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High-Performance AI for Enterprise Java.

Run AI directly inside your Java applications – accelerated on modern CPUs with the Vector API and on GPUs for demanding workloads.

  • Faster AI decisions
  • Built for the JVM
  • Less architectural complexity
  • Enterprise infrastructure control
View Benchmarks

AI EXECUTION LAYER FOR THE JVM

Java Application
Deep Netts
Vector API
CPU Acceleration
GPU Acceleration
CUDA
AI Result

AI doesn't have to sit outside your Java stack.

Typical AI architecture

Java Application
API / Network
AI Service
AI Runtime
Model

Result back to Java

  • More components to build and operate
  • Higher latency and data movement
  • Another runtime, another stack

With Deep Netts

Java Application
Deep Netts
AI Execution Layer
CPU / GPU
Result
  • AI runs inside your Java application
  • Lower latency, higher throughput
  • One stack. One team. Less complexity.

Performance you can measure.

Up to400×

GPU acceleration in Deep Netts VGGNet benchmark.

See Benchmark ResultsView Methodology

VGGNet Inference Benchmark (Java 21)

CPU (Vector API)GPU (CUDA)
Execution time (s)
12.74
Throughput (img/s)
7.85
3,130.00
≈400×
Faster on GPU

Tests run on NVIDIA A100 GPU

Use Case

Fraud Detection at Transaction Speed.

Score transactions in real time with ML models running inside your Java infrastructure.

  • Real-time scoring inside the transaction flow
  • Low latency with minimal system hops
  • High throughput for peak transaction volumes
  • Works with existing Java payment systems
  • Data stays in your controlled environment
Explore Fraud Detection
Transaction #893271
Amount
€1,240.00
Merchant
Retail Store
Location
New York, US
Device
Mobile iOS
Time
10:21:45
Transaction received
Deep Netts
Fraud Model
Vector API
or
GPU Acceleration
Risk Score
0.87
High Risk
Decision
Authenticate
Review
Decline

AI decision in milliseconds.

Modern Java. Modern AI execution.

Java Native

Build and run AI models using natural Java APIs. No separate runtime.

Vector API

Leverage SIMD instructions on modern CPUs for fast, efficient inference.

GPU Acceleration

Access CUDA, cuBLAS, cuDNN from Java via the FFM API for maximum performance.

Developer Experience

Maven, Gradle, IDEs, familiar tooling. Stay in the Java ecosystem you know.

Designed for enterprise Java infrastructure.

Transactions / Events
APIs
Databases
Streams
Kafka / Messaging / Event Bus
Java Applications (Business Logic)
Deep Netts
AI Execution Layer
Vector API
CPU Acceleration
GPU Acceleration
CUDA
Real-time AI Decision
  • Deploy anywhere
    On-premises, private cloud or public cloud.
  • Container ready
    Docker, Kubernetes, CPU or GPU.
  • Scalable by design
    High throughput, low latency, built for scale.
  • Security & control
    Data stays within your infrastructure and governance.
Explore Architecture

Proven in the real world.

Up to 400× GPU Acceleration

Deep Netts VGGNet benchmark using modern Java and CUDA.

View Benchmark

Jefferson Lab

AI accelerated scientific data processing on demanding real-world workloads.

Read Case Study

Java Standards

Reference implementation of the Java Standard API for Machine Learning (JSR 381).

Learn More

Trusted by innovators

Engineers and organizations building mission-critical systems with Java and AI.

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Bring high-performance AI into your Java architecture.

Evaluate Deep Netts on your workload. See what Java-native AI can do.

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