Scan less data
answer in milliseconds
Real-time analytics
for applications and agents
that can't wait
Fresh DataEvents are queryable on arrival and stay in milliseconds at p99 as concurrency climbs into the tens of thousands.
Real-time analytics engine
forAgent Driven DecisionsStarTree, 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,
Proven to deliver for analytic apps at scale
Uber serves real-time app crash analytics while saving $2M+ with Apache Pinot
For a company like Uber, real-time data is the lifeblood of both their customer-facing and internal services. Customers rely on real-time data to get rides and grab food at their convenience. Internal teams also depend on up-to-date data to power …
From pre-aggregation to fresh real-time query at 7Signal
7SIGNAL — a provider of enterprise Wi-Fi optimization that includes network optimization, monitoring, and diagnostics — reduced data freshness delays from 23 minutes to
How Grab Built a Next-Gen Observability Stack for Its Rideshare Marketplace
One of Southeast Asia’s largest makers of ‘super apps’ – is powering real-time observability in its rideshare marketplace with Apache Pinot. Its integrated platform supports operations across more than 300 cities with one-second response times. In this talk from Real-time …
Low latency or open tables?
You don't have to choose
When analytical queries become part of a production application.
Millisecond query response
Live results on changing data
When apps serve 10K+ QPS
Scan less, pay less
Sub-second query,
directly on the lakehouse!
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.
More
Real indexes,
not just metadata pruning.
| Other Lakehouse Engines | StarTree (powered by Pinot) | |
|---|---|---|
| Mechanism | Purpose-built indexes (inverted, range, sorted) | Iceberg metadata + partition pruning |
| Reads at | File / row-group level | Individual Parquet page level |
| Map / nested columns | Full scan required | Indexed — same speed as a regular column |
| Data movement | Complete - Full table scans | Minimal - 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.
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.
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
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.
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.
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
Tiered and External Storage
Simple Administration
Improved data management
Enterprise Integrations
Six workloads that fit better
on StarTree.
Customer & Agent-Facing Apps
Observability
Real-Time + Historical Reporting
Log Analytics
Interactive OLAP
Vector Similarity Search
Proven to deliver for analytic apps at scale
BurdaForward: Analyzing Quality News Performance Using Real-Time Analytics
BurdaForward, a leading German digital news delivery organization, supports real-time analytics using StarTree Cloud for advertisers, media brands, and consumers.
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 …
How Primer.AI improved performance and ROI by moving from OSS Apache Pinot to StarTree Cloud
Should you manage OSS Apache Pinot yourself, or turn to professionally managed Apache Pinot on StarTree Cloud? This was the question Primer.ai asked themselves when their customer base and platform usage grew, and they started to encounter scaling challenges.
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