October 2025 Summaries

Public sector organizations face sophisticated and persistent cyber threats, with a significant portion lacking sufficient cyber resilience. To address these challenges, Elastic Security offers a comprehensive platform that enhances threat hunting capabilities through advanced AI and machine learning features. Elastic's distributed data mesh architecture enables the seamless ingestion and analysis of vast data streams, while Kibana provides visualization tools to identify security anomalies. The platform's machine learning capabilities aid in detecting deviations from normal behavior, reducing false positives, and uncovering subtle attack patterns. Elastic Managed Large Language Model (LLM) further integrates AI-driven capabilities to secure, scale, and maintain compliance, allowing agencies to accelerate threat detection and response without exposing sensitive data. Elastic's AI features, such as Attack Discovery and Elastic AI Assistant, empower public sector security teams to proactively identify, investigate, and mitigate threats, thereby safeguarding critical infrastructure and maintaining public trust.

Automatic Migration for Dashboards is a new feature available in technical preview for Elastic Cloud Serverless users with an Enterprise license or the Security Analytics Complete tier, aimed at simplifying and accelerating the migration of custom dashboards from Splunk to Elastic Security. It provides a streamlined process for exporting and translating Splunk dashboards into Elasticsearch-compatible formats, maintaining functional equivalence while offering transparency through a summary tab that outlines key translation decisions. This feature leverages large language models to analyze and translate individual dashboard panels, ensuring clarity and context, and handles complex conversions using subgraphs. The migration process is designed to save time by automating much of the manual work involved, allowing security operations center (SOC) analysts to focus on more critical tasks. Elastic Security also integrates AI features like Automatic Migration for Detection Rules and Attack Discovery to enhance SOC capabilities, and it encourages users to try out the migration feature with guidance provided throughout the process.

Chris Blaisure from Elastic discusses the importance of adopting a "build and buy" strategy for AI in enterprise architecture, emphasizing the need to integrate existing SaaS investments with custom AI applications. The article highlights the challenges of fragmented AI features in SaaS applications, which often lead to low adoption and failure to deliver business impact, as evidenced by a study indicating that 95% of enterprise AI investments fail to yield returns. To address these issues, Blaisure proposes a durable enterprise architecture focused on context retrieval and performance monitoring, allowing organizations to leverage AI models with accurate business context, thereby enhancing the relevance and trustworthiness of AI outputs. By utilizing context engineering and advanced search techniques, enterprises can improve AI model accuracy, while performance monitoring through tools like Elastic Observability ensures user engagement and operational efficiency. Blaisure illustrates the success of this strategy at Elastic, where the implementation of a unified, context-aware architecture has led to significant cost savings and improved digital support delivery, showcasing the potential for scalable AI solutions that provide long-term ROI.

Elastic Security's Device Control, introduced in version 9.2, enhances endpoint security by allowing organizations to manage and monitor the use of removable media and connected devices, such as USB storage, to prevent data loss, malware, and unauthorized access. It offers security teams the ability to define and enforce policies that control device usage without hindering productivity, addressing critical security gaps often overlooked by standard network protections. The tool supports compliance with regulations like HIPAA and GDPR, provides comprehensive visibility and auditability through detailed logging of device activity, and allows for the management of trusted devices through customizable policies. Real-world applications include controlling contractor access, containing incidents, maintaining forensic evidence integrity, and preventing unauthorized software installation in educational settings. With a user-friendly interface and centralized management dashboard, Device Control is a pivotal part of Elastic's comprehensive endpoint security strategy, ensuring robust threat prevention while maintaining operational efficiency.

The blog post discusses how AI, specifically large language models (LLMs) and the retrieval augmented generation (RAG) framework, can significantly accelerate the traditionally cumbersome process of migrating security information and event management (SIEM) systems. Usually, this involves manually transferring and translating detection rules from an old system to a new one, a task that can deter security teams due to its time-consuming nature. The use of AI enables automatic conversion of detection rules from legacy systems like Splunk to modern platforms like Elastic, translating query languages and normalizing data much faster than manual processes. This automation not only reduces the time required for migration but also minimizes errors associated with manual transfers. However, the process still requires human oversight to ensure accuracy, especially when dealing with incomplete documentation or missing elements. While AI-driven migration provides a potent solution for SIEM updates, users are reminded to exercise caution, particularly concerning data privacy and the limitations of third-party AI tools.

In air-gapped environments where external network connections are absent, implementing advanced search and AI technologies poses significant challenges, particularly for organizations in sensitive sectors like national security and defense. Elastic has been a trusted solution in these settings for over a decade, providing agile technology for essential use cases such as multimodal vector search. As AI becomes integral to daily operations, the ability to efficiently retrieve and analyze both structured and unstructured data is crucial. Challenges include a lack of technical expertise, high data volume, and data formats that hinder AI analysis. To address these, Elastic employs Retrieval Augmented Generation (RAG), which first queries a vector database to provide relevant context before engaging a large language model (LLM), thus optimizing the use of proprietary data. The model evolves with agentic AI, allowing queries to interact with AI agents for more refined results. Multimodal search, combining text, image, audio, and video inputs, enhances information retrieval, especially valuable in the public sector where data is often stored in complex formats like lengthy PDFs and videos. Elastic's hybrid search, integrating keyword and vector search, further refines results, offering a tailored dataset by reranking and personalizing search outputs.

Integrating proprietary data with generative AI enhances the accuracy, relevance, and actionability of AI outputs by ensuring that the models are trained on data specific to an agency's mission, thus reducing the risk of hallucinations associated with publicly available data. Elastic offers a solution by securely connecting data stores with third-party large language models (LLMs) or custom transformer models, thereby optimizing the utility of generative AI for specific organizational needs.

Elastic 9.2, the latest release of the Elasticsearch Platform, introduces a range of new features aimed at enhancing Search & AI, Observability, and Security. Among its innovations are the Elastic Agent Builder for developing AI agents, DiskBBQ for efficient vector storage, and Streams for AI-driven log analysis in Observability. The release also highlights improvements in Elasticsearch's core platform, including ES|QL enhancements and time-series analysis tools. Elastic 9.2 is designed to streamline operations and improve performance, offering tools like automatic migration for dashboards and detection rules in Elastic Security, and introducing AutoOps for self-managed environments. Available on Elastic Cloud, this version underscores Elastic's commitment to bridging enterprise data with high-quality AI experiences while cautioning users on the use of third-party AI tools with sensitive information.

Version 8.19.6 of the Elastic Stack was released on October 23, 2025, with recommendations for users to upgrade from version 8.19.5 to this latest iteration. The release includes a variety of fixes and changes across different products within the Elastic Stack, detailed in the accompanying release notes.

Version 9.1.6 of the Elastic Stack, released on October 23, 2025, is now available and recommended over the previous version 9.1.5. Users are encouraged to upgrade to this latest version for improved performance and enhancements. For a detailed list of issues resolved and specific changes made in each product included in this update, users should refer to the release notes.

Elastic has been recognized as a finalist in the 2025 TSIA STAR Awards for its innovative use of AI to enhance digital customer experiences through its customer portal. The company's commitment to improving digital self-service support is exemplified by the Elastic Support Assistant, which utilizes their Search AI Platform with features like semantic and vector search, machine learning, and natural language processing. This initiative has led to a 49% increase in digital support usage and a 23% reduction in customer response times, as customers are able to resolve simpler issues independently, freeing up support capacity for more complex cases. Elastic's future plans include introducing further AI-driven enhancements such as enriched case context and multilingual support, aiming to continually exceed customer expectations. The recognition by TSIA underscores Elastic's success in empowering customers to efficiently access support and solve problems autonomously, while also acknowledging the importance of exercising caution when using third-party AI tools.

Elastic and AWS are set to showcase their advancements in cloud and AI technologies at Money20/20 USA 2025, focusing on how these innovations are transforming financial services, including data management, fraud prevention, and compliance. The event highlights the dual challenge facing financial institutions: the need to innovate for relevance while maintaining trust through transparency and robust risk controls. Elastic's integration with AWS is posited as a solution for financial entities to enhance business efficiency, security, and customer engagement, facilitated by AI-powered experiences and comprehensive data visibility. Sessions at the event will cover crucial topics such as payments modernization, regulatory changes, and the use of AI in financial services, emphasizing the need for real-time, AI-driven fraud detection and the importance of end-to-end observability in banking infrastructures. Elastic's booth will provide insights into their capabilities in fraud detection, regulatory compliance, and AI-driven customer and operational resilience, offering actionable solutions to industry challenges.

In the evolving landscape of agentic AI, leaders and enterprises are encouraged to focus on specialization, aligning deeply with partners to accelerate deal-making and anticipate future trends in AI adoption. Emphasizing the importance of defining a unique specialty, the text suggests that partners should aim to become trusted experts in specific solution spaces, using Elastic as an example of a company that powers diverse experiences across sectors like healthcare and finance. The piece highlights the significance of co-sell strategies and partnerships that foster mutual growth through coordinated efforts in technical alignment, sales goals, and account mapping. Looking ahead, it identifies trends such as the verticalization of AI, the rise of ecosystems as differentiators, and a shift towards outcome-first mindsets, urging leaders to focus on customer-centric investments and collaboration with technology-leading partners. The post concludes by advising leaders to position themselves strategically for long-term success by setting a clear vision and executing with discipline, while also cautioning about the responsible use of AI tools and the importance of understanding privacy practices.

In the rapidly evolving landscape of AI development, enterprises are transitioning from viewing AI as a novel technology to demanding tangible outcomes that enhance business intelligence and operations. This shift emphasizes the importance of semantic search, which allows organizations to interpret and apply data with context and precision, moving beyond simple keyword matches. The article highlights the foundational role of Elasticsearch's vector database in facilitating this transition by enabling scalable, context-driven data retrieval for enterprises. It outlines a hierarchy for developing agentic AI solutions, starting with a solid technology foundation and progressing through alignment, partnership, execution, and ultimately achieving impactful outcomes. Elastic's open AI ecosystem and strategic partnerships with major tech companies like Microsoft, Google, and AWS are pivotal in creating integrated, scalable, and enterprise-ready AI solutions. The piece underscores the necessity of holistic technical integration and collaboration in achieving meaningful AI advancements, as illustrated by the successful partnership with Ernst & Young in developing generative AI solutions.

In response to evolving security needs and limitations of its legacy ArcSight SIEM platform, Airties, a leader in Wi-Fi mesh technology, transitioned to Elastic to enhance its security operations. The switch addressed challenges such as outdated technology, integration difficulties, and performance bottlenecks, resulting in a significant reduction in investigation times from hours to seconds. Elastic's user-friendly interface, robust integrations, and scalable architecture allowed Airties to handle increased data volumes and improve threat detection without extensive custom development. The migration was managed internally, leveraging Elastic's ease of use, and led to reduced reliance on third-party support, thereby increasing operational efficiency and control. Looking forward, Airties plans to migrate its Elastic deployment to the cloud and explore security orchestration, automation, and response capabilities, aiming to further streamline operations through managed services.

The October 2025 Elastic DevRel newsletter highlights the new Agent Builder feature in Elasticsearch, which integrates with Kibana to enable users to transform ES|QL queries into reusable tools and organize them into agents capable of solving multistep tasks. Although still in preview, the feature leverages the Model Context Protocol (MCP) to allow seamless integration with MCP-compatible clients, offering flexibility beyond Kibana. The newsletter also introduces Kibana’s new natural-language conversation interface, where Elastic Inference Service (EIS) can process queries using the appropriate tools. It details various resources, including free on-demand training, blogs, and videos that explore topics such as context engineering, vector search, and retrieval-augmented generation (RAG) solutions. Upcoming Elastic events are announced, including Elastic{ON} Tour, which will occur in major cities worldwide, focusing on AI and security. The newsletter encourages community participation and submissions for presentations at these events, emphasizing Elastic's commitment to fostering collaboration and innovation within its community.

Elastic Cloud Serverless has been launched on AWS in the regions of London and Tokyo, offering a streamlined solution for observability, security, and search without the need for infrastructure management. It leverages the Search AI Lake architecture to deliver high speed, scalability, and ease of use by integrating large-scale storage, decoupled compute, and advanced AI capabilities. Users can independently scale workloads and benefit from low latency and hassle-free operations, with a flexible usage-based pricing model. The service integrates seamlessly with existing cloud environments and is available across several AWS regions, allowing easy deployment via the AWS Marketplace. Elastic aims to continue expanding its serverless capabilities to further enhance performance and usability.

Elastic has been recognized as a Visionary in the 2025 Gartner Magic Quadrant for Security Information and Event Management (SIEM), highlighting its innovative approach to security that emphasizes openness, intelligence, and seamless integration into business operations. Elastic aims to address the increasing complexity of security threats with an AI-driven vision that enhances how organizations detect, investigate, and respond to threats. The company offers solutions that diminish alert fatigue and streamline security operations through built-in agentic and conversational AI, unified detection and response across various ecosystems, and a transparent, open-source foundation. Elastic's platform unifies SIEM, XDR, and cloud security, allowing organizations to correlate vast amounts of data and respond efficiently. It also supports intelligent migration capabilities, enabling modernization without the need for extensive rewrites. Elastic's commitment to democratizing security and leveraging AI to transform the security landscape is underscored by its recognition in Gartner's report, showcasing its role in helping security teams become more proactive and effective.

Elastic has partnered with Jina AI to enhance open-source retrieval capabilities for AI applications, focusing on integrating Jina's expertise in multimodal and multilingual embeddings with Elastic's search platform. The collaboration aims to leverage Jina's universal embeddings models, advanced rerankers, and small language models to improve search relevance and AI experiences by combining them with Elastic's large-scale infrastructure. These models will be accessible on Hugging Face and through Elastic's Cloud services, allowing users to implement them alongside Elastic's vector search. Han Xiao, the former CEO of Jina AI and now VP of AI at Elastic, emphasized the potential of this partnership to advance mission-critical search applications by uniting Jina's search foundation models with Elastic's ecosystem. The announcement also includes a disclaimer regarding the uncertainty of future features and the importance of understanding the privacy practices of third-party AI tools.

Elastic has announced the Elastic Inference Service (EIS), a GPU-accelerated inference solution integrated with Elasticsearch on Elastic Cloud, designed to enhance the efficiency of modern search and AI workloads by providing fast, scalable inference for embeddings, reranking, and language models. EIS offers a managed inference-as-a-service platform that reduces operational overhead by eliminating the need for infrastructure management, model testing, and integration handling. It introduces Elastic Learned Sparse EncodeR (ELSER) as its first text-embedding model to improve semantic search relevance and performance, with plans to expand its model catalog further. EIS, leveraging NVIDIA GPUs, promises low-latency, high-throughput inference, and integrates seamlessly with Elasticsearch, offering a streamlined developer experience without the need for manual configuration. It supports multi-cloud and multi-region deployments, ensuring broad accessibility and flexibility, while consumption-based pricing and backward compatibility facilitate ease of use. Future developments aim to introduce additional models and expand coverage across more cloud service providers and regions, further enhancing the capabilities of the Elastic ecosystem.

Agentic AI is set to revolutionize cybersecurity by automating and personalizing threat detection and response, ultimately boosting operational efficiency and innovation. Security teams are already leveraging these AI agents for advanced threat hunting, incident investigation, and real-time fraud protection. Anas Khatri from Elastic emphasizes that agentic AI is not a threat to human analysts but a tool to augment their capabilities, allowing them to focus on complex tasks by handling routine ones. The integration of AI agents involves large language models, automated workflows, APIs, and retrieval augmented generation to optimize security operations. Khatri advises that adopting agentic AI is crucial, as attackers are already utilizing similar technologies, and it is essential for staying ahead in cybersecurity. While AI agents cannot yet replace all existing tools, their incorporation is necessary for strengthening security posture and ensuring resilience against potential threats.

Companies eager to implement generative AI often struggle due to a lack of executive sponsorship and strategic alignment, with most AI projects not progressing beyond pilot stages. Elastic successfully navigated these challenges by embedding generative AI into workflows and addressing specific issues in customer support, employee productivity, and security operations. The company developed three key AI-driven solutions: a Support Assistant that significantly improved customer service efficiency and satisfaction, ElasticGPT that enhanced employee productivity by enabling faster information retrieval, and an AI Assistant for Security that increased threat intelligence output while freeing up analysts' time. These initiatives, championed by IT leaders, demonstrated measurable returns on investment shortly after launch, showcasing the potential of AI when integrated into business processes and backed by executive support.

João Neto's journey from a dedicated community contributor to an employee at Elastic highlights the impact of perseverance, networking, and community engagement. Initially active in the Elastic Contributor Program, João's involvement in sharing knowledge and building connections within the community opened up opportunities and enhanced his visibility, ultimately leading to his role as a customer architect at Elastic. Despite applying multiple times, his persistence and belief in the right timing paid off, culminating in recognitions such as the Customer Architect Rookie of the Year award. João attributes his success to the support of the Elastic community and emphasizes the importance of staying true to one's dreams, highlighting the company's commitment to community-focused events and the welcoming culture that values every team member's voice. Now on the other side as an employee, João encourages other community members to pursue their aspirations at Elastic, underscoring the significance of hard work, support from loved ones, and resilience against doubt.

Elastic Security and Kyndryl have formed a strategic alliance to enhance managed Security Operations Center (SOC) operations by integrating Elastic's AI-powered security analytics with Kyndryl's global SOC services. This collaboration aims to address the challenges of protecting complex environments from evolving threats while managing costs and disruptions. The joint architecture leverages Elastic Security for detection and analysis, and Kyndryl's Security Orchestration, Automation, and Response (SOAR) platform for coordinating responses. The integration of these technologies allows for faster threat detection, improved automation, and better executive visibility, resulting in more efficient and auditable security outcomes. Elastic's AI capabilities streamline the process by transforming noisy data into actionable insights, while Kyndryl's global network of SOCs ensures consistent processes and rapid response. This partnership offers customers the flexibility of data residency choices and AI models, ensuring a scalable, efficient approach to managed security operations with clear accountability and improved mean time to resolution (MTTR). As the alliance evolves, it promises to further advance managed security by enhancing integration, automation, and security outcomes worldwide.

Elastic and Contextual AI have partnered to enhance the effectiveness of AI models by bridging the "context gap" that limits generative AI. This collaboration integrates Contextual AI's context engineering platform with Elastic's Elasticsearch, a widely used vector database, to improve AI's ability to process and understand complex, unstructured enterprise data. Contextual AI's platform focuses on building AI agents that provide accurate and relevant responses by extracting actionable intelligence from data, while Elastic's capabilities offer fast, scalable retrieval and hybrid search functionalities. The partnership aims to streamline AI development by providing a unified system that allows developers to build, evaluate, and deploy AI agents efficiently, ensuring they are grounded in precise enterprise context. This integration is designed to cater to enterprise needs, offering flexible deployment options and robust security, making it suitable for regulated industries like finance and healthcare, ultimately advancing AI's scalability and production readiness.

Elastic Stack version 9.1.5 has been released, addressing potential security vulnerabilities present in previous versions, specifically improving upon version 9.1.4. Users are advised to upgrade to this latest version to benefit from enhanced security. Detailed information on the fixed issues and the comprehensive list of changes for each product included in this release can be found in the associated release notes, while further insights into the security updates are available in the security advisory.

Version 8.18.8 of the Elastic Stack has been released, offering crucial updates and fixes for potential security vulnerabilities, which make it a recommended upgrade over the previous version 8.18.7. Users are encouraged to review the release notes for a comprehensive list of changes and to consult the security advisory for further details on the fixed issues.

Integrating Jamf Protect with Elastic Security enhances the ability of security teams to detect, investigate, and respond to macOS threats by providing comprehensive visibility and advanced analytics across endpoints, networks, cloud, and identity systems. Jamf Protect supplies detailed macOS telemetry and alerts on suspicious activities, which are then normalized into the Elastic Common Schema for seamless correlation with other data sources. Elastic Security's AI-driven analytics and machine learning capabilities enable real-time threat detection and response, while the Elastic AI Assistant offers investigative guidance and decision-making support. The integration also allows for automated threat correlation and visualization of macOS activity, transforming raw telemetry into actionable insights. Elastic Defend enhances response actions by allowing teams to isolate devices, kill processes, and execute remote commands, thereby bridging the gap between detection and remediation. By operationalizing Jamf Protect data within a broader XDR strategy, Elastic Security provides scalable storage, long-term visibility, and a unified platform for comprehensive endpoint protection.

Version 8.19.5 of the Elastic Stack has been released, offering crucial fixes for potential security vulnerabilities, which makes it highly recommended for users to upgrade from the previous version, 8.19.4. Users are advised to consult the security advisory for detailed information on the vulnerabilities addressed, and the release notes provide a comprehensive list of changes and issue resolutions for each product included in this update.

Version 9.0.8 of the Elastic Stack has been released, with a strong recommendation for users to upgrade from version 9.0.7 due to fixes for potential security vulnerabilities. The release includes a detailed security advisory and comprehensive release notes that outline the issues addressed and changes made to each product within this version.

Elastic has been recognized as a Leader in The Forrester Wave™: Cognitive Search Platforms, Q4 2025, highlighting its innovation in AI-powered search through its Elasticsearch Platform. Forrester's evaluation emphasized Elastic's robust capabilities in search, data storage, and applied AI, which are underpinned by its core products: Elasticsearch, Logstash, and Kibana. The platform is praised for its scalability, flexibility, and its return to an open-source model, fostering rapid innovation and stronger community engagement. Key strengths include the depth of relevancy and results delivery, efficient deployment options, and a customizable developer experience that supports both structured and unstructured data. Elastic's roadmap is aligned with modern AI search needs, introducing features like Elasticsearch Query Language (ES|QL) and enhanced vectorization controls, which empower developers to create advanced search and generative AI experiences. Additionally, Elastic offers a versatile deployment environment with cloud, serverless, and on-premise options, making it suitable for varied enterprise needs while maintaining high performance and security standards.

Elastic has been recognized as a Leader in the IDC MarketScape: Worldwide Extended Detection and Response (XDR) Software 2025 Vendor Assessment, highlighting its strengths in AI-driven, open, and unified Security Information and Event Management (SIEM) and XDR solutions. Elastic's robust real-time search capabilities, user behavioral analytics, integrated threat intelligence, and advanced endpoint protection are included in its platform without additional cost, providing organizations with the ability to detect, investigate, and respond to threats efficiently. The platform is praised for unifying various data signals into one contextual framework, allowing security teams to prevent threats and respond quickly using over 400 integrations for data ingestion. Elastic's open platform supports third-party integrations, giving users flexibility in their security strategies while continuously evolving with threat research and protection models. Moreover, Elastic's success in AV-Comparatives tests, where it outperformed competitors in malware and real-world protection, alongside endorsements from cybersecurity practitioners, reinforces its leadership position in the XDR market.

Elastic Security is revolutionizing security operations with its unified platform that combines Security Information and Event Management (SIEM) and Extended Detection and Response (XDR) capabilities to streamline SOC workflows through AI-powered analytics. In a discussion with cybersecurity expert John Hammond, Elastic's Director of Product Management, James Spiteri, demonstrated how the platform's AI-assisted data ingestion and analysis transform raw security data into actionable intelligence, enabling faster threat detection and real-time incident response. Elastic's approach to endpoint security emphasizes openness and accessibility, offering enterprise-grade protection with a single command installation of the Elastic Agent, which integrates over 400 prebuilt data collection integrations. The platform addresses common challenges in security operations, such as alert fatigue and tool sprawl, by using large language models for triage and merging data collection into a single agent, which reduces administrative overhead. Elastic's transparency in detection logic, with nearly 1,500 public detection rules, and its unified response capabilities across multiple platforms, including third-party endpoints, enhance analysts' ability to efficiently manage security incidents without switching between tools. The platform's comprehensive security analytics, powered by AI, offers a consolidated and flexible solution for security teams facing sophisticated threats and growing data volumes, while maintaining visibility and transparency.

AI is revolutionizing the field of cybersecurity by automating the labor-intensive process of data onboarding, which traditionally involves normalizing and enriching diverse data formats for integration into security platforms. This advancement is exemplified by Elastic Security's use of large language models (LLMs) to streamline data ingestion, significantly reducing the time and effort required to prepare data for threat detection and analysis. Security professionals can now rely on AI to handle the complexities of data parsing, allowing them to focus on core activities such as intrusion detection and incident response. However, challenges remain due to the diverse and complex nature of data environments, which AI cannot entirely simplify. Elastic emphasizes the importance of continuous data onboarding as organizations evolve, highlighting their Automatic Import feature as a modern solution to this ongoing challenge. While AI tools are transforming security workflows, users are advised to be cautious with sensitive data and familiarize themselves with the terms of any third-party AI tools employed.

AutoOps, initially launched for Elastic Cloud Hosted environments, is now available for self-managed Elasticsearch clusters, offering simplified cluster management with real-time issue detection, performance recommendations, and insights into resource utilization. This development aims to reduce administrative overhead and manual performance tuning efforts, allowing users to enhance performance by identifying and addressing bottlenecks such as slow queries and unbalanced shards. By providing clear insights into resource utilization, AutoOps helps lower costs by identifying underutilized nodes and optimizing hardware expenditure. The setup process is quick, requiring only a few minutes to connect a self-managed cluster to an Elastic Cloud account. Existing users have reported improvements in performance and system reliability, with companies like Tipalti achieving significant cost savings. AutoOps is included in the Enterprise subscription and can be easily enabled through the deployment of a Metricbeat agent, ensuring secure data privacy as only cluster metrics, not the underlying data, are shared with Elastic Cloud.