Every technology shift adds new context you need to measure. Cloud computing added regions and services. Kubernetes added containers and pods. Multi-tenant applications added users and tenants. AI systems add models, prompts, agents, and execution paths.
The result is that metrics are becoming dramatically more dimensional, faster than ever before. Over time, engineers are forced to make tradeoffs. They remove dimensions, sample data, or avoid instrumenting workflows altogether, not because the data isn’t valuable, but because the cost of capturing it becomes difficult to predict.
Today, we’re introducing Infinite Cardinality Metrics, a new way to capture, explore, and scale custom metrics built for modern workloads. It gives teams the freedom to capture every dimension that matters, aligns cost with data volume rather than cardinality, and enables agentic exploration of richly contextual data. Infinite Cardinality Metrics is built on three simple principles:
1. Freedom to capture every dimension
With Infinite Cardinality Metrics, teams can capture every attribute and dimension that matters without constantly evaluating the cost impact of each new tag. A metric such as request latency is counted once, regardless of whether it’s tagged by service, region, user, tenant, or device, giving teams the freedom to add the dimensions they actually need.
