In today's age of cloud services and SaaS platforms, continuous improvement isn't just a goal — it's a necessity. Here at Elastic, we're always on the lookout for ways to fine-tune our systems, be it our internal tools or the Elastic Cloud service. Our recent investigation in performance optimization within our Elastic Cloud QA environment, guided by Elastic Universal Profiling, is a great example of how we turn data into actionable insights.

In the complex world of microservices and distributed systems, achieving transparency and understanding the intricacies and inefficiencies of service interactions and request flows has become a paramount challenge. Distributed tracing is essential in understanding distributed systems. But distributed tracing, whether manually applied or auto-instrumented, is usually rather coarse-grained.

As we’ve already shared, Elastic is committed to helping OpenTelemetry (OTel) succeed, which means, in some cases, building distributions of language SDKs. Elastic is strategically standardizing on OTel for observability and security data collection. Additionally, Elastic is committed to working with the OTel community to become the best data collection infrastructure for the observability ecosystem.

In today's cloud-centric landscape, managing and optimizing cloud resources efficiently is paramount for cloud engineers striving to balance performance and cost-effectiveness. By leveraging solutions like Tines and Elastic, cloud engineering teams can streamline operations and drive significant cost savings while maintaining optimal performance.

Elastic Universal Profiling™ agent is now open source! The industry’s most advanced fleetwide continuous profiling solution empowers users to identify performance bottlenecks, reduce cloud spend, and minimize their carbon footprint. This post explores the history of the agent, its move to open source, and its future integration with OpenTelemetry.

As AI continues to grow and solve problems across countless industries, a key part of that tech is the ability to seamlessly bridge the gap between human language and machine understanding. This is where natural language processing (NLP) and large language models (LLMs) come in. They provide distinct and specialized approaches for connecting the power of human communication with software and machines. Or in simpler terms, NLP and LLMs enable us to have human-like conversations with software.

Site reliability engineers (SREs) and security analysts — despite having very different roles — share a lot of the same goals. They both employ proactive monitoring and incident response strategies to identify and address potential issues before they become service impacting. They also both prioritize organizational stability and resilience, aiming to minimize downtime and disruptions.

Recently, Elastic announced the AI Assistant for Observability is now generally available for all Elastic users. The AI Assistant enables a new tool for Elastic Observability providing large language model (LLM) connected chat and contextual insights to explain errors and suggest remediation.

Adopting OpenTelemetry native standards for instrumenting and observing applications If you develop applications, you may have heard about OpenTelemetry. At Elastic®, we are enthusiastic about OpenTelemetry as the future of standardized application instrumentation and observability.

Observability goes beyond monitoring; it's about truly understanding your system. To achieve this comprehensive view, practitioners need a unified observability solution that natively combines insights from metrics, logs, traces, and crucially, continuous profiling. While metrics, logs, and traces offer valuable insights, they can't answer the all-important "why." Continuous profiling signals act as a magnifying glass, providing granular code visibility into the system's hidden complexities.