Analyst Coverage: Daniel Howard
Ultipa is only a few years old – it was founded in 2019 – and its product (of the same name) is even younger – it was commercialised in 2021 – but the company already has offices that stretch across Europe, Asia, and the United States, as well as a customer base in (retail) banking.
So, what makes it worth talking about? Apart from its youth, its most significant differentiator is its performance, which it claims outstrips pretty much all of the most relevant graph databases in use today, including open-source databases like JanusGraph (which is honestly not terribly impressive), Neo4j, and TigerGraph (which, if true, absolutely is). It also claims to use a more intuitive, easier-to-use custom query language (the straightforwardly named ‘Ultipa Query Language’, or UQL) than, say, TigerGraph (UQL is actually closer to Cypher than anything else – it’s certainly simpler than, say, GSQL, and an adaptor for going from Cyper to UQL is provided). In addition, and interestingly, Ultipa describes its database as ‘demi-schematic’, meaning that it can be used both with and without a schema.
But it is clear that sheer speed and performance is Ultipa’s main selling point, and the company has provided some impressively detailed (and just generally impressive) benchmarks to this effect, that seem to indicate that – at the very least – Ultipa is much faster than JanusGraph and Neo4j, and significantly faster than TigerGraph. Now, we at Bloor tend to be sceptical of these kinds of performance benchmarks, for a number of reasons which aren’t worth getting going into here. And Ultipa’s benchmarks are not exceptional in that regard, so we would not take these conclusions at face value. But even then, it seems pretty obvious that Ultipa’s performance is in the same ballpark as the TigerGraphs of the world, not the Neo4js (and let alone the JanusGraphs). For such a recent offering, that alone is impressive.