut up to 80% of your cloud compute costs.

Build & Deploy Better DL Models, Faster

Deci Platform

Foundation or Custom Models

Choose an ultra performant model or generate a custom one.



Neural Architecture Search Engine

On Prem


Dataset Analyzer

Train or

Use Deci’s library & custom recipe to train on-prem.

On Prem


PyTorch Training Library

Optimize & Run

Apply acceleration techniques. 
Run self-hosted inference anywhere.

On Prem


Optimization & Inference Engine SDK

Here’s where Deci can help you

Efficient Foundation Models
Get direct access to enterprise-grade models generated with AutoNAC. Enhance performance, reduce risk, and cut development time from months to days.
0 %

Shorter development process. Go from data to production ready model in days.

0 %

Lower development costs per model on average.

0 X


0 X

Inference cost reduction


Why Deci?


The world's most efficient and cost effective foundation models.

Control, Quality & Customization

Gain a competitive edge through advanced model customizations.

Full Data

Self-hosted inference. No vendor lock-in. Ideal for enterprises and for handling sensitive data.

Easily Integrate with your existing MLOps Stack

Deep Learning Technical Resources

Deploy Efficient Models to Production with Deci’s Deep Learning Development Platform

Add Your Heading Text Here
					from transformers import AutoFeatureExtractor, AutoModelForImageClassification

extractor = AutoFeatureExtractor.from_pretrained("microsoft/resnet-50")

model = AutoModelForImageClassification.from_pretrained("microsoft/resnet-50")