Top 0 Graph Databases SaaS Companies With $10M–$100M Revenue (September 2026)
As of September 2026, Latka tracks 0 graph databases SaaS companies with $10M–$100M in annual revenue.
Every company below sells graph databases software to other businesses and is ranked by its most recent annual revenue. Revenue, funding, headcount and customer figures come from CEO interviews on the Latka podcast, public company filings, and Latka estimates where a company has not disclosed a number.
What Graph Databases SaaS Companies do
Graph databases are specialized database management systems designed to store and manage data structured as graphs. They utilize nodes, edges, and properties to represent and handle complex relationships effectively, making them particularly useful for modeling interconnected data. These databases excel in scenarios where relationships between data points are paramount, such as social networks, recommendation engines, and fraud detection. Typical features of graph databases include the ability to perform complex queries on relationships and traverse data efficiently. Users can execute graph algorithms to derive insights and analyze patterns, making these databases popular among data scientists, software developers, and analysts. The common buyer personas for graph databases include IT and data engineering teams that are focused on handling large volumes of interconnected data and require versatile solutions for data modeling and retrieval.
- Companies
- 0
- Revenue
- -
- Funding
- -
- Employees
- -
Filters
Sorting: Highest -> Lowest
Top Graph Databases SaaS Companies With $10M–$100M Revenue
Showing 0 of 0 companies ranked by annual revenue.
Related IT Infrastructure Software categories
Inclusion Criteria
- Must utilize a graph data model to represent data structures - Should support complex relationships and allow for efficient traversal of these relationships - Must include capabilities for conducting graph algorithms and analytical operations - Should be designed to scale with large datasets and high query volumes - Not just a relational database; must specifically offer graph-based querying features - Should provide robust data integrity and support for transactional operations - Must allow for flexible schema design to accommodate evolving data structures