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Version 6.5 of Elastic Stack Released

Elastic announced the general availability of version 6.5 of the Elastic Stack.

This release highlights continued investment in solutions for logging, metrics, and application performance management (APM), and foundational Elastic Stack features that benefit multiple use cases, such as app search, site search, enterprise search, business analytics, and security analytics.

This latest release aims to help users of the Elastic Stack store, manage, and analyze their data more efficiently, scale their operations, and improve how they interact with their data through new user interfaces (UIs), visualizations, and dashboards. 6.5 contains the initial release of our Infrastructure solution and a dedicated UI for log viewing, giving observability teams a more efficient and streamlined way of navigating their data.

It also introduces distributed tracing support and the general availability of Java and Go agents in Elastic APM.

This release also launches a number of highly awaited Kibana features - Canvas, a powerful way for users to build pixel-perfect infographic experiences on live data and Spaces, a new way for users to group and organize their dashboards and visualizations into “spaces”, and secure access by roles like security, operations, finance, and marketing.

“Our focus has always been to make users successful in whatever ways they decide to use our technology, on-premises or in the cloud, using the Elastic Stack for a single use case like security or logging, or for many others,” said Shay Banon, founder and CEO of Elastic. “6.5 is a MAJOR.MINOR release, showcasing our relentless execution across multiple usage patterns of our technology, as well as foundational product capabilities that are critical across a variety of use cases.”

The Elastic Stack has always enabled powerful ways to analyze logs and metrics from IT infrastructure. The 6.5 release makes it even easier for users to gain visibility into operational data. The introduction of an Infrastructure monitoring solution provides a 10,000-foot view of physical, virtual, or container-based infrastructure, and allows users to drill into the details of any host, pod, or container, while a new UI for log analysis provides an advanced log viewer with live tail, even on filtered data. 6.5 also includes the first release of Functionbeat, which adds new monitoring capabilities for cloud-based serverless infrastructure like AWS Lambda for collecting data from sources such as CloudWatch Logs and Simple Queue Service (SQS).

Elastic APM gives users the ability to combine APM data with infrastructure logs, server metrics, and security events to identify bottlenecks and issues faster, all using the same open source foundation that they are already familiar with. In 6.5, we released distributed tracing, a beta feature that allows users to string together transactions and visualize the performance of requests as they flow through an organization's infrastructure. We also announced the general availability of agents for popular programming languages, Go and Java.

Kibana has found wide adoption in NOCs, SOCs and boardrooms to visualize and explore data stored in Elasticsearch. In 6.5, we’ve released two major features that make it easier to share and collaborate within Kibana. Canvas allows users to create live data presentations and pixel-perfect workpads in new interactive ways for their teams, including operations, business, and executive users. Kibana users with 100s and 1,000s of dashboards and visualizations, can use the new Kibana spaces feature to group collections of Kibana objects into “spaces” such as, a logging space, marketing space, etc. and then define role-based access permissions and privileges for each space.

Other significant developments in the Elastic Stack 6.5 include a major focus on usability, with enhancements and new features like:

- Cross-cluster replication (beta). Users can replicate indices from one cluster to another cluster. Use cases include high availability/disaster recovery, geo proximity/data locality, and centralized search/analysis.

- Rollup support in Kibana (beta). Now it is easier than ever to dramatically shrink the storage requirements for metrics data, by summarizing the data intelligently at lower granularity. 6.5 includes support for configuring rollup jobs and for visualizing the rolled up data.

- Interactive file import (beta). This free feature in the Machine Learning application allows the easy import of data into the Elastic Stack. When uploading a file, the file type is automatically detected, and grok patterns are suggested dynamically. This dramatically simplifies the process of onboarding new data sources or importing specific data sets.

- New machine learning functionality. Improvements in multi-bucket anomalies; now with improved underlying modeling methods, anomalies can be detected more accurately.

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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 ...

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Version 6.5 of Elastic Stack Released

Elastic announced the general availability of version 6.5 of the Elastic Stack.

This release highlights continued investment in solutions for logging, metrics, and application performance management (APM), and foundational Elastic Stack features that benefit multiple use cases, such as app search, site search, enterprise search, business analytics, and security analytics.

This latest release aims to help users of the Elastic Stack store, manage, and analyze their data more efficiently, scale their operations, and improve how they interact with their data through new user interfaces (UIs), visualizations, and dashboards. 6.5 contains the initial release of our Infrastructure solution and a dedicated UI for log viewing, giving observability teams a more efficient and streamlined way of navigating their data.

It also introduces distributed tracing support and the general availability of Java and Go agents in Elastic APM.

This release also launches a number of highly awaited Kibana features - Canvas, a powerful way for users to build pixel-perfect infographic experiences on live data and Spaces, a new way for users to group and organize their dashboards and visualizations into “spaces”, and secure access by roles like security, operations, finance, and marketing.

“Our focus has always been to make users successful in whatever ways they decide to use our technology, on-premises or in the cloud, using the Elastic Stack for a single use case like security or logging, or for many others,” said Shay Banon, founder and CEO of Elastic. “6.5 is a MAJOR.MINOR release, showcasing our relentless execution across multiple usage patterns of our technology, as well as foundational product capabilities that are critical across a variety of use cases.”

The Elastic Stack has always enabled powerful ways to analyze logs and metrics from IT infrastructure. The 6.5 release makes it even easier for users to gain visibility into operational data. The introduction of an Infrastructure monitoring solution provides a 10,000-foot view of physical, virtual, or container-based infrastructure, and allows users to drill into the details of any host, pod, or container, while a new UI for log analysis provides an advanced log viewer with live tail, even on filtered data. 6.5 also includes the first release of Functionbeat, which adds new monitoring capabilities for cloud-based serverless infrastructure like AWS Lambda for collecting data from sources such as CloudWatch Logs and Simple Queue Service (SQS).

Elastic APM gives users the ability to combine APM data with infrastructure logs, server metrics, and security events to identify bottlenecks and issues faster, all using the same open source foundation that they are already familiar with. In 6.5, we released distributed tracing, a beta feature that allows users to string together transactions and visualize the performance of requests as they flow through an organization's infrastructure. We also announced the general availability of agents for popular programming languages, Go and Java.

Kibana has found wide adoption in NOCs, SOCs and boardrooms to visualize and explore data stored in Elasticsearch. In 6.5, we’ve released two major features that make it easier to share and collaborate within Kibana. Canvas allows users to create live data presentations and pixel-perfect workpads in new interactive ways for their teams, including operations, business, and executive users. Kibana users with 100s and 1,000s of dashboards and visualizations, can use the new Kibana spaces feature to group collections of Kibana objects into “spaces” such as, a logging space, marketing space, etc. and then define role-based access permissions and privileges for each space.

Other significant developments in the Elastic Stack 6.5 include a major focus on usability, with enhancements and new features like:

- Cross-cluster replication (beta). Users can replicate indices from one cluster to another cluster. Use cases include high availability/disaster recovery, geo proximity/data locality, and centralized search/analysis.

- Rollup support in Kibana (beta). Now it is easier than ever to dramatically shrink the storage requirements for metrics data, by summarizing the data intelligently at lower granularity. 6.5 includes support for configuring rollup jobs and for visualizing the rolled up data.

- Interactive file import (beta). This free feature in the Machine Learning application allows the easy import of data into the Elastic Stack. When uploading a file, the file type is automatically detected, and grok patterns are suggested dynamically. This dramatically simplifies the process of onboarding new data sources or importing specific data sets.

- New machine learning functionality. Improvements in multi-bucket anomalies; now with improved underlying modeling methods, anomalies can be detected more accurately.

The Latest

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

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 ...