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If you are struggling to optimize cloud cost in Azure.

Or you want to really do hands on FinOps there.

And with special pricing for the new year.

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Enrollment only enable for 10 days during this batch. Teached by Alfonso San Miguel, FinOps Author. Someone who knows the stuff, really well.

Code NEWYEAR for 10% discount on any course

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Cloud spending has become the second biggest expense at midsize IT companies, right behind payroll.

A recent survey of 100 CFOs at IT companies with up to 1,000 employees found that these organizations now spend an average of 10% of their annual revenue on cloud services.

One third of these companies spend between 5% and 8% of revenue on cloud, while 29% spend over 13%. To put this in perspective, Gartner found in 2024 that entire IT budgets at midsize companies across industries took up just 3% of revenues.

The main driver behind these rising costs is AI and machine learning workloads, which now make up 22% of cloud spending at these organizations. For now, IT companies are accepting these higher costs because they expect AI to deliver efficiency and productivity gains down the road. But this creates a tricky situation for their customers.

FinOps practices can help find obvious waste like unused resources, but that is just a one time fix. The bigger issue is that developers often have no insight into revenue, margin, or business goals when they make infrastructure decisions.

Cost is a business decision, not a technical decision. IT leaders need to build stronger relationships with their CFOs to set cloud spending goals that match both technical needs and business objectives. CFOs want things to follow a plan, and as long as that plan is being met, they are happy.

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Simplified CloudTrail Lake Import AWS has streamlined the workflow for importing historical CloudTrail Lake data into CloudWatch for unified analytics. While the import operation is free, the ingested data incurs standard CloudWatch custom logs pricing.

Read All AWS Updates

New AKS Pricing Scenario Azure introduced a new "Cloud-native apps on Kubernetes" scenario in the pricing calculator. This tool models AKS cluster costs alongside associated services, helping teams build accurate, production-ready TCO estimates and speeding up FinOps planning for containerized workloads.

Read All Azure Updates

GKE In-place Pod Resize & Writable cgroups Now GA in GKE (Kubernetes 1.35), In-place Pod Resize allows teams to modify CPU and memory resources without restarting pods. This reduces downtime and enables aggressive rightsizing. Additionally, Writable cgroups allow workloads to manage resources for child processes directly, improving efficiency for complex applications.

Read All GCP Updates

​FinOps Weekly Call — 2026 Year Launch. We’ll celebrate last year’s wins, set practical goals for the months ahead, and share quick, actionable tactics for saving money and improving visibility across cloud bills.

​Bring your favorite drink and one cost challenge or clever hack to share — there’ll be time for rapid-fire wins and open mic troubleshooting.

​Whether you want to pick up new ideas, show off a success, or help shape the call plans for the year, this session is made for getting things moving (and having a little fun while we do it).

Google Kubernetes Engine users often see a mysterious line item in their billing reports labeled "kube:unallocated" and wonder what it means and how to reduce it.This cost represents the gap between what you pay for and what you can actually track back to specific workloads. You can fix this by taking six specific actions:

Set resource requests on all your pods so GKE knows what each workload needs.

Right-size your workloads by comparing actual usage to requested resources using the GKE console recommendations.

Turn on autoscaling with Horizontal Pod Autoscaler, Vertical Pod Autoscaler, and Cluster Autoscaler to match capacity to real demand.

Pick the right machine types for your node pools and consider using GKE compute classes to automatically match workloads to appropriate nodes.

For batch jobs or less critical services, use the Optimize Utilization scaling profile to scale down more aggressively.

Or consider GKE Autopilot, where Google manages the nodes and you only pay for what your pods actually request.

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AWS Technical Account Manager Loïc Fournier in-depth breakdown of the most impactful AWS re:Invent announcements for FinOps practitioners. Discover how Database Savings Plans, Compute Optimizer automation, and new cost management features can transform your cloud financial strategy in 2026.

In this episode, we cover the top 3 re:Invent announcements, hidden cost implications, real-world savings scenarios, and a complete 30-day action plan to optimize your AWS spending from day one.

Listen on Apple Podcast - Listen on Spotify - Watch on YT

Snowflake just released Storage Lifecycle Policies, and they could cut your storage bills by 55% to 90%.

All that data sits in expensive storage, quietly eating your budget month after month. Storage Lifecycle Policies fix this by moving old data to cheaper storage automatically. Similar to S3 Lifecycle policies.

Snowflake offers two archive options: COOL tier costs $6 per terabyte per month and works well for data you might need a few times per year. COLD tier costs just $1 per terabyte per month for data you almost never touch. Compare that to standard storage at $23 per terabyte per month.

Watch out for three cost traps though: During the first 8 to 37 days after moving data, you pay for both old and new storage because of Snowflake's safety features. If you need to pull archived data back, you pay retrieval fees of $20 to $30 per terabyte. And if you move data across cloud regions, transfer fees can add up fast.

The best approach is to start with data older than 90 days. Move it to COOL tier first, then shift anything older than a year to COLD tier. Keep your Snowflake account in the same region as your users to avoid transfer charges.

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Organizations are rushing to add GenAI to their applications, but many are not tracking the costs until bills spiral out of control.

This creates a serious problem. Without proper cost management, GenAI projects can fail simply because no one saw the spending coming.

The solution is to apply FinOps practices to GenAI workloads from day one.

Here’s the complete article by Eyal Estrin on how to apply FinOps to GenAI workloads

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German FinOps Quality

Julia has been one of the most active members I’ve seen since I started. Always participating in events and webinars, she shows how to be a real FinOps & community member!

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Azure FinOps Cost Optimization Course
(Starting February 2026)

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