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Top 3 Graph Databases SaaS Companies (August 2026)

As of August 2026, Latka tracks 3 graph databases SaaS companies. They have combined revenues of $1.1M and employ 10 people.

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
3
Revenue
$1.1M
Funding
-
Employees
10

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Top Graph Databases SaaS Companies by revenue

Showing 3 of 3 companies ranked by annual revenue.

1GraphGrid logo
GraphGrid

Wooster, Arizona, United States

Developer of graph database management platform designed to facilitate aggregating, managing, securing and analyzing multi-source data at scale. The company's platform unleashes the power of enterprise Neo4j and Amazon cloud services through embracing data relationships at the core of the architecture and provides 24/7 deployment, management, operation and support for an entire ecosystem of services, enabling users to have rapid connection and analysis of their enterprise's data.

Revenue
$649.5K
Year founded
2018
Team size
5
2Speedment logo
Speedment

Palo Alto, California, United States

Developer of a cloud-based software designed to focus on graph databases and optimization of databases. The company's software helps to improve the performance of data-intensive web application, reduces time to market for Ext JS projects and improves application responsiveness, enabling back end developers to rapidly convert their large relational databases into in-memory Java objects that speed up data access by orders of magnitude.

Revenue
$321.1K
Year founded
2007
Team size
3
3Dydra logo
Dydra

New Orleans, Louisiana, United States

Provider of a cloud-based database-as-a-service designed to help in semantic data management. The company's graph database focuses on data integrity and availability, offering cloud services for remote access and in-house installations, enabling businesses to store, analyze and leverage big data.

Revenue
$97.5K
Year founded
2010
Team size
2

Frequently asked questions about Graph Databases SaaS Companies

How many graph databases SaaS companies are there?

Latka tracks 3 graph databases SaaS companies with reported revenue. Together they generate $1.1M in annual revenue and employ 10 people.

Which graph databases SaaS company is the largest?

GraphGrid is the largest, with $649.5K in annual revenue, founded in 2018.

How much revenue does a typical graph databases SaaS company make?

The average graph databases SaaS company in this list makes $356K a year, across 3 companies with reported revenue.

Who are the leading Graph Databases vendors?

Ranked by annual revenue, the leaders are GraphGrid, Speedment and Dydra.

Related IT Infrastructure Software categories

All IT Infrastructure Software SaaS categoriesEvery category we track inside IT Infrastructure Software, each ranked by revenue.Browse every SaaS industryThe full A-Z of SaaS categories Latka tracks, from Graph Databases to everything else.

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