Cardyai

Discover why the standard high-cardinality playbook is wrong and how to handle metrics at scale without ballooning costs. Read the full guide now!

Before merging datasets, LLM agents should classify whether the join is one-to-one, one-to-many, or many-to-many. Getting cardinality wrong leads to duplicated rows, missing matches, and broken pipelines. Here's how to classify merge problems automaticall

Prometheus pays for high cardinality metrics at write time. ClickHouse pays at query time. Neither solves it-they fail differently. Here

What actually breaks when teams add high cardinality metrics and why those failures are hard to avoid unless the system is built for it.

Last week we talked about the different ways we can decompose a simple predicate which is a conjunction of two simpler predicates. Given the query: SELECT ...

Metrics complete Axiom's observability story with purpose-built metrics storage that treats high cardinality as a design principle, not a limitation. Query metrics alongside logs and traces without switching tools or learning new query languages.