PhoenixAI vs.
Trino / Starburst
Trino and Starburst are excellent for ad-hoc lake exploration and large ETL jobs — the workloads they were designed for. When teams use them as a serving layer for dashboards and AI agents, the architecture breaks: queries take seconds to minutes, concurrency saturates quickly, and there is no path to real-time data. PhoenixAI replaces Trino as the serving engine. Your lake and ETL workflows stay exactly where they are.
When to switch
Three signals Trino has hit its serving ceiling.
01
Dashboard queries take 10–60 seconds
Trino reads from remote object storage on every query. Every dashboard page load issues a round-trip to S3 or GCS. End users abandon before the data arrives. PhoenixAI’s intelligent local SSD cache delivers sub-second responses on the same data — without moving it out of your lake.
02
A handful of concurrent queries saturates the cluster
Trino was built for a few large ad-hoc jobs, not many small concurrent ones. When customer traffic or AI agent workloads hit simultaneously, the cluster saturates and queries queue. PhoenixAI sustains 10,000+ QPS at sub-second latency — the access pattern serving layers actually need.
03
You need real-time data alongside historical lake queries
Trino queries what’s in your lake — which is only as fresh as your last batch ETL. For workloads that mix streaming event data with historical lake tables in the same query, Trino has no answer. PhoenixAI ingests streaming data natively and federates lake tables in one SQL layer.
Head-to-head
| PhoenixAI | Trino / Starburst | |
|---|---|---|
| Query latency (serving) | Sub-second with local SSD cache | Seconds to minutes; every query reads remote storage |
| Concurrency | 10,000+ QPS sustained | Built for few large jobs; saturates under concurrent load |
| Data freshness | Sub-10s with native streaming ingestion | As fresh as last batch ETL; no streaming ingestion |
| Real-time + historical in one query | Native: stream tables + lake tables unified | Not supported; separate systems required |
| Native upserts | PK-based upserts on streaming tables | No native upsert capability |
| Local data caching | Intelligent SSD cache, auto-promoted | No local cache; all reads from remote storage |
| Lakehouse support | Iceberg, Delta Lake, Hudi — native | Native Iceberg and Delta; strong lake connector |
| Ad-hoc lake exploration | Capable, not the primary focus | Core Trino strength — keep for this |
| Large ETL jobs | Not the design point | Well-suited; keep Trino for ETL |
| Operations model | BYOC managed: AWS, GCP, Azure | Self-managed OSS or Starburst Galaxy SaaS |
Teams that made the switch.
SmartNews
Replaced Trino + ClickHouse
3.6× faster
than Trino on ad-hoc
Replaced two serving engines — Trino and ClickHouse — with a single PhoenixAI deployment. 3.6× faster than Trino on ad-hoc queries. One SQL layer instead of two systems to maintain.
Herdwatch
Replaced Athena on Iceberg (same architecture)
700ms–1.5s
from 2–5 minutes
Swapped the query engine on existing Iceberg tables. No data movement. Query latency dropped from 2–5 minutes to 700 milliseconds to 1.5 seconds. Same migration pattern applies to Trino-served lake tables.
Yuno
Replaced Redshift + Athena stack
40%+
compute cost reduction
Consolidated Redshift and Athena into PhoenixAI. Latency from 3 seconds to sub-1 second. Freshness from 1 hour to 5 seconds. 40%+ compute reduction. One serving layer instead of two.
What stays on Trino
PhoenixAI replaces Trino as the serving layer — not as the exploration or ETL tool. The following workloads remain on Trino and are not in scope for PhoenixAI.