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Oct 21 - Webinar - Optimizing Data Latency and Query Latency in Open Table Formats : RSVP Here
Sub-second queries on Apache Iceberg and Delta Lake

Scan less data
answer in milliseconds

StarTree indexes your open tables in place, and narrows every query to the pages that hold the answer, not the partitions that might
StarTree vs Trino vs ClickHouse.... on Iceberg Tables
PROVEN to BE
39x
faster than ClickHouse
AVERAGE
15x
cheaper per query
Read the full benchmark →
Sub-second, HIGH concurrency, LOW COST, FRESH DATA

Real-time analytics
for applications and agents
that can't wait
Fresh Data

Events are queryable on arrival and stay in milliseconds at p99 as concurrency climbs into the tens of thousands.
Sub-second, HIGH concurrency, LOW COST

Real-time analytics engine
for
Agent Driven Decisions

StarTree, powered by Apache Pinot, is the real-time analytics query engine that delivers predictable sub-second latency on streaming or lakehouse data — at high concurrency and scale,
Apache Pinot powers:
Goldman Sachs
Slack
Wix
Wix
Walmart
Uber
Together.ai
Stripe
DoorDash
CASE STUDIES

Proven to deliver for analytic apps at scale

Apache Pinot and StarTree power high-concurrency, low-latency analytics applications at many of the worlds leading companies.

Scaling Real-Time Analytics at Angel One with Apache Pinot

Angel One, a prominent Indian financial services platform, has successfully leveraged Apache Pinot to handle high-capacity loads and deliver ultra-fast query times across its diverse business verticals. By implementing Pinot, Angel One has resolved previously difficult analytical challenges, enabling real-time …

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100k
Transactions per second
2 million
Queries per day
<100ms
p99 query latency

Webex Meets Unprecedented Analytics Demand with Apache Pinot

Seeking a real-time analytics solution to meet spiking customer demand for insights in the pandemic era, Webex chose Apache Pinot over Clickhouse and Elasticsearch.

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100 tb
data daily
100+
production nodes
4x faster
queries

MiQ Reinvents Programmatic Campaign Building with a Unified, AI-Driven Audiences Platform

MiQ’s Audiences engine is powered by a modern data stack designed for speed, scale, and flexibility. MiQ replaced Amazon Athena with StarTree, a real-time analytics engine built on Apache Pinot, to support the platform’s advanced search and indexing capabilities.

Learn more
~2 second
Segment listing latency
40-80% Acceleration
on metric calculations
Query Caching Eliminated
Due to performance gains
See more case studies
BUILT ON APACHE PINOT

Low latency or open tables?
You don't have to choose

StarTree is built on Apache Pinot, the engine designed for high-concurrency, low-latency queries at scale. Now you can get Pinot's index-first performance without giving up the lakehouse as your source of truth.
BUILT ON APACHE PINOT

When analytical queries become part of a production application.

StarTree is built on Apache Pinot to serve applications where the analytical system has to stay fresh, handle large query volumes, and maintain predictable low latency as the workload grows.
INTERACTIVE SPEED

Millisecond query response 

Serve dashboards or AI queries with near instant responses — even at high volume — across a wide variety of data types including JSON, vector, timestamps and geospatial.
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Stripe uses Apache Pinot to power dashboards with a 70ms p99 latency at 10k QPS.
FRESH, LOW-LATENCY RESULTS

Live results on changing data

Pinot servers can consume Kafka directly. StarTree does not require a separate micro-batch service simply to move events into the database. StarTree has been proven to ingest 1M events/s, while also maintaining high throughput, and fresh results for queries.
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Slack uses Pinot to achieve near real-time monitoring of data exfiltration.
High concurrency

When apps serve 10K+ QPS  

Support popular user-facing and agentic applications. StarTree is capable of serving hundreds of thousands of simultaneous queries across huge and changing datasets without performance bottlenecks.
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CrowdStrike supports security analytics handling 25k QPS across 10 clusters
Lower cost per query

Scan less, pay less

Compute cost tracks bytes scanned. StarTree scans fewer of them: partitioning strategies, segment assignment, and query routing all narrow the work before execution starts, so a query touches only the segments that matter and skips the rest. Less data read means less infrastructure to read it.
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Uber migrated from Elastic to Pinot bringing a 70% reduction in infrastructure costs
APACHE PINOT With OPEN TABLES

Sub-second query,
directly on the lakehouse!

StarTree queries directly on data in-place on Iceberg or Delta Lake.
Iceberg or Parquet can remain the authoritative data in object storage while StarTree maintains persistent Pinot indexes over selected fields.

Indexes mean StarTree only has to fetch precisely the data — and only the data — needed to answer a query. Reducing scans, lowering transfer costs and delivering fast responses directly from the lakehouse.

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StarTree vs Trino vs ClickHouse.... on Iceberg Tables
UP TO
39x
faster than ClickHouse
UP TO
21x
faster than Trino
AVERAGE
5x
less data transfer from s3
AVERAGE
15x
cheaper per query
Explore the benchmark →
How it works!

Real indexes,
not just metadata pruning.

By extending Apache Pinot’s index-first architecture to Iceberg, StarTree is able to precisely fetch the data — and only the data — needed to answer a query.  Reducing scans lowers costs and delivers fast responses directly from the lakehouse. Learn More
Same Query. Same Parquet Files. Less Scanning.
Error Code Aggregation
Other Query Engines
Local Storage
Row + Column pruning · full chunk reads
Data Fetched
—
—
VS
StarTree (Apache Pinot)
Local Storage
Row, Column & Page pruning
Data Fetched
—
—
—
Other Lakehouse EnginesStarTree (powered by Pinot)
MechanismPurpose-built indexes (inverted, range, sorted)Iceberg metadata + partition pruning
Reads atFile / row-group levelIndividual Parquet page level
Map / nested columnsFull scan requiredIndexed — same speed as a regular column
Data movementComplete - Full table scansMinimal - Specified by Index

Deployment FLEXIBILITY

Deploy within your own cloud
StarTree manages the service

StarTree offers predictable performance, strong workload boundaries, flexible deployment, controlled networking costs, and 24/7 Slack-first support.

StarTree Cloud

SaaS

Each customer gets an infrastructure-isolated data plane and Pinot cluster. The components responsible for ingestion, query execution, storage, and the operational services required to run the cluster live within that dedicated environment.

Simple pricing with no additional per-GB streaming-ingestion meter on top of that capacity.

Bring your own cloud

BYOC

Our Bring Your Own Cloud (BYOC) model allows the data plane (ingestion, query processing, data management) to live fully in your cloud account while preserving the benefits of a fully managed-service experience.

StarTree operates hundreds of staging and production BYOC environments across AWS, GCP, and Azure

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Bring your own KUBERNETES

BYOK

For highly regulated environments, StarTree can also be deployed behind the firewall—offering full control while still leveraging the power and capabilities of StarTree.

StarTree manages the software lifecycle and provides 24×7 support.

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24/7/365 SUPPORT

A production environment—and a team—that keeps applications fast under pressure.

When you become a StarTree customer, you’re not just adopting a technology, you’ll be getting direct access to a team that consists of many of the founding engineers and committers to Apache Pinot.

StarTree support operates 24/7/365. The team continuously monitors cluster health and can proactively investigate and take corrective action when thresholds are breached. StarTree’s published average first-response time for mission-critical incidents is under 90 seconds.

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StarTree COMPARED TO OSS PINOT

Elevate Apache Pinot with enterprise capabilities

StarTree elevates Apache Pinot with a fully managed, production-grade platform, making real-time analytics easier to scale, faster to deploy, and more cost-efficient to operate

Scalable Upserts

Handle mutable data at scale. Off-heap upsert functionality can handle updates to billions of primary keys per server without sacrificing performance.
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Tiered and External Storage

Get low-latency queries directly on tiered storage, or external tables (Parquet/Iceberg/Delta)  without the need for ingestion pipelines, ETL, or data duplication.
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Simple Administration

Eliminate unnecessary infrastructure costs during idle times with autoscaling; also, ease of cluster, instance, and storage management. 
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Enterprise Security

Additional security features, including RBAC, advanced data encryption, single sign-on, and SOC 2 and ISO 27001 compliance ensure your data is safe and protected.
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Improved data management

StarTree's Data Portal UI adds capabilities and makes it easier to ingest and manage data. Evolve schema, tune performance and backfill data without disrupting operations.
More

Enterprise Integrations

Connect with other enterprise data sources, such as Confluent, Snowflake and Tableau with additional StarTree integrations.
More
CASE STUDIES

Proven to deliver for analytic apps at scale

Apache Pinot and StarTree power high-concurrency, low-latency analytics applications at many of the worlds leading companies.

Real-Time Analytics for the Entire Crypto-economy

Amberdata, a blockchain and crypto market company, relies on StarTree for real-time analytics. With StarTree and Pinot, Amberdata has seen benefits that include faster query performance, a drastic reduction in query SLAs, and reduced infrastructure costs.

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66%
reduced infrastructure costs
350k
insert events per second
Sub-second
query latencies

Analyzing Financial Transactions Using StarTree Cloud

Razorpay – India’s fastest-growing payment processing company – chose StarTree, powered by Apache Pinot, for real-time analytics in its user-facing applications, including its Success Rate and internal monitoring dashboards.

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1-3 seconds
p99 latencies
200 million
events ingested daily
50% savings
for upsert tables costs

How Meesho migrated from pre-aggregated views to raw real-time analytics with Apache Pinot

At Meesho, real-time analytics provides the operational backbone for incident management and product analysis. As their platform scaled, their architectural requirements shifted from monitoring high-level metric aggregates to requiring granular, raw-data exploration. This post details Meesho’s journey from a legacy …

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See more case studies
REQUEST A TRIAL

Benchmark your workloads

Show us your query patterns, QPS, ingestion rates and freshness requirements. We’ll show you how we’d architect it in StarTree.

We can help you:

  • Design a real-time architecture for your ingestion, query, and scale requirements
  • Right-size infrastructure and cut query and storage costs
  • Compare against Clickhouse, ElasticSearch, or other systems
  • Troubleshoot performance issues in an existing Pinot deployment
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