Deeplearning4j vs Lightning AI Studios
Deeplearning4j
deeplearning4j.konduit.aiLightning AI Studios
lightning.aiSide by side
| Figure | Deeplearning4j | Lightning AI Studios |
|---|---|---|
| Price from | Free | $20/mo |
| Free plan | Yes | Yes |
| Free trial | — | Yes |
| Open source | — | — |
| Web | — | Yes |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 9 |
| Spec score · Deep Learning Software | 8.7 (#4) | 9.1 (#1) |
| Purpose | Eclipse Deeplearning4j is an open-source, distributed deep learning framework for the JVM. | AI Studio is a persistent, collaborative GPU cloud workspace for training models, running inference, building agents and AI apps, and coding together. |
| Support | The support page lists GitHub issues, community forums, Stack Overflow, and professional support from Konduit. | Lightning lists email, Discord, and Twitter as support channels, and a dedicated Slack channel for enterprise support. |
| What it does | Eclipse Deeplearning4j is an open-source deep learning framework for the JVM, for building, training, and deploying neural networks in Java and Scala. | AI Studio is a persistent collaborative GPU workspace for training, inference, coding, agents and AI applications. |
Plans
Lightning AI Studios
- FreeFree
- FreeFree
- Pro$20 a month
- Pro (annual)$20 a month
- Pro (monthly)$50 a month
- Teams$119 a month
- Teams (annual)$119 a month
- Teams (monthly)$140 a month
- EnterprisePriced on request
In their own words
Deeplearning4j
Deeplearning4j is an open-source framework for building, training, and deploying neural networks on the JVM, using Java or Scala. It can compute on CPUs through OpenBLAS and oneDNN, or use CUDA for GPU acceleration. For distributed training, it supports Apache Spark across CPU or GPU clusters. The framework can import models from Keras, TensorFlow, and ONNX, while Python4J provides Python interoperability from Java through CPython embedding. Its wider ecosystem includes ND4J, SameDiff, DataVec, Keras Import, Python4J, OmniHub, and Arbiter. Documented deployment targets include JVM microservices, mobile devices, IoT, and Spark environments. The libraries are licensed under Apache License 2.0 and governed by the Eclipse Foundation. The quickstart recommends Maven for Java projects, with Ivy and Gradle also workable. Documentation covers version 1.0.0-M2.1. The free plan is listed at free. Support options include GitHub issues, community forums, Stack Overflow, and professional support from Konduit.
Lightning AI Studios
Lightning AI Studios is a cloud workspace for training models, running inference, building agents and AI apps, and coding collaboratively. Its persistent environments save installed packages, custom Python versions, and downloaded files. Studios offer browser-accessible VS Code and JupyterLab, plus AI copilots for AI and machine-learning work, including CUDA and PyTorch debugging. Teams can collaborate in real time on private projects, use a full-stack runtime, and deploy full-stack applications. Users can connect S3 buckets and EFS volumes through Lightning Drive, import models from Hugging Face and GitHub, or connect local IDEs such as VS Code, Cursor, and PyCharm over SSH. An SDK supports workflow automation. The Free plan is free and includes one active Studio, 50 GB persistent storage, and up to two concurrent GPUs; that Studio requires a restart every four hours. Pro and Teams plans have higher storage and GPU limits. Enterprise offers deployment in a customer VPC using AWS or GCP cloud credits.