Why CockroachDB makes sense for AI / vector workloads:
One system for transactions + vectors
CockroachDB lets teams store embeddings alongside transactional data (users, sessions, permissions, fraud events) instead of stitching together OLTP + vector DB + cache.
Global, multi-region by default
Vector search often sits on the critical path for agentic workflows. CRDB’s multi-region replication and locality controls keep latency low without giving up consistency.
Strong consistency for agent state
Agentic and GenAI apps need correct state (memory, tool outputs, decisions). CRDB’s serializable isolation avoids subtle bugs caused by eventually consistent vector stores.
Operational simplicity at scale
No shard management, no leader pinning, no manual failover. This matters when vector workloads spike unpredictably due to LLM traffic.
Vectors are first-class, not an add-on
Native vector types + indexing (C-SPANN) means embeddings live in the same durability, backup, and security model as the rest of your data.
Works well for hybrid workloads
Real AI systems mix:
vector similarity search
metadata filtering
transactional updates
streaming / CDC
CRDB handles all of these in one engine.
