PhoenixAI Cloud · Bring your own cloud
Sub-second queries across streaming and lakehouse data, at the concurrency AI agents and customer-facing analytics demand. Deployed in your own cloud account, fully managed by us.
What it is
PhoenixAI is a real-time analytical database for AI agents and customer-facing analytics. It serves sub-second SQL across streaming data and your Apache Iceberg lakehouse, with minimal data movement.
It handles workloads that break other databases: agent queries needing sub-second answers, complex multi-table joins on fresh data, and customer-facing analytics at tens of thousands of QPS. Standard SQL throughout, so existing tools connect without rewrites.
<1s
Query latency
on multi-table analytical queries
<5s
Ingest to queryable
on streaming mutable data
10K+
QPS
Sustained load
90%
Lower infrastructure footprint
than other real-time analytics solutions
Deployment model: BYOC
PhoenixAI Cloud is bring-your-own-cloud, not a shared SaaS. Clusters run inside your VPC and your data stays in your storage. We operate the service from outside.
Your tables live in your own object storage, and every query runs on compute inside your VPC. Nothing is copied into a PhoenixAI-hosted warehouse.
Upgrades and patches are handled by the PhoenixAI team, with monitoring, alerting and automated failure recovery built in. Your team runs the cluster from one console.
Clusters deploy into a new or existing VPC of yours, through credentials you create and can delete. Traffic to our VPC can stay on your cloud provider's network with AWS PrivateLink, Azure Private Link or Google Cloud Private Service Connect.
Instances and storage are resources in your own cloud account, so they count toward the commitments and discounts you already have with AWS, Azure or Google Cloud. PhoenixAI usage is billed by the second, and clusters can be suspended when idle.
Core capabilities
Four things PhoenixAI is built to do, at the scale AI workloads demand.
PhoenixAI keeps query latency under a second across both freshly streamed data and historical lakehouse data, under the high concurrency AI agents and customer-facing analytics demand. Latency stays stable whether traffic is hundreds of QPS or tens of thousands.
AI agents and dashboards issue queries you can’t always plan for. PhoenixAI runs multi-table joins on the fly against normalized schemas, with no need to pre-flatten or pre-aggregate. Materialized views stay available as optional accelerators for the heaviest repeat patterns, not as a mandatory starting point.
PhoenixAI sits alongside the infrastructure you already have. Query your lakehouse in place, ingest streams as they arrive, and connect through standard SQL, without rewriting queries, replacing tools, or copying data into a separate warehouse.
Enterprise governance is built into the database, not bolted on. Fine-grained access controls, per-query audit, and BYOC deployment on AWS, Azure, or GCP let your data and security teams keep enforcement and sovereignty in their own hands.
How it works
The optimizer picks join strategies, predicate push-downs, and execution paths based on actual data statistics at runtime. Multi-table joins that time out elsewhere complete in well under a second, without manual hints or query rewrites.
PhoenixAI’s query engine processes data in column-oriented batches using SIMD CPU instructions. On complex aggregations and scans across billions of rows, this translates directly into sub-second response times.
Massively parallel processing compute architecture distributes joins and aggregations across every node in the cluster. Complex multi-table joins on large fact and dimension tables scale out as the dataset grows, with no need to pre-aggregate or denormalize by default.
Works with
In production
We reduced p90 latency by 50% using only 32% of our previous instance count — a three-fold improvement in cost-performance efficiency. Data freshness dropped to 10 seconds.
PhoenixAI customer
50%
latency reduction
PhoenixAI is at the center of our real-time data analytics. We strive for quicker and easier insights into day to day operations. We chose PhoenixAI for its ability to upsert data in real-time, support for joins across large fact tables with very low latency, and the ability to serve and join native and external tables from the same cluster.
PhoenixAI customer
<1s
join latency
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
Book a 30-minute session with our team. Bring your workload; we’ll walk through it live and show you how PhoenixAI would handle it.