Understanding and reducing what your infrastructure costs
View All Tags
Every AI initiative starts with a choice: buy tokens from a frontier lab, or run models in your own cloud account.
For many teams, the lab API is the right answer. But renting is not an option when you need conditions a lab cannot sell you. You might need weights you own and can pin indefinitely, data that never leaves your infrastructure, predictable costs at scale, or independence from a model provider's roadmap.
Kubernetes standardized infrastructure primitives. Nobody standardized the glue above them.
Every company needs a different platform blueprint. They should be able to compose it from shared capabilities and production controls, inside infrastructure they own.
Different infrastructure underneath. One foundation for the blueprint you choose.
Here is a small thing that still delights me. Take the kind cluster running on your laptop, run one command, and it shows up in the same console as a production cluster on EKS: same deploy pipeline, same add-ons, same cost view. Not a simulation of production. The same blueprint running on the same foundation.
I want to explain why we built it that way, because behind that small trick sits our entire argument about how infrastructure should work for startups and mid-size companies. It starts with giving Kubernetes credit for what it actually standardized, and being honest about what it never did.
Requests are not waste: teams use them to preserve schedulable capacity for bursts, startup spikes, and reliability. They also determine how workloads fit on nodes, so more headroom than a workload needs can block consolidation or occupy capacity another workload could use.
AstroPulse shows requests beside time-averaged usage for every cluster, namespace, and application. The gap starts an investigation; it does not prescribe a new request.
Compare requested headroom with average use, then decide whether the gap is intentional.