Unlocking the Power of Graph AI for Developers, Researchers, and Startups
Graph databases have come a long way from powering social networks and recommendation systems to enabling real-time fraud detection, anti-monitoring, entity resolution, customer 360, supply chain optimization, and AI-driven decision-making. Today, with the rapid evolution of AI and retrieval-augmented generation (RAG) models, traditional databases are no longer enough. Developers need hybrid capabilities that combine graph traversal, vector search, and real-time analytics into a single system.
This is especially true for collaborative Agentic AI, where AI agents continuously learn, adapt and make informed decisions in real time. Multi-modal databases, combining graph and vector search, are critical for structuring knowledge, retrieving relevant information, and optimizing workflows dynamically.
Despite this need, most free graph databases limit their capabilities, offering only basic traversal features, low CPU limits, restricted storage, and no vector search. Developers, AI researchers, and data scientists are left searching for alternatives that provide true AI-driven graph analytics.
That’s why we’re introducing TigerGraph DB Community Edition, a fully featured, AI-ready multi-modal database that supports both structured graph analysis and unstructured vector search. It is free, allows full production use, and provides the most advanced hybrid search experience available today.
Why TigerGraph DB Community Edition Stands Out? Why Now?
Most free graph databases have strict limitations, making it difficult for AI developers and data scientists to build production-ready applications. TigerGraph DB Community Edition removes these restrictions and offers unmatched flexibility.
- More compute power – Competing free graph databases only offer 2-4 CPUs, but TigerGraph DB Community Edition provides 16 CPUs, enabling high-performance analytics, graph traversal, and AI workloads.
- Higher storage limits – Many free databases restrict users to small datasets, but TigerGraph DB Community Edition supports 200GB of graph storage and 100GB of vector storage, allowing developers to handle large-scale AI applications.
- Fully integrated graph+vector hybrid search – Other graph databases lack native vector support, forcing users to connect to external vector databases. TigerGraph provides seamless hybrid graph + vector search, making it the ideal database for AI applications.
- Multi-query language support – Developers can use GSQL, OpenCypher, and ISO GQL, allowing easy adoption without rewriting queries.
- Built for the rise of Agentic AI – AI systems are becoming autonomous, self-improving, and capable of orchestrating complex multi-agent workflows. TigerGraph Turing complete graph query language provides the ideal infrastructure for Agentic AI by enabling task dependency management, structured knowledge storage, and dynamic reasoning through hybrid search.
As AI shifts from passive models to active agents, databases must evolve to support reasoning, collaboration, and workflow orchestration. TigerGraph’s Graph + Vector Hybrid search allows AI agents to retrieve relevant information, optimize task execution, and dynamically adjust workflows – capabilities that are critical for the next generation of AI applications.
Production-ready without licensing barriers – Unlike other free graph databases that impose non-commercial restrictions, TigerGraph DB Community Edition is fully production-ready for real-world deployments.
AI developers, data scientists, and startups no longer need to choose between performance and affordability. This is the most advanced free graph database available today, designed to help teams build next-generation AI-powered applications at scale.
Who Should Use TigerGraph DB Community Edition?
TigerGraph DB Community Edition is designed for a broad range of users who need a powerful graph database with AI and vector search capabilities.
AI Developers & ML Engineers
- Build hybrid graph+vector AI models with retrieval-augmented generation (RAG), recommendation engines, and real-time anomaly detection.
- Use TigerGraph’s integrated vector search to improve AI model accuracy and search efficiency.
- Leverage multi-hop reasoning and hybrid queries to retrieve structured and unstructured data in real-time.
Data Scientists & Analysts
- Perform deep graph analytics to uncover hidden relationships in large datasets.
- Integrate structured knowledge graphs with unstructured vector embeddings for enhanced predictive modeling.
- Work with graph algorithms and ML models to improve fraud detection, recommendation systems, and entity resolution.
Startup Founders & Innovators
- Develop scalable AI-powered applications without the financial burden of enterprise licenses.
- Deploy production-ready AI solutions with 200GB of graph storage, 100GB of vector storage, and 16 CPUs.
- Quickly ingest and analyze large datasets from multiple sources, including data lakes, transactional systems, and real-time event streams.
Academic & Research Teams
- Experiment with advanced graph AI techniques, combining structured and unstructured data for deep learning applications.
- Work with cutting-edge retrieval-augmented generation (RAG) techniques for LLM research.
- Access a free, high-performance graph+vector database to support AI, NLP, and complex network analysis.
TigerGraph + Iceberg: Bringing Graph AI to Data Lakes
TigerGraph Community Edition now includes native integration with Apache Iceberg, making it easier to connect graph-powered AI with massive datasets stored in data lakes. Businesses leveraging Iceberg and Spark can now perform real-time analytics on structured and semi-structured data without costly ETL transformations.
With the TigerGraph Spark Connector, users can load data from Iceberg tables directly into TigerGraph’s graph schema. This integration enables advanced AI workflows, where historical transaction data stored in Iceberg can be combined with real-time graph analysis to detect fraud, predict customer behavior, and optimize search recommendations.
To set up Iceberg ingestion:
- Create Iceberg tables corresponding to TigerGraph’s schema.
- Use Spark SQL to load structured data into Iceberg tables.
- Connect TigerGraph to Spark and load data into the graph database.
TigerGraph’s Iceberg integration is ideal for financial fraud detection, risk analytics, and large-scale customer intelligence, where historical transaction data in Iceberg can be combined with real-time graph analysis to detect anomalies faster.
What Can You Build with TigerGraph DB Community Edition?
TigerGraph DB Community Edition provides a multi-modal approach to AI, combining structured graph data with vector search to unlock powerful real-world applications.
AI-powered knowledge graphs
- Build a real-time retrieval-augmented generation (RAG) pipeline for LLMs and generative AI models.
- Enhance AI applications by enriching vector search with structured graph insights.
Personalized search & recommendations
- Implement multi-modal search using both graph traversal and vector similarity.
- Power context-aware search that understands relationships between people, products, and events.
Fraud detection & risk scoring
- Detect fraudulent financial transactions by integrating real-time transactional data with historical risk scores.
- Use graph-based anomaly detection to uncover hidden fraud networks.
Data lake analytics with Iceberg
- Use Spark + TigerGraph to query and analyze massive datasets in a distributed, AI-ready environment.
- Combine structured enterprise data with real-time graph analytics for faster insights.
For a deeper comparison of how TigerGraph’s hybrid graph+vector capabilities surpass traditional graph solutions, check out our blog post here [link to be added].
Getting Started with TigerGraph DB Community Edition
Follow these steps to get up and running with TigerGraph Community Edition.
- Download the TigerGraph Docker Image
Visit our product download page, navigate to the Community Edition section, and request a download link. - Follow the instructions mentioned in Getting Started with Docker
- Follow this quick start guide to use:
Developers can use GSQL Shell to execute queries or GraphStudio to create and visualize data models.
What’s Next?
Once you have set up TigerGraph DB Community Edition, here’s what you can explore next:
- Write your first GSQL query – Learn how to run graph queries
- Try out vector search – Run your first hybrid graph+vector search query to see TigerGraph’s AI capabilities in action.
- Connect to an Iceberg data lake – Load real-world datasets from Iceberg into TigerGraph for large-scale AI analytics.
- Join the TigerGraph Community – Share your experiences, ask questions, and collaborate with fellow developers.
Get Started Today
The best AI applications don’t just retrieve information, they understand relationships.
TigerGraph DB Community Edition is the first free database that lets you build graph-powered AI applications without compromise.
Download TigerGraph DB Community Edition and Start Building AI Applications Today.
