GenAI Articles
Troubleshooting your Agents and Amazon Bedrock AgentCore with Elastic Observability
Discover how to achieve end-to-end observability for Amazon Bedrock AgentCore: from tracking service health and token costs to debugging complex reasoning loops with distributed tracing.
Elastic Observability: Streams Data Quality and Failure Store Insights
Discover how the Streams a new AI driven Elastic Observability feature help manage data quality with a failure store to help you monitor, troubleshoot, and retain high-quality data.
Reconciliation in Elastic Streams: A Robust Architecture Deep Dive
Learn how Elastic's engineering team refactored Streams using a reconciliation model inspired by Kubernetes & React to build a robust, extensible, and debuggable system.
How Streams in Elastic Observability Simplifies Retention Management
Learn how Streams simplifies retention management in Elasticsearch with a unified view to monitor, visualize, and control data lifecycles using DSL or ILM.
Live logs and prosper: fixing a fundamental flaw in observability
Stop chasing symptoms. Learn how Streams, in Elastic Observability fixes the fundamental flaw in observability, using AI to proactively find the 'why' in your logs for faster resolution.
Automating User Journeys for Synthetic Monitoring with MCP in Elastic
This post explores how you can automatically create user journeys with Synthetic Monitoring in Elastic Observability, TypeScript, and FastMCP, and walks through the app and its workflow.
LLM Observability with Elastic’s Azure AI Foundry Integration
Gain comprehensive visibility into your generative AI workloads on Azure AI Foundry. Monitor token usage, latency, and cost, while leveraging built-in content filters to ensure safe and compliant application behavior—all with out-of-the-box observability powered by Elastic.
Optimizing Spend and Content Moderation on Azure OpenAI with Elastic
We have added further capabilities to the Azure OpenAI GA package, which now offer content filter monitoring and enhancements to the billing insights!
Transforming Industries and the Critical Role of LLM Observability: How to use Elastic's LLM integrations in real-world scenarios
This blog explores four industry specific use cases that use Large Language Models (LLMs) and highlights how Elastic's LLM observability integrations provide insights into the cost, performance, reliability and the prompts and response exchange with the LLM.
LLM Observability for Google Cloud’s Vertex AI platform - understand performance, cost and reliability
Enhance LLM observability with Elastic's GCP Vertex AI Integration — gain actionable insights into model performance, resource efficiency, and operational reliability.
End to end LLM observability with Elastic: seeing into the opaque world of generative AI applications
Elastic’s LLM Observability delivers end-to-end visibility into the performance, reliability, cost, and compliance of LLMs across Amazon Bedrock, Azure OpenAI, Google Vertex AI, and OpenAI, empowering SREs to optimize and troubleshoot AI-powered applications.
LLM observability: track usage and manage costs with Elastic's OpenAI integration
Elastic's new OpenAI integration for Observability provides comprehensive insights into OpenAI model usage. With our pre-built dashboards and metrics, you can effectively track and monitor OpenAI model usage including GPT-4o and DALL·E.