
Elastic released Elastic Cloud Enterprise (ECE) version 2.0.
ECE was designed to provide a centralized and easy way to provision, manage, monitor and scale multiple Elastic Stack deployments, giving organizations the ability to control all their deployments from a single place. ECE 2.0 elevates the control and administrative ease with many new features such as host tagging, customizable deployment templates including hot-warm architecture, automated index curation, and more.
"For Elastic customers who choose to adopt the Elastic Stack for new cases and larger deployments, ECE makes the complexity of managing multiple Elastic Stack environments simple," said Shay Banon, co-founder and CEO at Elastic. "ECE 2.0 introduces new features that will give our customers greater control and more scale as they utilize our technology to address newer and more advanced use cases.”
As an example, consider an Elastic customer starting with a single cluster servicing a single use case, such as centralized logging, and growing that use case over time. As the initial logging use case grows to multiple teams or divisions, ECE enables the customer to establish a centralized “logging as a service” for their entire organization. In addition, if the customer chooses to expand into new use cases like application or site search, APM, metrics, business analytics, and security analytics, ECE provides the foundation to manage all deployments, including multiple tenants, use cases, data sources and services with a single product aligned with the customer’s IT, security, backup, and compliance policies and procedures. ECE provides an Elastic Stack focused orchestration platform that lets the customer manage and control their entire fleet of deployments from a single place - via a simple user interface (UI) or a comprehensive application programming interface (API).
Some of the new features and benefits of ECE 2.0 include:
- Deployment Control and Optimization - New host tagging and tag filtering features help users control how deployments are mapped to their underlying hardware to optimize for both performance and cost
- Templated Architectures and Provisioning - ECE has new instance configuration and deployment templates for common architecture patterns to streamline and control how new clusters are structured and provisioned
- Hot-Warm Deployment Templates - ECE’s hot-warm deployment template and automated index curation make it easy to deploy and scale hot-warm clusters, a common topology for time-series use cases like logging and metrics
- Anomaly Detection and Forecasting - ECE now comes with new dedicated machine learning nodes to let users easily add anomaly detection and forecasting capabilities to their Elasticsearch clusters
- SAML Security Authentication - Users have the option to secure Elasticsearch clusters launched via ECE with SAML authentication using their own preferred SAML identity provider
The Latest
In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...
In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...
Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...
In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ...
Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...
Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...
Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...
The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...
The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...
In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...
