8 Observability Categories
Platforms purpose-built to monitor, trace, and evaluate AI model behaviour, performance, and drift in production environments.
General-purpose observability platforms covering metrics, logs, and traces across distributed systems and microservices architectures.
Specialised tools for gaining deep visibility into traffic flows, latency, and failures within Istio service mesh deployments.
Application Performance Monitoring tools that track response times, error rates, and transaction traces to ensure application health.
Tools designed for Kubernetes, containers, and cloud-native workloads — integrating with OpenTelemetry, Prometheus, and modern stacks.
Platforms that monitor data pipeline health, data quality, freshness, and lineage — preventing silent data failures from reaching downstream consumers.
Tools for tracing LLM prompts, responses, token costs, latency, and quality — essential for debugging and optimising AI-powered applications.
Observability platforms purpose-built for autonomous AI agent systems — tracking multi-step reasoning, tool calls, agent handoffs, and decision chains.
The definitive guide to machine learning observability tools — detect drift, diagnose failures, measure bias, and maintain model health across the full inference lifecycle.