The real-time
analytical database
for AI agents.

Ryan NowacoskiSenior Engineering Manager, Data Platform · Demandbase

“Demandbase AI introduced unpredictable LLM-generated SQL that our previous ClickHouse-based architecture wasn’t built to handle. PhoenixAI gives us a fast, isolated warehouse for agent workloads directly on our Apache Iceberg tables, with the optimizer handling novel joins automatically. Our agents now query petabytes of normalized data across thousands of tenants while customer-facing dashboards keep their second-level SLAs.”

60%

hardware reduction

90%

storage cost reduction

49→1

cluster consolidation

Wei ZhengChief Product Officer · Conductor

“Hundreds of enterprise brands use Conductor to track their AI and search visibility across years of data. Delivering that customer-facing experience at terabyte scale under high concurrency outgrew our legacy analytics layer. PhoenixAI now serves that workload as the real-time analytical database on top of our data lakehouse. Production queries scanning hundreds of millions of rows return in under a second, and this is the architecture we need for the next generation of agentic workloads.”

<1 s

query latency

<2 mo

idea to production

100%

decimal accuracy

Xinyu LiuSenior Staff Software Engineer · Coinbase

“Coinbase’s blockchain dataset spans hundreds of billions of rows across more than 300 normalized tables. Our fraud and compliance workloads require complex joins, and blockchain network analytics demand low-latency query performance that our previous query engines could not provide. PhoenixAI changed the equation: streaming updates from Kafka become queryable within seconds, analysts get sub-second responses on live normalized data, and our AI agents operate on the same real-time dataset. This level of performance at scale fundamentally changes what data teams can do.”

573B

rows in production

30K/s

Kafka ingestion

<1 s

query latency