Skip to main content

Elastic Launches Elastic Cloud Enterprise (ECE)

Elastic, the company behind Elasticsearch, and the Elastic Stack, announced the general availability of Elastic Cloud Enterprise (ECE).

This new product will enable organizations to centralize the management, monitoring, and provisioning of multiple Elastic Stack clusters; easily deploy X-Pack features such as security, alerting, monitoring, Graph, reporting, and machine learning; and run Elasticsearch, Kibana, and X-Pack on physical hardware, in a virtual environment, private cloud, in a private zone in a public cloud, or in a public cloud, such as, Google Cloud, Azure, or AWS.

“As organizations have the discovered the simplicity of our products to solve their most complex data challenges, they’ve moved from a few clusters to many single Elastic Stack environments,” said Shay Banon, Elastic Founder and CEO. “By distributing usage across clusters, use cases, and teams into a single deployment, ECE gives organizations the scale and control to manage hundreds of Elastic Stack deployments as one.”

ECE is part of Elastic’s vision to make complex things simple, and will allow organizations that adopt it to manage and scale-out their mission-critical deployments with a few clicks. By giving users and their organizations a single console and user interface, ECE simplifies provisioning, maintenance, and resource management; provides out-of-the-box security for every managed cluster for protecting an organization’s data and ensuring compliance; and automates processes, such as, cluster management, upgrades, and version control. Based on Elastic Cloud, and Elastic’s experience hosting and managing thousands of clusters over the past two years, ECE extends capabilities to organizations who want the control to manage their own Elastic Stack environments.

It includes, the ability to:

- Create and deploy new Elastic Stack clusters

- Orchestrate all the features of Elasticsearch, Kibana, and X-Pack from a single console

- Scale clusters up (or down) based on data volumes

- Turn on security features like authentication, role-based access control, and encryption

- Host multiple versions of the Elastic Stack

- Upgrade clusters to the latest versions via an intuitive admin interface

- Monitor clusters and endpoints in a central dashboard

- Offer search/logging/monitoring/security-as-a-service internally

- Establish a Elastic Center of Excellence across engineering, architecture, devops, IT security, and compliance teams

The Latest

In MEAN TIME TO INSIGHT Episode 14, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud network observability... 

While companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending ...

Telecommunications is expanding at an unprecedented pace ... But progress brings complexity. As WanAware's 2025 Telecom Observability Benchmark Report reveals, many operators are discovering that modernization requires more than physical build outs and CapEx — it also demands the tools and insights to manage, secure, and optimize this fast-growing infrastructure in real time ...

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

Elastic Launches Elastic Cloud Enterprise (ECE)

Elastic, the company behind Elasticsearch, and the Elastic Stack, announced the general availability of Elastic Cloud Enterprise (ECE).

This new product will enable organizations to centralize the management, monitoring, and provisioning of multiple Elastic Stack clusters; easily deploy X-Pack features such as security, alerting, monitoring, Graph, reporting, and machine learning; and run Elasticsearch, Kibana, and X-Pack on physical hardware, in a virtual environment, private cloud, in a private zone in a public cloud, or in a public cloud, such as, Google Cloud, Azure, or AWS.

“As organizations have the discovered the simplicity of our products to solve their most complex data challenges, they’ve moved from a few clusters to many single Elastic Stack environments,” said Shay Banon, Elastic Founder and CEO. “By distributing usage across clusters, use cases, and teams into a single deployment, ECE gives organizations the scale and control to manage hundreds of Elastic Stack deployments as one.”

ECE is part of Elastic’s vision to make complex things simple, and will allow organizations that adopt it to manage and scale-out their mission-critical deployments with a few clicks. By giving users and their organizations a single console and user interface, ECE simplifies provisioning, maintenance, and resource management; provides out-of-the-box security for every managed cluster for protecting an organization’s data and ensuring compliance; and automates processes, such as, cluster management, upgrades, and version control. Based on Elastic Cloud, and Elastic’s experience hosting and managing thousands of clusters over the past two years, ECE extends capabilities to organizations who want the control to manage their own Elastic Stack environments.

It includes, the ability to:

- Create and deploy new Elastic Stack clusters

- Orchestrate all the features of Elasticsearch, Kibana, and X-Pack from a single console

- Scale clusters up (or down) based on data volumes

- Turn on security features like authentication, role-based access control, and encryption

- Host multiple versions of the Elastic Stack

- Upgrade clusters to the latest versions via an intuitive admin interface

- Monitor clusters and endpoints in a central dashboard

- Offer search/logging/monitoring/security-as-a-service internally

- Establish a Elastic Center of Excellence across engineering, architecture, devops, IT security, and compliance teams

The Latest

In MEAN TIME TO INSIGHT Episode 14, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud network observability... 

While companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending ...

Telecommunications is expanding at an unprecedented pace ... But progress brings complexity. As WanAware's 2025 Telecom Observability Benchmark Report reveals, many operators are discovering that modernization requires more than physical build outs and CapEx — it also demands the tools and insights to manage, secure, and optimize this fast-growing infrastructure in real time ...

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...