{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Programming Tech Brief By HackerNoon","title":"I Priced the Same Inference Workload on 4 GPU Clouds. Egress Was the Catch","html":"<iframe width=\"100%\" height=\"180\" frameborder=\"no\" scrolling=\"no\" seamless src=\"https://share.transistor.fm/e/96651962\"></iframe>","width":"100%","height":180,"duration":942,"description":"\n        This story was originally published on HackerNoon at: https://hackernoon.com/i-priced-the-same-inference-workload-on-4-gpu-clouds-egress-was-the-catch.\nA reproducible 2026 cost model pricing one inference workload across six GPU clouds, and why egress, not GPU-hours, decides a third of the bill.\nCheck more stories related to programming at: https://hackernoon.com/c/programming.\n            You can also check exclusive content about #gpu, #ai-infrastructure, #cloud-computing, #llm-inference, #cloud-costs, #no-egress-fee-cloud, #mlops, #hackernoon-top-story,  and more.\nThis story was written by: @andreasusic. Learn more about this writer by checking @andreasusic's about page,\n            and for more stories, please visit hackernoon.com.\nMost GPU cloud comparisons stop at dollars-per-GPU-hour, which is only half the bill. Pricing one identical inference workload (1 GPU, 24/7, 30 TB/month egress) across six clouds from their published rates shows egress quietly eating 22–31% of an AWS, Azure, or GCP bill, a line item that's $0 on providers that don't meter it. The real lesson: egress is an architecture decision, not a billing surprise, so model it before you commit.\n        \n        ","thumbnail_url":"https://img.transistorcdn.com/KhCapPSRkLGL2Xw8888yuChkNRWthaKapLYTvNdu4W4/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9zaG93/LzQxMTY2LzE2ODM1/ODIzMzAtYXJ0d29y/ay5qcGc.webp","thumbnail_width":300,"thumbnail_height":300}