VOOZH about

URL: https://www.phoenixdata.ai/solutions/customer-facing-analytics

⇱ Customer-Facing Analytics | PhoenixAI

Log in Contact Sales

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.

Industries

Finance & Banking Logistics & Transportation Manufacturing Healthcare Retail & E-Commerce Energy & Utilities Telecommunications Public Safety & Government

Comparisons

PhoenixAI vs. Databricks Real-time OLAP vs. batch-first lakehouse. PhoenixAI vs. ClickHouse Managed, governed, and built for AI workloads. PhoenixAI vs. Snowflake Real-time freshness vs. warehouse latency.

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 free

About

About PhoenixAI Who we are and why we built this. Partners AWS, Confluent, Databricks, and more. Newsroom Press coverage and announcements.

Join us

Careers Open roles at PhoenixAI. Contact Talk to our team directly.

Customer story

Customer Facing Analytics Meets Agents

Eightfold’s Move from Redshift.

Read the story

Customer-Facing Analytics

Analytics your customers
actually experience as fast.

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.

Book a demo Start free trial

<1s

Dashboard load time

Coinbase production (down from 8 seconds)

10K+

Concurrent users

Sustained QPS

3×

Cost-performance gain

Replacing Apache Druid at Pinterest

The problem

Serving analytics to users is a different problem than internal BI.

cancelWhat breaks at scale

Latency and cost blow up as users grow

Dashboard load times degrade as concurrent users increase
Tenant data isolation requires complex workarounds
Pre-aggregation pipelines multiply as product features grow
Infrastructure cost scales faster than revenue

check_circleWith PhoenixAI

Fast for every user, isolated by design

Sub-second dashboard loads at any concurrency level
Row-level security enforces tenant isolation at the database layer
Multi-warehouse design — isolate workloads without copying data
Predictable cost as user count and data volume scale

Capabilities

Built for products, not just analysts.

The features that matter when analytics is a core part of your product.

speed

Sub-second at any concurrency

Maintain consistent dashboard load times whether you have 100 or 100,000 concurrent users. PhoenixAI’s architecture doesn’t degrade under load.

domain

Native multi-tenant isolation

Cell-level security enforces tenant data boundaries at query execution time. Multi-warehouse design adds hard compute isolation so noisy tenants can’t starve quiet ones.

update

Live data in product dashboards

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.

api

Build with standard SQL

Standard JDBC/ODBC, REST API, and MySQL-compatible wire protocol. Integrate PhoenixAI into your application stack without changing your query patterns.

Fits your application stack

PhoenixAI connects to the tools product engineering teams already use for data pipelines, embedding, and visualization.

Data ingestion

Apache KafkaApache FlinkChange Data CaptureREST API

Application integration

JDBC / ODBCMySQL protocolREST API

Embedded analytics

SupersetMetabaseGrafanaCustom UI

In production

Real results from teams building on PhoenixAI.

“The migration reduced the p90 latency by 50% with only 32% of the instances required by the previous set up. This resulted in a 3-fold increase in cost-performance efficiency. The data ingestion process was also streamlined, achieving a data freshness of just 10 seconds.”

P

Pinterest

PhoenixAI customer

10s

data freshness

Build faster analytics for your users.

Tell us your use case. We’ll show you what PhoenixAI looks like for your product.

Book a demo arrow_forward Start free trial