Elastic AutoOps is now free: Investing in our community

Distributed systems like Elasticsearch are designed to be dynamic, constantly adjusting to data and query loads. In dynamic environments with variable search and indexing demands, such as sudden data bursts or peak query periods, maintaining peak efficiency requires more than just basic monitoring. To stay ahead of these shifts, teams need a proactive approach to resource optimization that ensures the cluster remains performant even as workloads evolve. With the right visibility, telemetry and insights become a powerful asset, allowing teams to act on performance improvements before they become urgent and catch potential issues before they reach production.

However, interpreting these signals demands a depth of knowledge. Distinguishing between a healthy resource spike and a critical configuration flaw requires understanding the dependencies between configuration and infrastructure. Historically, teams built and maintained their own separate monitoring tools and relied on configuring ad-hoc rules alerts and dashboards to try and correlate metrics to troubleshoot issues. This is a lot of work. Today, that changes. Elastic AutoOps is now free for every Elasticsearch user, regardless of license tier. No credit card. No license upgrade. No strings.

Elastic AutoOps transforms operational metadata from your self-managed clusters into expert-level diagnostics and actionable resolution paths. Once connected to Elastic Cloud via Cloud Connect, AutoOps identifies performance bottlenecks and optimization opportunities in real time, helping teams slash MTTR and eliminate infrastructure waste without their business data ever leaving their environment. Because the heavy lifting happens in Elastic Cloud, you gain a centralized view of your entire fleet with zero self-managed monitoring infrastructure to provision or maintain. 

The architecture is secure and lightweight: It connects via Cloud Connect to Elastic Cloud, ensuring your actual business data remains in your environment while only operational metadata is processed. Because the analysis happens entirely in the cloud and not on your infrastructure, there's no need to provision or maintain a local monitoring cluster.

When something goes wrong in a distributed system (e.g., slow queries, unexpected resource pressure, and shard imbalances), diagnosis means correlating metrics across nodes, indices, and cluster state. Even experienced operators can spend hours narrowing down the root cause.

Elastic AutoOps provides automated root cause analysis. It identifies the issue, explains why it's happening, and provides specific remediation steps, including ready-to-execute Elasticsearch API commands. Instead of building mental models from raw metrics, you get a direct path from symptom to fix. This meaningfully reduces mean time to resolution, regardless of your team's Elasticsearch expertise level.

Without detailed utilization insights, teams tend to over-provision. It's the rational choice when you lack visibility; better to waste capacity than risk an outage. But it's also expensive.

AutoOps analyzes actual resource utilization across your nodes and indices. It identifies underutilized infrastructure, slow and expensive queries, sub-optimally sized shards, and opportunities for data tier optimization. The result is concrete guidance on right-sizing your deployments, helping you reduce hardware costs based on evidence rather than estimation.

Setup takes seconds: A single lightweight agent connects your cluster to AutoOps via Cloud Connect. From there, you get a centralized view across multiple clusters out of the box with the prebuilt customizable alerts and notifications via Slack, PagerDuty, Microsoft Teams, and more — no separate cluster to provision, no dashboard to build, and no ongoing infrastructure to maintain.

A clear example of this is Tipalti, a global leader in payables automation. Tipalti replaced its manual monitoring stack with Elastic AutoOps to support a lean engineering team. Previously, diagnosing performance issues required hours of cross-referencing dashboards.

By switching to AutoOps, the Tipalti team:

• Eliminated overhead: Decommissioned legacy monitoring clusters entirely

• Optimized resources: Used AutoOps' index consolidation recommendations to reduce infrastructure footprint, resulting in a 10% annual cost saving

• Prevented outages: Leveraged machine learning (ML)-powered anomaly detection to catch disk capacity issues before they impacted customers