LLM Observability Articles
Elastic now alerts at 80% OpenAI rate limit usage, before your app gets throttled
OpenAI rate limit monitoring in Elastic maps headroom across every project and model. Compare configured RPM, TPM and IPM limits against real usage and plan capacity before a throttling alert fires.
Stop finding out about your Claude bill on invoice day: Anthropic API monitoring is now in Elastic
Track Anthropic API spend and rate limit headroom across every workspace, model, and service tier, so cost surprises and throttling stop being production-time discoveries.
ES|QL queries for debugging LLM latency, cost and GPU saturation
Learn how to investigate LLM latency, token cost and GPU saturation using ES|QL against OpenTelemetry traces and get a root cause, not just a symptom.
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.
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.
LLM observability with Elastic: Taming the LLM with Guardrails for Amazon Bedrock
Elastic’s enhanced Amazon Bedrock integration for Observability now includes Guardrails monitoring, offering real-time visibility into AI safety mechanisms. Track guardrail performance, usage, and policy interventions with pre-built dashboards. Learn how to set up observability for Guardrails and monitor key signals to strengthen safeguards against hallucinations, harmful content, and policy violations.
2025 observability trends: Maturing beyond the hype
Discover what 500+ decision-makers revealed about OpenTelemetry adoption, GenAI integration, and LLM monitoring—insights that separate innovators from followers in Elastic's 2025 observability survey.