StarTree Pricing

Deploy on your terms.

Choose an operating model that fits your architecture, geography, security posture, and budget. Expert technical support and simple vCPU based pricing.

Public SaaS

Fully managed by StarTree

We run everything: compute, storage, autoscaling, backups, and security in our cloud. Nothing to provision.
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Pay as you go

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Platform, managed service, and infrastructure in one price
Available on AWS, GCP and Azure Marketplaces
Automatic scaling, backups, and security included
Discounted rates for non-production vCPUs
Available in all regions
Includes technical support
Annual and volume pricing available

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Private BYOC

Bring Your Own Cloud

StarTree manages the platform inside your own cloud account. You keep control of the underlying infrastructure.
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Pay as you gO

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Platform and managed service only.
Deployed within your AWS, GCP, or Azure account
Infrastructure billed by your provider.
You control the underlying infrastructure
Data stays in your environment
Includes technical support
Annual and volume pricing available

BYOK

Bring Your Own Kubernetes

Deploy the full StarTree stack inside your own Kubernetes environment, including air-gapped systems, for complete control over infrastructure and data.
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Custom terms for regulated and security-first teams

Runs in your own Kubernetes cluster
Supports fully air-gapped deployments
No delegated data-plane access required
Premium or Standard support available

^** Prices reflect list price. Contact us for volume discounts.

The Bigger Picture

StarTree Reduces Costs

License costs are only one part of the equation. For real-time analytics workloads, the bigger question is often: how much compute, engineering time, data movement, and operational risk can you reduce?

Reduced Compute

The Apache Pinot query engine at the heart of StarTree has been optimized for low-latency, high-concurrency analytics. Customers are able to serve demanding user-facing analytics workloads with less brute-force compute than general-purpose systems.

Scan Less Data

Advanced indexes with page-level on tiered and external storage makes it easier to serve low-latency analytics from the data lake without moving data or stitching together additional pipelines

Improved Performance

Faster analytics is not just cheaper infrastructure; it delivers faster dashboards, fresher operational decisions, better anomoly detection, more responsive internal tools and better engagement for your products.

Operational Efficiency

Spend less engineering time tuning, operating, upgrading, and firefighting your real-time analytics infrastructure. StarTree reduces operational burden with a managed platform, expert support, automation, and production-grade capabilities.

CASE STUDIES

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.

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Sub-millisecond

Query performance

Stabilized

p99 tail latencies

Improved

Operational ROI

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

Enhancing Last Mile Delivery with Apache Pinot at Walmart

Overview Walmart’s last-mile delivery lifecycle is highly complex, involving 20 to 30+ microservices. Because each microservice maintains its own state of a given entity (such as a customer’s order), tracking the complete life cycle of an order as it moves …

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20-30

Microservices

Realtime platform

50%

Reduction in resolution time

Empowering Real-Time Security Visibility at Slack with Apache Pinot

For over three years, Slack has utilized Apache Pinot to power customer-facing dashboards and analytics. Historically, these analytics relied on batch processing, where data flowed through Spark into S3 and was ingested into Pinot offline servers. This batch-based approach resulted …

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<1s

Ingestion Latency

100%

Data Accuracy

<10s

Query Latency SLA

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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Common Cost Saving Scenarios

StarTree delivers value by serving high-concurrency queries efficiently, performance on tiered storage, avoiding unnecessary data movement, reducing operational overhead, and improving performance without overprovisioning.

From OSS Pinot to Managed Pinot

Improved capabilities, superior performance and lower maintenance costs help organizations make the most from Apache Pinot

Disaggregated Observability

As part of a disagregated observability stack, organizations find big savings in running StarTree for customized log and event analysis at scale

Real-time analytics on the lakehouse

No secondary system data store needed. StarTree helps organizations consolidate query onto a single source of truth with open table formats - all while delivering high performance.

Agent-facing analytics

As agents start to generate record query loads, StarTree provides the tooling to keep responses fast, timely and cost-efficient.

Let's talk!

Real-time analytics costs vary heavily by data volume, ingestion rate, query concurrency, latency goals, deployment model, and support requirements.

Talk with us to develop a customized plan to meet your goals:

A review of your workload and architecture
Identify main cost drivers from your current setup
Recommended deployment model
A pricing estimate
Opportunities for compute, storage, data-transfer, or operational savings