Hosted Elasticsearch Service

Launch our supported, fully-managed, RESTful search application instantly.
Scale up or down and pay as you go.

More Affordable at Any Scale

Get affordable prices for large datasets with free 24/7 premium support and maintenance.

Single Endpoint Smart Scaling

Resize and replicate any cluster from your dashboard without switching endpoints.

A Perfect, Dedicated Fit

Adjust RAM and processors independently to align with your data requirements.

Powering Production Elasticsearch
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Free 24/7 support and maintenance for every customer

With Qbox, you can count on your clusters being up when you need them. You're fully backed with a SLA, a dedicated support team, and fully managed upgrades and migrations.

  • Fully managed upgrades and migrations

    Upgrading to the newest Elasticsearch version has never been so easy. Our support team will work with you and follow up to make sure there are no issues with your cluster upgrade.

  • Automatic backups and simple restore

    Have an issue and need to roll back? We're here to help. Qbox will work with you to quickly restore your data with minimal downtime.

  • Add or remove plugins on demand

    Want to add the newest plugin on the block to your existing Elasticsearch cluster? With Qbox, it's as easy as sending a ticket to our support team. We'll take care of the rest.

Available on multiple cloud platforms in over 47 regions

Qbox is the only hosted Elasticsearch provider that allows you to choose both the location and the cloud platform of your cluster, which lowers response times significantly.

Qbox makes it easy for us to provision an Elasticsearch cluster without wasting time on all the details of cluster configuration.

Chris Olivares, Software Engineer

Infinitely Scalable

Launch a 1-node Elasticsearch cluster for testing and then scale to a 10-node production cluster with ease.

Unrestricted API

Whether it’s 10 thousand or 10 billion documents you need to index, we’ve got it covered.

VPC Peering

Increase your security and throughput with VPC peering. Available on clusters on the AWS platform.

Kibana, Logstash, and Elasticsearch. Get the ELK stack, a suite of powerful search analytics for your cluster.

Hook your cluster into the ELK stack to get detailed Kibana and Logstash analytics. Let our expert support team help design your ideal ELK Stack infrastructure.

Read more

Elasticsearch News & Tutorials

Learn more about Qbox Elasticsearch and how to get the most out of shards, aggregations, and more.

  • The Authoritative Guide to Elasticsearch Performance Tuning (Part 3)

    Editors Note: This post is part 3 of a 3-part series on tuning Elasticsearch performance. Part 1 can be found here and Part 2 can be found here.

    Shard Allocation, Rebalancing and Awareness are very crucial and important from the perspective of preventing any data loss or to prevent the painful Cluster Status: RED (a sign alerting that the cluster is missing some primary shards). Apart from shard allocation, everyone loves to tweak threadpools. For whatever reason, it seems people cannot resist increasing thread counts. 

    The default threadpool settings in Elasticsearch are very sensible. For all threadpools (except search) the threadcount is set to the number of CPU cores. If we have eight cores, we can be running only eight threads simultaneously. It makes sense to assign only eight threads to any particular threadpool.

    In this tutorial, we’ll be focussing on Shard Allocation and Threadpool Configuration settings to keep our cluster's health green and improve overall performance.

  • The Authoritative Guide to Elasticsearch Performance Tuning (Part 2)

    Editors Note: This post is part 2 of a 3-part series on tuning Elasticsearch performance. Part 1 can be found here.

    If we are using Elasticsearch mainly for search, or if search is a customer-facing feature that is key to our organization, we should monitor query latency and take action if it surpasses a threshold. 

    It’s important to monitor relevant metrics about queries and fetches that can help us determine how our searches perform over time. For example, we may want to track cluster's health to provide high availability or track spikes and long-term increases in query requests, so that we can be prepared to tweak our configuration to optimize for better performance and reliability.

    In this tutorial, we continue focusing on performance tuning strategies to keep our cluster's health green and improve overall performance.

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