· python
Your Artifact Registry Is Too Far Away: Cut Egress and Build Times With a Distributed Cache
For global engineering organizations, the distance between where artifacts are stored and where they are consumed is both a line item and a performance bottleneck.
A typical setup is a handful of large registries, often Artifactory SaaS, in US, EMEA and APAC cloud regions, serving developers and CI runners in every time zone. Every pip install, npm ci, and docker pull travels across regions to reach them, and when the registry lives in someone else's cloud, every gigabyte that leaves it is metered.
Distance costs you twice: in latency on every request, and in egress on every gigabyte. Three trends are compounding the problem, multiplying both request volume and artifact size. Container images and ML models now move far more gigabytes per pull than the libraries of a few years ago.
