Get Started
Java-native AI platform

Build, Train and Run High-Performance AI in Java.

Everything you need to develop and deploy machine learning applications – without leaving the JVM.

Deep Netts brings data preparation, model training and accelerated execution into one Java-native platform, so intelligent features live directly inside your existing applications.

Get Started Free See the Benchmarks
  • Open Source Edition
  • CPU & GPU Acceleration
  • Enterprise Ready
Deep Netts · Visual AI Builder
Deep Netts Visual AI Builder: visual workflow, network layers and live training charts
18 ms
VGGNet inference on GPU
400×
GPU vs pure-Java CPU

One Platform. From Data to Production AI.

Building AI shouldn't need multiple languages and disconnected tools. Deep Netts keeps the whole machine learning lifecycle inside the Java ecosystem.

  1. 01

    Explore & Prepare Data

    Load CSV and image datasets, split, normalize and inspect them with Java tools.

  2. 02

    Build & Train Models

    Design networks visually or with the fluent Java API. Train, evaluate, tune.

  3. 03

    Accelerate Execution

    Vector API on the CPU, CUDA on NVIDIA GPUs through the Panama FFM API.

  4. 04

    Deploy in Your Applications

    A trained model is a Java object – call it from your services like any other class.

High-performance AI

Accelerated by Modern Java

Deep Netts uses the newest Java platform features to run fast on both CPUs and GPUs – from the same Java code.

CPU Acceleration
Java Vector API
  • Vectorized (SIMD) computation
  • Multithreaded execution
  • Pure Java, no native runtime
  • Runs wherever the JVM runs
GPU Acceleration
CUDA via Panama FFM API
  • NVIDIA CUDA integration
  • No JNI glue code
  • GPU-accelerated inference
  • Switch with one setting
Measured, not promised
Up to400×

GPU acceleration, in pure Java.

VGGNet forward inference on Java 25, presented at Devoxx Belgium 2025. 100 measurements per implementation, NVIDIA GPU through the Panama FFM API.

Full Benchmark Watch the Devoxx Talk

VGGNet inference – average time per image

Deep Netts · GPUDeep Netts · CPUPyTorch (reference)

Lower is better · log scale · line = best to worst of 100 runs, dot = average. Hover a row, or focus the chart and use ↑ ↓.

400×

GPU faster than pure-Java CPU on the same model.

= JNI

Panama FFM API matches JCuda – no native glue code.

= PyTorch

Same 18 ms average as the Python industry standard.

›Statistics for 100 measurements
ImplementationBest (ms)Average (ms)Worst (ms)STD (ms)
Deep Netts CPU (pure Java, 16 threads, 6 cores)7,8928,20710,278347.29
Deep Netts GPU (Panama FFM API)1618210.9
Deep Netts GPU (JCuda (JNI))1718210.68
PyTorch (Python, reference)1718190.51
Two ways to build AI

Visual or Code. Your Choice.

Visual AI Builder

Develop models in a graphical environment – from dataset to trained network.

  • Import and prepare datasets
  • Design neural networks visually
  • Train and watch error and accuracy live
  • Analyze weights, metrics and confusion matrix
  • Export trained models for Java
Visual AI Builder: weight histograms and 3D weight analysis per layer

Deep Netts Java Library

Build, train and run neural networks directly in Java code.

  • Fluent Java API for networks and training
  • Feedforward and convolutional networks
  • Regression, classification, image recognition
  • CPU and GPU acceleration
  • Plugs into existing Java applications
ImageClassifier.java
ConvolutionalNetwork net = ConvolutionalNetwork.builder()
    .addInputLayer(32, 32, 3)
    .addConvolutionalLayer(12, Filters.ofSize(3))
    .addMaxPoolingLayer(2, 2)
    .addFullyConnectedLayer(64)
    .addOutputLayer(10, ActivationType.SOFTMAX)
    .lossFunction(LossType.CROSS_ENTROPY)
    .build();

net.getTrainer()
    .setOptimizer(OptimizerType.ADAM)
    .setStopEpochs(20);

net.train(trainingSet);
Built for real-world enterprise AI

Proven in Demanding Industries

Financial Services & Fraud Detection

Real-time transaction scoring and risk decisions inside the payment flow.

Learn more

Scientific Computing

Particle trajectories, materials and molecular research with neural networks.

Learn more

Enterprise Predictive Analytics

Classification, forecasting and anomaly detection for operational decisions.

Learn more

Telecom & Industrial Systems

Predictive monitoring and classification on streaming sensor and network data.

Learn more
AI that fits your existing architecture

Enterprise Ready. On Your Terms.

Java-Native

Your language, your tools, your infrastructure.

High Performance

CPU and GPU acceleration for demanding workloads.

Infrastructure Control

On-premises, private cloud or public cloud.

Simplified Integration

No separate Python AI service to run.

Enterprise Scalability

AI inside real-time and event-driven systems.

Open source to enterprise

An Edition for Every Need.

Compare editions

Open Source

Community Edition

The heart of Deep Netts, developed in the open. Clone it, read it, build with it.

Get Started on GitHub

Free Edition

For small production use

The full accelerated platform, free to run in one real production deployment while your product is small.

Free Download

Managed Service

Fully managed, on our cloud

Deep Netts run for you on our infrastructure – training and inference billed like cloud compute, by the hour. No license, no setup, no ops.

Contact Us

Enterprise Edition

On-premises, your infrastructure

Installed in your own environment under an enterprise license. Scale and support for companies whose product earns more than $100,000 a year.

Contact Sales

Bring High-Performance AI Into Your Java Applications.

From your first experiment to demanding enterprise workloads – Deep Netts runs AI where your Java applications already live.

Get Started Free Talk to Our Team