AI & ML Data Infrastructure
Vector stores handle semantic search. PhoenixAI handles everything else — structured analytics, aggregations, filtering, and joins on data that updates in real time. The two work together. Neither replaces the other.
<1s
Query latency for AI agents
p99 on structured analytical queries
<5s
Data freshness
Streaming ingestion
10K+
QPS
Agent workloads
The problem
What breaks without real-time data
With PhoenixAI
Capabilities
What PhoenixAI provides that vector stores and data warehouses can’t.
AI agents query PhoenixAI via standard SQL or REST. Responses are sub-second on live data — not batched results from the previous hour.
Serve ML features with sub-5-second freshness. Aggregate raw event streams into feature vectors in real time without a separate feature computation pipeline.
Combine structured analytical queries with your vector retrieval pipeline. Filter by recency, user segment, or business rules in SQL before passing context to your LLM.
Thousands of agents querying simultaneously. PhoenixAI maintains consistent latency under high concurrency — designed for the access patterns AI workloads create.
PhoenixAI integrates with the streaming, orchestration, and model serving tools in modern AI infrastructure.
Streaming data sources
Integrations
ML platforms
In production
“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.”
Ryan Nowacoski
Senior Engineering Manager, Data Platform, Demandbase
Petabytes
agent workloads