Sr. Data Scientist at a computer software company with 1,001-5,000 employees
Real User
Top 10
Jun 27, 2026
The learning curve for new users getting started with TigerGraph can be somewhat challenging. Understanding tree diagrams and graph algorithms is necessary, so it could be tough initially. TigerGraph handles real-time analytics and streaming data well, but you will need additional add-ons for that, such as streaming add-ons including Pub/Sub. The documentation and training resources for TigerGraph are phenomenal and incredibly well done on their site. My advice to others looking into using TigerGraph is to consider the strong support and tutorials available for learning it. For data scientists and engineers, I would recommend looking into TigerGraph alongside other major players such as Neo4j to make a fair comparison, as TigerGraph provides excellent support and licensing options. I give this review a rating of 9 out of 10.
Enabling TigerGraph represents a niche skill, and most people initially lack knowledge about it. However, once they understand TigerGraph's features, the exposure to the NoSQL category of graph databases fascinates many, demonstrating how we can visually see and represent data without the rigidity of legacy systems, significantly affecting project acceptance and positively impacting our approach. Concerning agentic AI, graph RAG, and hybrid graph with vector search capabilities, TigerGraph has made significant advancements that cater to current needs. My experience with TigerGraph involves establishing agents that automate processes, allowing an agent to convert text to GSQL language, facilitating output in English format. This in-house POC helps us understand leveraging LLMs for interaction with the database, making it accessible even for users without prior knowledge of TigerGraph. The features and built-in use cases and solutions TigerGraph offers are compelling reasons for anyone to consider its implementations. I gave this review a rating of eight out of ten overall. My experience with TigerGraph has been exceptional, and I believe more focused enablement and diverse use cases will encourage broader adoption and implementation across various projects.
Start with the Community Edition and then design your graph schema iteratively. Expect a learning curve with GSQL and test real-world scale early so you can plan your export strategy and engage with the TigerGraph solution team for any enterprise licenses. I would rate this product a nine out of ten.
Senior DB Engineer And Sre at a tech vendor with 10,001+ employees
Real User
Top 20
May 14, 2026
My advice for others considering using TigerGraph is to test it, conduct a proof of concept, and verify that it meets their requirements. Perform a load test to see the performance. I would rate this review as a seven out of ten.
Responsable Del Equipo D Más D at a tech services company with 51-200 employees
Real User
Top 10
May 12, 2026
My advice to other people who are considering using TigerGraph is to learn GSQL well in order to write good queries, to understand how graphs work, and to thoroughly review TigerGraph's user guides. I rate this review seven overall.
If your application or company needs a platform that will grow and handle datasets growing into millions in the near future, and if your company has the budget for TigerGraph, then you should go for it. It may be a little costly, but it ultimately provides very fast analytical capabilities of datasets, which is great. I would rate this product a 9 out of 10.
TigerGraph offers a graph analytics platform that efficiently handles large-scale and complex data relationships, providing insights for informed decision-making.Specialized for big data, TigerGraph leverages a native parallel graph architecture to analyze data relationships rapidly. It is designed to manage extensive datasets, providing real-time insights that are invaluable for sectors like financial services, healthcare, and telecommunications. With its scalable infrastructure, it supports...
The learning curve for new users getting started with TigerGraph can be somewhat challenging. Understanding tree diagrams and graph algorithms is necessary, so it could be tough initially. TigerGraph handles real-time analytics and streaming data well, but you will need additional add-ons for that, such as streaming add-ons including Pub/Sub. The documentation and training resources for TigerGraph are phenomenal and incredibly well done on their site. My advice to others looking into using TigerGraph is to consider the strong support and tutorials available for learning it. For data scientists and engineers, I would recommend looking into TigerGraph alongside other major players such as Neo4j to make a fair comparison, as TigerGraph provides excellent support and licensing options. I give this review a rating of 9 out of 10.
Enabling TigerGraph represents a niche skill, and most people initially lack knowledge about it. However, once they understand TigerGraph's features, the exposure to the NoSQL category of graph databases fascinates many, demonstrating how we can visually see and represent data without the rigidity of legacy systems, significantly affecting project acceptance and positively impacting our approach. Concerning agentic AI, graph RAG, and hybrid graph with vector search capabilities, TigerGraph has made significant advancements that cater to current needs. My experience with TigerGraph involves establishing agents that automate processes, allowing an agent to convert text to GSQL language, facilitating output in English format. This in-house POC helps us understand leveraging LLMs for interaction with the database, making it accessible even for users without prior knowledge of TigerGraph. The features and built-in use cases and solutions TigerGraph offers are compelling reasons for anyone to consider its implementations. I gave this review a rating of eight out of ten overall. My experience with TigerGraph has been exceptional, and I believe more focused enablement and diverse use cases will encourage broader adoption and implementation across various projects.
Start with the Community Edition and then design your graph schema iteratively. Expect a learning curve with GSQL and test real-world scale early so you can plan your export strategy and engage with the TigerGraph solution team for any enterprise licenses. I would rate this product a nine out of ten.
My advice for others considering using TigerGraph is to test it, conduct a proof of concept, and verify that it meets their requirements. Perform a load test to see the performance. I would rate this review as a seven out of ten.
My advice to other people who are considering using TigerGraph is to learn GSQL well in order to write good queries, to understand how graphs work, and to thoroughly review TigerGraph's user guides. I rate this review seven overall.
If your application or company needs a platform that will grow and handle datasets growing into millions in the near future, and if your company has the budget for TigerGraph, then you should go for it. It may be a little costly, but it ultimately provides very fast analytical capabilities of datasets, which is great. I would rate this product a 9 out of 10.