AI & ML Data Infrastructure
Your AI agents need data that’s actually live.
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.
The problem
AI agents are only as good as the data they can access.
What breaks without real-time data
Agents working with stale structured data
- Agents query a data warehouse and get yesterday’s results
- Feature stores lag behind live events by hours
- Analytical queries over large tables time out or queue
- High concurrency from multiple agents degrades performance
With PhoenixAI
Live structured data at agent speed
- Sub-second SQL responses for AI agent queries
- 10K+ QPS — PhoenixAI handles this and everything else
- Standard SQL — no custom API for agents to learn
- Sub-5-second data freshness from streaming ingestion
Capabilities
The analytical layer your AI stack is missing.
What PhoenixAI provides that vector stores and data warehouses can’t.
Real-time data for agents
AI agents query PhoenixAI via standard SQL or REST. Responses are sub-second on live data — not batched results from the previous hour.
Feature store acceleration
Serve ML features with sub-5-second freshness. Aggregate raw event streams into feature vectors in real time without a separate feature computation pipeline.
Hybrid retrieval for RAG
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.
High-concurrency agent workloads
Thousands of agents querying simultaneously. PhoenixAI maintains consistent latency under high concurrency — designed for the access patterns AI workloads create.
Fits your AI stack
PhoenixAI integrates with the streaming, orchestration, and model serving tools in modern AI infrastructure.
Streaming data sources
- Apache Kafka
- Apache Flink
- Apache Spark
- Confluent
Integrations
- REST / SQL
- Apache Iceberg
ML platforms
- Databricks
- Snowflake
In production
How AI teams use PhoenixAI today.
Petabytes agent workloads
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.
See it run on your AI workload.
Bring your agent query patterns. We’ll show you latency on live data.