AI brings order to observability disorder

Digital tools are the lifeblood of today’s enterprises, but the complexity of hybrid cloud architectures, involving thousands of containers, microservices and applications, frustrates operational leaders trying to optimize business outcomes. Many are using a profusion of point siloed tools to manage performance, adding to complexity by making humans the principal integration point.

Traditional IT performance monitoring technology has failed to keep pace with growing infrastructure complexity. Siloed point tools frustrate collaboration and scale poorly. Proprietary data formats and capacity-based pricing dissuade customers from mining the analytical value of historical data.

Artificial intelligence has contributed to complexity. Businesses now want to monitor large language models as well as applications to spot anomalies that may contribute to inaccuracies,  bias, and slow performance. Legacy observability systems were never designed for the ability to bring together these disparate sources of data. 

A unified observability platform leveraging AI can radically simplify the tools and processes for improved visibility and resolving problems faster, enabling the business to optimize operations based on reliable insights. By consolidating on one set of integrated observability solutions, organizations can lower costs, simplify complex processes, and enable better cross-function collaboration. 

“Noise overwhelms site reliability engineering teams,” says Gagan Singh, Vice President of  Product Marketing at Elastic. Irrelevant and low-priority alerts can overwhelm engineers, leading them to overlook critical issues and delaying incident response. Machine learning models are ideally suited to categorizing anomalies and surfacing relevant alerts so engineers can focus on critical performance and availability issues. “We can now leverage GenAI to enable SREs to surface insights more effectively,” Singh says. 

Generative AI can simplify problem resolution and recommend appropriate remediation actions for your organization, all while interacting using natural language. It can enrich telemetry data with business context to explain problems and solutions in terms business leaders can also understand. 

“Customers want an observability solution that gives them an integrated view across all segments, whether on-premises or in the cloud,” Singh says.

Consolidate observability tools

When considering a next-generation observability solution, IT leaders should look for these features and characteristics: 

• A comprehensive set of integrated capabilities that can be extended through third-party integrations.
• A single view of all operations on premises and in the cloud.
• Petabyte-level scalability and use of low-cost object storage with millisec response to enable historical analysis and reduce costs. 
• The ability to enrich data with business context. For example, a bank should be able to see separate views of the performance of its ATM and online banking systems.
• The ability to connect operational and business data so site reliability engineers can be notified of the highest-priority business-impacting issues.
• Support for OpenTelemetry, APIs, and open standards to unify telemetry data for comprehensive analysis. 
• Support for a wide range of large language models in the cloud and on premises.
• Powerful vectorDB capabilities to allow engineers to flexibly query large sets of structured and unstructured data. 
• A transparent, published pricing model based on consumption. 
• AI-powered capabilities that enable rapid analysis and provide performance-related information in an understandable business context. 

Elastic resolves problems faster with an open-source, AI-powered observability solution that is accurate, proactive, and efficient. Leveraging an efficient, high-performance data store. Elastic enables comprehensive visibility and tools consolidation across complex hybrid and multi-cloud environments and accelerates problem resolution with unified observability at lower TCO. Built for practitioners, Elastic reduces toil and improves SRE productivity, accelerating problem resolution and triage using a RAG-powered AI Assistant, zero-config multi-signal anomaly detection and pattern analysis, and an open, extensible architecture with OTel-native interoperability.

To request a meeting to learn how Elastic Observability can help your business click here.