Real-Time Analytics
PhoenixAI sits on top of your existing data stack and makes it real-time. Connect to your Kafka streams and lakehouse, and serve sub-second query results to dashboards, applications, and teams — on data that’s seconds old, not hours.
100×
Faster than batch analytics
vs. Apache Druid on production workloads
<1s
Ingest to queryable
Streaming data
10s
Data freshness
Pinterest production
The problem
With batch ETL today
With PhoenixAI
Capabilities
Built for the workloads that break batch-oriented databases.
Ingest from Kafka, Flink, Spark, or Kinesis. Data is indexed and queryable within one second of arrival — with full support for appends, updates, and deletes.
Vectorized columnar execution and intelligent caching maintain stable p99 latency even under thousands of concurrent queries on billions of rows.
No denormalization required. PhoenixAI’s cost-based optimizer handles multi-table joins across large fact and dimension tables at sub-second latency.
Define views for your most common query patterns. PhoenixAI auto-refreshes them as data arrives — delivering pre-computed results without manual pipeline maintenance.
Query Apache Iceberg and Delta Lake tables directly. Intelligent local caching brings data warehouse performance to your object storage — without moving data.
SOC 2 certified. Row-level security, column masking, audit logging, and fine-grained access controls built into the database — not bolted on.
PhoenixAI connects to the streaming, storage, and BI tools you already use. Most teams are in production within two to four weeks.
Streaming ingestion
Storage & lakehouse
BI & visualization
In production
“After migrating to PhoenixAI, we reduced p90 latency by 50% using only 32% of our previous instance count. Data freshness dropped to 10 seconds. The team spent less time on operations and more time building.”
Data Infrastructure Team
50%
latency reduction
AI & ML Data Infrastructure
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.
<100ms
Query latency for AI agents
p99 on structured analytical queries
<1s
Data freshness
Streaming ingestion
10K+
Concurrent queries
Agent workloads
The problem
What breaks without real-time data
With PhoenixAI
Capabilities
What PhoenixAI provides that vector stores and data warehouses can’t.
AI agents query PhoenixAI via standard SQL or REST. Responses are sub-100ms on live data — not batched results from the previous hour.
Serve ML features with second-level freshness. Aggregate raw event streams into feature vectors in real time without a separate feature computation pipeline.
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.
Thousands of agents querying simultaneously. PhoenixAI maintains consistent latency under high concurrency — designed for the access patterns AI workloads create.
PhoenixAI integrates with the streaming, orchestration, and model serving tools in modern AI infrastructure.
Data ingestion
Agent & LLM frameworks
ML platforms
In production
“We needed a database that could serve our AI pipelines with fresh data at millisecond latency — at the concurrency our agent workloads demanded. PhoenixAI was the only database that handled all three without trade-offs.”
Eightfold.ai
AI Platform Engineering
<100ms
agent query latency
Customer-Facing Analytics
Building embedded analytics into your product means serving thousands of users, each with their own data, simultaneously. PhoenixAI is built for exactly this — multi-tenant, sub-second, at any scale.
82ms
Dashboard load time
Coinbase production (down from 8 seconds)
10K+
Concurrent users
Sustained QPS
50%
Lower infra cost
vs. Druid
The problem
What breaks at scale
With PhoenixAI
Capabilities
The features that matter when analytics is a core part of your product.
Maintain consistent dashboard load times whether you have 100 or 100,000 concurrent users. PhoenixAI’s architecture doesn’t degrade under load.
Row-level security enforces tenant data boundaries at query execution time. No application-layer workarounds, no risk of cross-tenant data leakage.
Your customers see data that’s seconds old, not hours. Event streams feed directly into the query layer — no ETL delay between action and insight.
Standard JDBC/ODBC, REST API, and MySQL-compatible wire protocol. Integrate PhoenixAI into your application stack without changing your query patterns.
PhoenixAI connects to the tools product engineering teams already use for data pipelines, embedding, and visualization.
Data ingestion
Application integration
Embedded analytics
In production
“We were spending more time managing the database than building on top of it. After moving to PhoenixAI, dashboard load time dropped from 8 seconds to 82 milliseconds. The migration took three weeks. I expected three months.”
Rachel Johnson
Sr. Director, Data Infrastructure · Coinbase
82ms
dashboard load time
Data Lakehouse
Apache Iceberg and Delta Lake made the lakehouse possible. PhoenixAI makes it fast. Query your lakehouse tables at sub-second latency — no ETL, no data movement, no separate warehouse to maintain.
DW speed
On lakehouse data
Without copying data into a warehouse
0
ETL jobs needed
Query in place
50%
Cost reduction
vs. loading to DW
The problem
The lakehouse query problem
With PhoenixAI
Capabilities
What PhoenixAI adds to an Iceberg or Delta Lake architecture.
Query Iceberg, Delta Lake, and Hudi tables directly from S3, GCS, or ADLS. Full read and write support. No connectors, no Spark job, no ETL.
Hot data is automatically promoted to local SSD. Repeated queries hit cache instead of object storage — delivering sub-second latency at data lake cost.
Join real-time streaming data with historical lakehouse tables in a single SQL query. No federation overhead, no separate query engine for each layer.
Works with AWS Glue, Hive Metastore, and Unity Catalog. Use your existing table definitions — no schema migration, no re-registration.
PhoenixAI integrates with open table formats, object storage, catalogs, and the processing engines that feed your lakehouse.
Open table formats
Object storage
Catalogs & platforms
In production
“We were running Spark jobs just to get interactive query performance on our Iceberg tables. PhoenixAI eliminated that entirely. We query Iceberg directly and get sub-second results. One less pipeline to maintain.”
Demandbase
Data Platform Engineering
0
ETL jobs replaced