Elastic, the company behind Elasticsearch and the Elastic Stack, announced the next evolution in the Elasticsearch Service on Elastic Cloud to solve more use cases, operate at a larger scale, and give customers even more capabilities to customize their deployments.
Now known simply as the Elastic Elasticsearch Service, this is the only service offered and supported by Elastic.
New features include optimized deployment templates for the most common use cases, more hardware choices, support for hot-warm architectures, machine learning, and a revised pricing model, including a new free tier to help new customers get started.
"Elastic customers are putting more and more data into the Elasticsearch Service for their mission-critical use cases, and we've responded by making it more efficient, more customizable, more cost effective and more powerful," said Shay Banon, Elastic founder and CEO. "Elastic's engineering mission has always been to make complex things simple. The latest version of the Elasticsearch Service is doing that and more, making it easier for companies to gain immediate insights from large volumes of data across some of the most challenging search and analytics, logging, or security scenarios."
The key new features of the Elasticsearch Service include:
- Optimized Deployments Based on Use Case. From logging to security analytics to application search, companies are using Elasticsearch to solve some of the largest search problems. Customers now have the option to select out-of-the-box deployment templates to optimize the underlying hardware for their use cases based on the expected workload - I/O, CPU, or memory intensive.
- Hot-Warm Deployments with Index Lifecycle Management. One of the most powerful features of the new Elasticsearch Service is the hot-warm architecture template, which enables organizations to ingest and query current data quickly, while keeping older, longer-term data on denser and more cost-effective hardware. Customers can now quickly deploy this architecture in minutes and automate index lifecycle management without having to modify any configuration files.
- Dedicated Master Nodes. Organizations use the Elasticsearch Service to solve problems at petabyte scale and need their Elasticsearch clusters to scale with them. The Elasticsearch Service now provides the ability to deploy dedicated master nodes to better support larger deployments.
- New User Console that Simplifies Customization. The Elasticsearch Service has a new UI to make it easy for customers to define and configure all of the Elastic Stack components supported by the Elasticsearch Service from one page. For example, new sliders enable customers to scale Kibana instances or add more RAM to their master nodes with ease.
- Machine Learning for All Deployments. Thousands of Elastic customers already use Elastic's machine learning features to detect anomalous behavior, and reduce time to root cause analysis. Customers can now quickly add machine learning features to each of their Elasticsearch Service deployments and start gaining insights immediately.
- New Reduced Pricing. Elastic has made the Elasticsearch Service more accessible with a new lower starting price and a free tier. Pricing now follows a simple linear model based on size and number of availability zones. Additionally, each deployment can now include multiple types of nodes and instances, such as Elasticsearch data nodes, Elasticsearch master nodes, and Kibana instances, with each instance priced separately. For more information on the new pricing and the free tier, along with frequently asked questions, please visit Elastic's pricing blog.
- Elastic Rebrands its Software-as-a-Service (SaaS) Solutions. In addition to the new Elasticsearch Service naming, Elastic also announced the rebranding of the Swiftype hosted solutions to Elastic Site Search Service and Elastic App Search Service. Elastic Cloud is a collection of all Elasticsearch-powered SaaS solutions from Elastic.
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