Editions

TigerGraph ML Workbench comes in two flavors - Developer Edition and Enterprise Edition. While both editions offer the same useful Python-level features for your data science needs, the Enterprise edition offers more powerful features to support production-level model training on enterprise-level data sets.

The tables below can help you compare the key differences between the two editions.

Feature Availability

Developer Enterprise

Compatibility

  • TigerGraph Database

  • Amazon SageMaker, Azure ML, GCP Vertex

  • PyG, DGL ML Framework

  • TigerGraph Database

  • Amazon SageMaker, Azure ML, GCP Vertex

  • PyG, DGL ML Framework

Onboarding

  • ML Workbench Docker Images

  • MacOS and Linux Installers

  • pip install & conda install

  • ML Workbench Docker Images

  • MacOS and Linux Installers

  • pip install & conda install

Capabilities

Python-level capabilities with pyTigerGraph:

  • Graph data partitioning

  • Graph Data Loading & Export (HTTP)

  • Subgraph sampling

  • Data Batching

  • Graph feature generation

  • GNN: Homogeneous Graph Support

  • GNN: Node Prediction support

  • GNN: Heterogeneous Graph Support

  • GNN: Link Prediction Support

  • GNN Inference with real-time data

Python-level capabilities with pyTigerGraph:

  • Graph data partitioning

  • Graph Data Loading & Export (HTTP & Kafka)

  • Subgraph sampling

  • Data Batching

  • Graph feature generation

  • GNN: Homogeneous Graph Support

  • GNN: Node Prediction support

  • GNN: Heterogeneous Graph Support

  • GNN: Link Prediction Support

  • GNN Inference with real-time data

Data Export Method

HTTP only

Reliable and efficient data export via both HTTP and Kafka

Data Export Size

Limited to 2GB

Unlimited

Parallel Training

No

Yes

Support

Community support

  • 10 hr Professional Service / Consulting on Solution Building

  • Standard support SLA with 12 x 5

Customer Scenarios

Developer Edition Enterprise Edition

Purpose

  • Learning Graph Databases

  • Learning Graph Data Science

  • Building proofs of concept

Production deployment of Graph ML Models

Audience

  • Students

  • Researchers

  • ML Practitioners

Enterprise data science teams

Infrastructure Readiness

Local machines / Internal ML infra (self-managed)

Local machines / Internal ML Infra (self-managed)

Data Size

Small data set (<2GB)

Production / Enterprise level data

Performance requirements

  • Low requirements for training time

  • Low requirements for model accuracy / predictability

  • High requirements for training time (near real time)

  • High requirements for model accuracy / predictability