The Agentic AI Database
AI agents don’t wait for batch jobs. PhoenixAI gives them sub-second SQL on live enterprise data — at the concurrency, freshness, and scale that agentic workloads actually demand.
Results from production
<1s
Analytical query latency
5s
Ingest to queryable
Mutable streaming data
10K+
Queries per second
Sustained concurrency
The problem
Traditional data warehouses run on batch ETL pipelines. Data arrives on a schedule — which means your dashboards, agents, and applications are always working with data that’s hours old.
PhoenixAI ingests streaming data directly from Kafka, Flink, Spark, along with other real-time and batch data sources, and makes it queryable in under one second. Same SQL, same BI tools, same cloud — just without the lag. No rip-and-replace required.
See how it worksBatch analytics
2–24 hrs
Time from event to queryable data with typical batch ETL pipelines
PhoenixAI
<5 sec
Time from event to queryable data with streaming ingestion
Why PhoenixAI
Three things that make PhoenixAI the right choice for teams who can’t afford to wait for their data.
Vectorized execution and columnar storage deliver sub-second query results on billions of rows. Whether it’s a customer dashboard loading in 80ms or an AI agent querying live transaction data, PhoenixAI doesn’t make users wait.
Performance detailsPhoenixAI connects directly to the tools you already use — Kafka, Flink, Spark, Apache Iceberg, Delta Lake, dbt, and your existing BI layer. Standard SQL throughout. Most teams go from evaluation to production in two to four weeks.
View integrationsSOC 2 certified. Fine-grained access controls, row-level security, audit logging, and data masking built into the database — not bolted on. Enterprise security that doesn’t slow down your engineering team.
Security overviewThe architecture
Powering agents, enterprise applications, and BI inside your own cloud.
By the numbers
573B+
Rows at sub-second
Coinbase blockchain analytics
10K+
Concurrent queries
Sustained analytical throughput
90%
Cost reduction
Fanatics, 6 PB migrated
<5s
Ingest-to-queryable
Kafka stream to live SQL
Customer stories
"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."
<1 s
analytical query latency
"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."
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.
