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Elastic Delivers Cloud-Connected AutoOps for Self-Managed Elasticsearch Users

Elastic announced that AutoOps is now available for the first time for self-managed enterprise users—at no additional cost.

By offloading the heavy lifting to Elastic Cloud, self-managed users gain powerful diagnostic benefits without the overhead of associated infrastructure, also gaining operational insights that improve resource efficiency and lower hardware costs.

“We are always looking to simplify Elasticsearch management to allow developers to focus on building new features instead of troubleshooting issues,” said Ajay Nair, general manager, Platform, at Elastic. “Teams working in self-managed environments can now access the same benefits of AutoOps experienced by Elastic Cloud users.”

AutoOps simplifies cluster management with zero additional overhead. The first in a roadmap of Elastic Cloud connected services for self-managed environments, it runs through a lightweight integration that securely streams operational metadata, such as shard allocations, query latencies, and node utilization to Elastic Cloud. The cloud-powered service processes this telemetry to deliver real-time issue detection and resolution, while the underlying customer data never leaves the self-managed deployment.

Key features for AutoOps include:

  • Simplified cluster management: Delivers real-time cluster insights and automatic detection of ingestion bottlenecks, shard imbalance, and mapping errors so teams can resolve problems before they impact performance.
  • Reduced operational overhead: Eliminates the need to provision and manage a dedicated monitoring cluster. AutoOps data is stored in Elastic's infrastructure and managed by Elastic, freeing teams from additional infrastructure costs and administrative tasks.
  • Cost and resource optimization: Highlights underutilized nodes and inefficient indices, providing clear methods to improve resource utilization and reduce hardware costs.
  • Better support: Elastic Support engineers’ read-only access to AutoOps diagnostics makes support responses faster, as well as more precise resolutions to support tickets.

AutoOps users get the data sovereignty of a self-managed deployment, combined with simplified, proactive operational insights. The platform securely streams operational metadata about cluster workloads to Elastic Cloud, leaving underlying business data within the indices.

“Before using AutoOps, we were spending a significant amount of time manually analyzing nodes, charts, and indices to understand the root cause and determine a solution for issue diagnosis," said Oz Levy, data operations manager at Tipalti. “When we started using AutoOps, it provided actionable intelligence that changed the game; we no longer have to hunt for answers, they get delivered to us right away, along with the solutions to address them.”

AutoOps is available at no additional cost for Enterprise subscription users. Support for AutoOps for self-managed customers is available now. Sign up for an Elastic Cloud Account to get started.

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Elastic Delivers Cloud-Connected AutoOps for Self-Managed Elasticsearch Users

Elastic announced that AutoOps is now available for the first time for self-managed enterprise users—at no additional cost.

By offloading the heavy lifting to Elastic Cloud, self-managed users gain powerful diagnostic benefits without the overhead of associated infrastructure, also gaining operational insights that improve resource efficiency and lower hardware costs.

“We are always looking to simplify Elasticsearch management to allow developers to focus on building new features instead of troubleshooting issues,” said Ajay Nair, general manager, Platform, at Elastic. “Teams working in self-managed environments can now access the same benefits of AutoOps experienced by Elastic Cloud users.”

AutoOps simplifies cluster management with zero additional overhead. The first in a roadmap of Elastic Cloud connected services for self-managed environments, it runs through a lightweight integration that securely streams operational metadata, such as shard allocations, query latencies, and node utilization to Elastic Cloud. The cloud-powered service processes this telemetry to deliver real-time issue detection and resolution, while the underlying customer data never leaves the self-managed deployment.

Key features for AutoOps include:

  • Simplified cluster management: Delivers real-time cluster insights and automatic detection of ingestion bottlenecks, shard imbalance, and mapping errors so teams can resolve problems before they impact performance.
  • Reduced operational overhead: Eliminates the need to provision and manage a dedicated monitoring cluster. AutoOps data is stored in Elastic's infrastructure and managed by Elastic, freeing teams from additional infrastructure costs and administrative tasks.
  • Cost and resource optimization: Highlights underutilized nodes and inefficient indices, providing clear methods to improve resource utilization and reduce hardware costs.
  • Better support: Elastic Support engineers’ read-only access to AutoOps diagnostics makes support responses faster, as well as more precise resolutions to support tickets.

AutoOps users get the data sovereignty of a self-managed deployment, combined with simplified, proactive operational insights. The platform securely streams operational metadata about cluster workloads to Elastic Cloud, leaving underlying business data within the indices.

“Before using AutoOps, we were spending a significant amount of time manually analyzing nodes, charts, and indices to understand the root cause and determine a solution for issue diagnosis," said Oz Levy, data operations manager at Tipalti. “When we started using AutoOps, it provided actionable intelligence that changed the game; we no longer have to hunt for answers, they get delivered to us right away, along with the solutions to address them.”

AutoOps is available at no additional cost for Enterprise subscription users. Support for AutoOps for self-managed customers is available now. Sign up for an Elastic Cloud Account to get started.

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Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ...