PhoenixAI alongside
Databricks.
Databricks is the right platform for Spark training, notebooks, Unity Catalog governance, and large-scale ETL. Databricks SQL was designed for interactive BI — not for sub-second customer-facing analytics or the concurrency that AI agent workloads demand. PhoenixAI handles the serving layer; Databricks handles everything else.
PhoenixAI
vs
Databricks
When to add PhoenixAI
Three signs Databricks SQL needs a serving layer.
01
Customer-facing queries stall under concurrent load
Databricks SQL is built for interactive BI — a few analysts querying at once. Customer-facing workloads need 10,000+ QPS at sub-second latency sustained over time. PhoenixAI is purpose-built for that access pattern, on the same Delta Lake data, without moving it.
02
AI agents need data fresher than Delta sync delivers
DeltaStreamer and Auto Loader batch-sync data into Delta Lake. Agents working on customer behavior or transaction data see what the last sync pushed. PhoenixAI ingests streaming data directly and serves it at sub-10 second freshness — without disrupting your Delta tables.
03
SQL warehouses are an expensive way to serve reads
SQL warehouses start cold, spin up slowly, and are priced for compute-heavy jobs — not always-on serving workloads. PhoenixAI delivers consistent sub-second latency on a serving-optimized architecture, at lower cost than running SQL warehouses hot 24/7.
What each system does best
| Workload | PhoenixAI | Databricks |
|---|---|---|
| Customer-facing analytics | Sub-second, 10K+ QPS, multi-tenant | SQL warehouses queue under real user traffic |
| AI agent serving queries | Built for high-concurrency, unpredictable shapes | Not optimized for agent-scale concurrency |
| Real-time data freshness | Sub-10s from streaming sources | Delta sync is batch-based; minutes to hours |
| Spark ML training & notebooks | Not applicable | Industry-leading |
| Unity Catalog governance | Works with Unity Catalog | Full governance, lineage, access control |
| Large-scale ETL & data prep | Not the focus | Core Databricks strength |
| Delta Lake federation | Native Delta Lake reads, no copy | Source of truth |
| Always-on serving cost | Optimized for sustained serving workloads | SQL warehouses are expensive to keep warm 24/7 |
| Deployment | BYOC: your AWS, GCP, or Azure account | Vendor-managed cloud |
Teams running both.
Conductor
Greenfield agentic AI deployment
<2 months
idea to production
Built a new agentic AI application on a Hudi + S3 lakehouse. PhoenixAI served as the real-time query layer. Sub-second latency on 240 million rows. Greenfield to production in under two months.
Herdwatch
Replaced Athena on Iceberg lakehouse
700ms–1.5s
from 2–5 minutes on Athena
Query engine swapped on existing Iceberg tables. No data movement. Same Unity Catalog. Latency dropped from minutes to under 1.5 seconds. Databricks retained for training and ETL.
SmartNews
Consolidated Trino + ClickHouse
3.6×
faster than Trino on ad-hoc
Replaced two serving engines with PhoenixAI as the single analytical layer. 3.6× faster than Trino on ad-hoc queries. Spark and notebook workloads remained on the data platform unchanged.
What stays on Databricks
PhoenixAI is not a Databricks replacement. Databricks has deep capabilities PhoenixAI does not target. The following remain on Databricks and are not in scope for PhoenixAI.