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Elastic Launches Elasticsearch Service on MS Azure

Elastic, the company behind Elasticsearch and the Elastic Stack, announced the launch of Elasticsearch Service on Azure.

Organizations that have standardized on Azure will now be able to enjoy the convenience of a fully managed Elasticsearch service, from the creators of Elasticsearch, on their preferred cloud platform — something that was not previously possible.

Elasticsearch Service users on Azure can now deploy fully hosted Elasticsearch and Kibana from the creators of the software all with the click of a button. Existing Elasticsearch Service customers can launch deployments on Azure in their existing accounts, and new users can get started with a free 14-day trial of the Elasticsearch Service.

The Elasticsearch Service on Elastic Cloud is the official hosted Elasticsearch and Kibana service, created and supported by Elastic. It offers features — like Elastic APM, SIEM, Maps, Canvas, machine learning and moree. Users can wield Elasticsearch and Kibana with confidence, knowing they always have the latest release and security patches and can upgrade their deployments with a single click and zero downtime. And now, all these benefits are available to Azure customers.

“The developer and open source focus that both companies share have made this integration a very natural fit. Microsoft’s commitment to choice is evident in their developer experience on Microsoft Azure, and mirrors our own,” said Shay Banon, founder and CEO of Elastic.

Scott Guthrie, EVP of Cloud + AI, Microsoft Corp. said, “As customers adopt cloud services, having a solution for their most important needs such as search, logging, observability, and security of their critical applications, will be a key advantage. The focus on developer choice and managed services that both Microsoft and Elastic share benefit our mutual customers.”

The public beta is accessible to all Elasticsearch Service customers and trial users delivered from two Azure regions — East US 2 in Virginia and West Europe in the Netherlands — and includes the full set of Elasticsearch Service features. During the beta period, Elastic technical support is available. Later in 2019, Elastic intends to move the service to generally available and add mission-critical support service levels.

The engineering teams at Elastic and Microsoft have collaborated on carefully benchmarking and selecting the optimal VMs to support a variety of Elastic use cases with different performance profiles when running Elasticsearch Service on Azure. This effort has resulted in four deployment templates that optimize Elasticsearch Service on Azure:

- High I/O: Perfect for search or general use cases, this template runs on top of L-series VMs that have local NVMe SSD optimized for high read/writes.

- Hot/Warm: A powerful architecture perfect for logging and time series use cases, combining NVMe SSD for fast access and a 1:100 RAM:Disk ratio with HDD storage for longer cost-effective retention.

- High CPU: Often used for scripting, calculations, ingest processing or other compute-intensive use cases, this template offers double the CPU.

- High Memory: Offers search use cases a cost-effective option for lower data volumes.

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Elastic Launches Elasticsearch Service on MS Azure

Elastic, the company behind Elasticsearch and the Elastic Stack, announced the launch of Elasticsearch Service on Azure.

Organizations that have standardized on Azure will now be able to enjoy the convenience of a fully managed Elasticsearch service, from the creators of Elasticsearch, on their preferred cloud platform — something that was not previously possible.

Elasticsearch Service users on Azure can now deploy fully hosted Elasticsearch and Kibana from the creators of the software all with the click of a button. Existing Elasticsearch Service customers can launch deployments on Azure in their existing accounts, and new users can get started with a free 14-day trial of the Elasticsearch Service.

The Elasticsearch Service on Elastic Cloud is the official hosted Elasticsearch and Kibana service, created and supported by Elastic. It offers features — like Elastic APM, SIEM, Maps, Canvas, machine learning and moree. Users can wield Elasticsearch and Kibana with confidence, knowing they always have the latest release and security patches and can upgrade their deployments with a single click and zero downtime. And now, all these benefits are available to Azure customers.

“The developer and open source focus that both companies share have made this integration a very natural fit. Microsoft’s commitment to choice is evident in their developer experience on Microsoft Azure, and mirrors our own,” said Shay Banon, founder and CEO of Elastic.

Scott Guthrie, EVP of Cloud + AI, Microsoft Corp. said, “As customers adopt cloud services, having a solution for their most important needs such as search, logging, observability, and security of their critical applications, will be a key advantage. The focus on developer choice and managed services that both Microsoft and Elastic share benefit our mutual customers.”

The public beta is accessible to all Elasticsearch Service customers and trial users delivered from two Azure regions — East US 2 in Virginia and West Europe in the Netherlands — and includes the full set of Elasticsearch Service features. During the beta period, Elastic technical support is available. Later in 2019, Elastic intends to move the service to generally available and add mission-critical support service levels.

The engineering teams at Elastic and Microsoft have collaborated on carefully benchmarking and selecting the optimal VMs to support a variety of Elastic use cases with different performance profiles when running Elasticsearch Service on Azure. This effort has resulted in four deployment templates that optimize Elasticsearch Service on Azure:

- High I/O: Perfect for search or general use cases, this template runs on top of L-series VMs that have local NVMe SSD optimized for high read/writes.

- Hot/Warm: A powerful architecture perfect for logging and time series use cases, combining NVMe SSD for fast access and a 1:100 RAM:Disk ratio with HDD storage for longer cost-effective retention.

- High CPU: Often used for scripting, calculations, ingest processing or other compute-intensive use cases, this template offers double the CPU.

- High Memory: Offers search use cases a cost-effective option for lower data volumes.

The Latest

For many B2B and B2C enterprise brands, technology isn't a core strength. Relying on overly complex architectures (like those that follow a pure MACH doctrine) has been flagged by industry leaders as a source of operational slowdown, creating bottlenecks that limit agility in volatile market conditions ...

FinOps champions crucial cross-departmental collaboration, uniting business, finance, technology and engineering leaders to demystify cloud expenses. Yet, too often, critical cost issues are softened into mere "recommendations" or "insights" — easy to ignore. But what if we adopted security's battle-tested strategy and reframed these as the urgent risks they truly are, demanding immediate action? ...

Two in three IT professionals now cite growing complexity as their top challenge — an urgent signal that the modernization curve may be getting too steep, according to the Rising to the Challenge survey from Checkmk ...

While IT leaders are becoming more comfortable and adept at balancing workloads across on-premises, colocation data centers and the public cloud, there's a key component missing: connectivity, according to the 2025 State of the Data Center Report from CoreSite ...

A perfect storm is brewing in cybersecurity — certificate lifespans shrinking to just 47 days while quantum computing threatens today's encryption. Organizations must embrace ephemeral trust and crypto-agility to survive this dual challenge ...

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 ...