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VOOZH | about |
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VOOZH | about |
Products
PhoenixAI Cloud Fully managed. Cloud or self-hosted. Pricing Flexible plans for every scale.Use Cases
AI & Agents Real-time data for agentic AI workloads. Real-Time Analytics Sub-second queries on live data streams. Customer-Facing Analytics Embedded analytics built for your product. Lakehouse Analytics Query Iceberg and Delta Lake at sub-second speed.Learn
Blog AI agents, real-time analytics, and data engineering. Docs Full technical documentation. Glossary Key terms in real-time analytics and AI data.Assets & Events
Whitepapers Deep technical reads on performance and architecture. Events Conferences, meetups, and online sessions. Customer Case Studies Real-life customers stories of how they transformed their data stack.Latest whitepaper
Real-Time Analytics for Customer-Facing Applications Whitepaper
Analytics is no longer just a tool for internal decision-makers.
Download freeAbout
About PhoenixAI Who we are and why we built this. Partners AWS, Confluent, Databricks, and more. Newsroom Press coverage and announcements.Customer story
Customer Facing Analytics Meets Agents
Eightfold’s Move from Redshift.
Read the storyProduct › Comparisons
ReplaceClickHouse is fast on single-table aggregations. When your workload adds multi-table joins, AI agent queries, or production governance requirements, the architecture hits its limits. PhoenixAI is a direct replacement — same SQL, no rewrites, better results on the workloads that matter to modern data teams.
PhoenixAI
vs
ClickHouse
When to switch
01
Your JOINs are failing or slow
ClickHouse runs JOINs on a single node. Multi-table dashboards stall, and teams spend weeks pre-flattening data into wide tables. PhoenixAI runs distributed MPP JOINs sub-second across normalized schemas — no denormalization required.
02
An AI agent is writing your SQL
When an LLM writes the SQL, no human checks the plan. ClickHouse's rule-based optimizer can't adapt, so novel queries silently get bad plans. PhoenixAI's CBO reorders joins and picks algorithms at runtime — the LLM owns intent, the engine owns the plan.
03
You need real-time updates, not just append-only
ClickHouse Cloud's merge-on-read forces a choice: stale rows or the FINAL tax. So it only fits append-only logs — CDC, payments, and inventory drift to minute-level freshness. PhoenixAI commits upserts in place on the PK: sub-10-second freshness at 100K+ events/sec.
| PhoenixAI | ClickHouse | |
|---|---|---|
| Multi-table JOINs | Distributed MPP, sub-second | Single-node; breaks at scale; teams denormalize |
| Query optimizer | Cost-based, auto-tunes any shape | Rule-based; every novel shape risks a bad plan |
| SQL compatibility | Full ANSI SQL | Incomplete dialect; LLM-generated SQL fails at random |
| Concurrency | 10,000+ QPS sustained | Fast on flat-table scans; degrades on concurrent joins |
| Data freshness | Sub-10s, native upserts on PK tables | Merge-on-read: stale rows or FINAL penalty on every read |
| Lakehouse support | Native Iceberg and Delta Lake | Building |
| Agentic AI workloads | Built for unpredictable query shapes | Struggles with ad-hoc; requires pre-computation |
Demandbase
Replaced 49 ClickHouse clusters
49 → 1
clusters after migration
Consolidated 49 ClickHouse clusters to a single PhoenixAI deployment. ~90% storage reduction. Petabyte-scale analytics on Iceberg. Denormalization pipelines retired entirely.
Coinbase
Replaced ClickHouse + TiDB
573B rows
300+ tables, sub-second
Consolidated ClickHouse and TiDB onto PhoenixAI's StarRocks engine. 573 billion rows across 300+ tables and 10 blockchains, 30K messages/sec ingestion, sub-second query latency.
SmartNews
Replaced ClickHouse + Trino
800+ QPS
sub-second, one engine
Collapsed a fragmented ClickHouse + Trino stack onto PhoenixAI Cloud. 800+ QPS at stable sub-second latency, 3.6× faster ad-hoc queries, real-time joins without denormalization, and one engine serving both customer-facing and internal analytics.
We’ll walk through a live demo and scope the migration path for your specific setup.