agreed, and we've heard heart-attack-level pricing pushback for the few db's that do scale well likewise, for analytics, we're seeing a bit of a split: - DB s

agreed, and we've heard heart-attack-level pricing pushback for the few db's that do scale well likewise, for analytics, we're seeing a bit of a split: - DB side: Traditional real-time graph analytics (pattern search, ...) can often be done on extracts from KV-store level queries, so just use those and post-process the compute ("extract 1-2 hop neighborhood, then cross-product in lang xyz"). Like the old Titan -> Cassandra days. GPU can be nice for accelerating a simple inferencer here (e.g., a T4), but not necessarily the actual fetch. Gets a bit more blended in 'real' knowledge graphs, but that's more R&D. (Edit: I believe Facebook's at-scale graph engine successfully runs on top of SQL for related reasons.) - AI side: Massive interest in GNNs (we're active here!) as starting to eat the lunch of the result quality that traditional graph analytics can give. Basically the pendulum swinging back to graph for areas like recommendors & classifiers. Shopping carts, fraud, cyber, etc. These had gone the way of ML + AI systems for awhile now, but with GNNs becoming practical, best of both. And... GPUs matter over CPUs again. Funny enough, we're getting into a bunch of vector search scenarios, and because of our particular scale & query richness needs... looking at OSS graph DBs and pairing with GPU nodes. /insert "why not both" meme here