Best AI Observability Tools for TierZero

Langfuse is a free and open-source LLM engineering platform that helps teams to debug, analyze, and iterate their LLM Applications. Observability: Incorporate Langfuse into your app to start ingesting traces. Langfuse UI : inspect and debug complex logs, user sessions and user sessions Langfuse Prompts: Manage versions, deploy prompts and manage prompts within Langfuse Analytics: Track metrics such as cost, latency and quality (LLM) to gain insights through dashboards & data exports Evals: Calculate and collect scores for your LLM completions Experiments: Track app behavior and test it before deploying new versions Why Langfuse? - Open source - Models and frameworks are agnostic - Built for production - Incrementally adaptable - Start with a single LLM or integration call, then expand to the full tracing for complex chains/agents - Use GET to create downstream use cases and export the data

Palo Alto Networks Cortex XCOR is an AI-powered observability platform that provides monitoring, troubleshooting, and operational intelligence across applications, infrastructure, and end-user experiences. The platform collects and analyzes logs, metrics, traces, and events to provide visibility into distributed systems and cloud-native environments. Its XCOR Operator provides a conversational interface that enables engineers to query system data, investigate performance issues, configure alerts, and create dashboards using natural language. AI Investigations automatically correlates telemetry signals and system dependencies to identify probable root causes and reduce incident resolution times. Operational Fabric maintains contextual information about system topology, historical incidents, user behavior, and operational knowledge to support AI-driven investigations. Application observability capabilities include automated service discovery, dependency mapping, service-level objective monitoring, and distributed tracing. Real user monitoring and synthetic monitoring help teams evaluate application responsiveness, browser performance, and potential issues affecting digital experiences. The platform's Optimization Engine identifies low-value telemetry and applies filtering, sampling, and aggregation rules to reduce unnecessary data storage costs. Cortex XCOR supports OpenTelemetry-based data collection, scalable infrastructure monitoring, and consumption budgets for controlling observability spending across enterprise environments.