---
title: "Cost Optimization"
description: "24 posts about Cost Optimization, summarised, each linking to the original."
---

# Cost Optimization
> 24 posts about Cost Optimization, summarised, each linking to the original.

## Articles

### [Introducing Atlas Gen2 on AWS and GCP for M30+ Dedicated Clusters](https://yomu.fyi/post/introducing-atlas-gen2-on-aws-and-gcp-for-m30-dedicated-clusters.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Published: Jul 7, 2026
- Type: Announcement

MongoDB Atlas introduced Gen2 for M30+ Dedicated Clusters on AWS and GCP, addressing the common inefficiency of overprovisioning storage to meet IOPS requirements. Gen2 runs on newer ARM-based processors, delivering a statistically significant 15-20% reduction in CPU utilization and approximately 45% lower read and write latency in internal pilots. It introduces extended standard IOPS that can be provisioned independently from storage capacity, allowing teams to scale performance without adding unused storage and gain more precise control over infrastructure costs. No application changes or migration are required; teams retain the same Atlas API, connection strings, and operational model while benefiting from newer infrastructure underneath. Gen2 is available in supported AWS and GCP regions across APAC and Europe, with limited U.S. availability on GCP, and existing customers can evaluate upgrade opportunities for eligible M30+ clusters.


### [How to price serverless GPUs](https://yomu.fyi/post/how-to-price-serverless-gpus.md)
- Company: [Modal](https://yomu.fyi/company/modal.md)
- Author: Charles Frye
- Published: Jul 6, 2026
- Type: Explainer

Modal’s engineering blog presents a cost model comparing serverless and reserved GPU pricing for AI workloads, centered on the peak-to-average demand ratio. Serverless GPUs charge only for usage, while reserved GPUs require long-term contracts for fixed capacity, often achieving under 30% utilization. The model shows that when the peak-to-average ratio exceeds the reservation discount rate (typically 2-5x), serverless GPUs become cheaper despite higher base rates, with real workloads like inference and training having ratios of 5-10x, making serverless an obvious cost win. An interactive tool allows teams to estimate costs using preset workloads or custom traffic patterns, defaulting to current B200 GPU rates on Modal and hyperscaler reservations. The post discusses model limitations, including assumptions of perfect prediction and instant allocation, and notes that serverless also reduces engineering costs through faster development and reduced dev/prod skew, while mixed strategies can combine baseline reservations with serverless bursting for high peak-to-average bursts.


### [The 5 Layers Every Cloud Commitment Depends On](https://yomu.fyi/post/the-5-layers-every-cloud-commitment-depends-on.md)
- Company: [Uber](https://yomu.fyi/company/uber.md)
- Published: May 4, 2026
- Type: Explainer

Long-term hyperscale cloud commitments are presented as distributed-systems problems with expensive consequences when physical assumptions are wrong. The post proposes a dependency sequence across regional and availability-zone topology, power architecture, fit-for-purpose compute, SKU qualification, and network design. It compares application-observable latency with bare-metal RTT, uses power consumption as a migration check, and argues that workload characteristics should drive memory-to-core ratios, silicon generations, instance families, and routing choices rather than provider defaults. It also explains why zone placement, inter-zone and inter-region transfer, and egress pricing affect quorum latency, reliability, and economics, while noting that cross-region traffic may require explicit justification. The conclusion is that these constraints compound over the commitment unless teams manage architectural drift, with software and contracting layers addressed separately.


### [Enhancing Cloud Usage Forecasting, Monitoring & Optimizing](https://yomu.fyi/post/enhancing-cloud-usage-forecasting-monitoring-optimizing.md)
- Company: [Etsy](https://yomu.fyi/company/etsy.md)
- Author: Anthony Tambasco
- Published: Jun 17, 2024
- Type: Explainer

After Etsy completed its 2020 migration from an on-premise data center to Google Cloud Platform (GCP), the resulting FinOps team developed a global approach to forecasting, monitoring, and optimizing cloud spend. Costs are grouped into core infrastructure, machine learning and product enablement, data enablement, and Dev, with trailing Cost Per Visit (CPV) serving as the primary forecasting metric after regression tests found visits most correlated with costs. BigQuery and Looker dashboards, alerts, weekly Finance comparisons, and recurring cost-owner and leadership readouts support variance detection, while costs are tracked centrally without chargebacks to preserve flexibility. Optimization work combines FinOps, engineering, and Architecture collaboration, producing GCS storage reductions through monitoring, automation, TTLs, and Intelligent tiering, plus compute, model-deployment, and network compression improvements. The next priority is better visibility into shared machine learning platform spend to improve experiment ROI analysis and identify optimization opportunities.


[Newer posts](https://yomu.fyi/topic/cost-optimization.md)