Transforming observability with AI Assistant, OTel standardization, continuous profiling, and enhanced log analytics

A more modern era of observability is being ushered in with the emergence of AI and generative AI. As these AI technologies go mainstream, expect observability to advance from a manual, reactive process to a more proactive AI-driven approach with auto-diagnosis of issues followed by remediation.

Long gone are the days of monolithic applications running in data centers, where software updates were infrequent events. Operations teams relied on server, network, and storage tools to monitor their tech silos, manually analyzed data, and used an on-call conference bridge with others to identify, triage, and resolve issues. With the advent of cloud along with its complexity, abstraction of infrastructure, and faster development cycles, operations and SRE teams needed observability to help address these new “unknown unknowns.” And while observability tools have made connecting the dots a bit easier, the overall effort is still manual and has been encumbered with the challenge of tool silos and exponentially increasing costs.

With years of experience and innovation in AI and machine learning (ML), vector databases, Elasticsearch Relevance Engine^TM (ESRE), and Retrieval Augmented Generation (RAG), Elastic is well positioned to lead IT teams into this new era of AI-powered observability — bringing metrics, logs, traces, and profiling into one single platform to deliver actionable insights.

As Kelly Fitzpatrick, senior analyst at RedMonk, notes:

Elastic has leveraged its years of machine learning expertise and integration with generative AI platforms to transform observability with relevant and context-aware AI-powered insights. The Elastic AI Assistant for Observability (now in technical preview), powered by the Elasticsearch Relevance Engine (ESRE), enhances the understanding of application errors, log messages, and alerts while offering suggestions for optimal code efficiency. Additionally, Elastic AI Assistant’s interactive chat interface allows SREs to interactively chat and visualize all relevant telemetry in one place, while also leveraging proprietary, internal information for remediation.

Elastic allows users to provide private data to the assistant, such as runbooks, a history of past incidents, case histories, and more. Using an Inference processor powered by the Elastic Learned Sparse EncodeR (ELSER), the Assistant gets access to the most relevant data for answering specific questions or performing tasks. The assistant can learn and grow its knowledge base with continued use and guided learning. SREs can teach the assistant about a specific problem so it can provide support for that scenario in the future and assist in composing outage reports, updating runbooks, and enhancing automated remediation. SREs can pinpoint and resolve issues faster and more proactively, through the combined power of the Elastic AI Assistant and machine learning capabilities, by eliminating cumbersome and manual data retrieval across silos.

By incorporating internal, business-specific information with the LLMs, Elastic AI Assistant delivers highly relevant results helping accelerate problem identification and resolution and turbocharging AIOps for your teams.  

Learn more about how the Elastic AI Assistant provides context-aware insights to solve observability problems.

Learn how to use OpenTelemetry and Elastic to instrument popular languages, employ a standardized open log format (ECS), and analyze data with AI and ML to achieve vendor independence with OpenTelemetry.

Elastic’s enhanced log analytics journey provides SREs with the ability to automatically categorize logs through its unique log routing processor, further processing and enriching log data, and to support log analysis with AI. Using Elastic’s machine learning algorithms, SREs get automated analysis for log spikes, pattern detections, anomaly discovery, and change point detection. Elastic Observability’s log ingestion now supports open ingestion with OpenTelemetry and hundreds of turnkey integrations and custom formats, all with optimal cost and performance. Elastic’s enhanced logs journey results in lower storage costs, heightened operational efficiencies, and reduced time to resolution.

Learn more about the logging journey and how Elastic helps.

Elastic continues to innovate and deliver on its full stack observability solution to help SRE teams manage complex hybrid and multi-cloud environments with actionable insights. Elastic’s investment in AI and machine learning along with a unified, open, and flexible platform continues to deliver on customer needs and will help transform the future of observability. To learn more about our thoughts, listen to this discussion with Kelly Fitzpatrick, senior analyst at RedMonk.

The release and timing of any features or functionality described in this post remain at Elastic's sole discretion. Any features or functionality not currently available may not be delivered on time or at all.

In this blog post, we may have used or referred to third party generative AI tools, which are owned and operated by their respective owners. Elastic does not have any control over the third party tools and we have no responsibility or liability for their content, operation or use, nor for any loss or damage that may arise from your use of such tools. Please exercise caution when using AI tools with personal, sensitive or confidential information. Any data you submit may be used for AI training or other purposes. There is no guarantee that information you provide will be kept secure or confidential. You should familiarize yourself with the privacy practices and terms of use of any generative AI tools prior to use. 

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