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AWS just released a way to get your cloud budget alerts sent straight to WhatsApp instead of waiting for emails you might miss. This new solution connects AWS Budgets to WhatsApp using AWS End User Messaging.

The AWS team provides a CloudFormation template that builds everything else automatically. This includes the SNS topic, Lambda function, and security permissions.

AWS Budgets watches your spending against limits you define. When you go over those limits, Amazon SNS catches the alert. A Lambda function grabs that alert and reformats it. AWS End User Messaging sends it to WhatsApp. Your team sees the alert on their phones right away. Their dream.

Think about a weekend when your development team spins up extra resources for testing. Your budget hits 80 percent on Saturday afternoon. With email alerts, you might not see it until Monday morning when costs have climbed even higher. What an issue huh? World ends …

With WhatsApp alerts, you see it immediately on your phone and can decide whether to take action or let it run. Who wouldn’t want to fix an alert issue on a Saturday afternoon instead of beers with friends.

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Faster Billing Transactions: The updated Billing Console Transactions view now loads in milliseconds with improved filtering, status indicators, and clear "owed vs. available" balance tracking.

Granular AI Chargeback: Data Exports for Amazon Bedrock now breakdown costs by operation type (e.g., InvokeModelInference), enabling precise attribution for foundation-model usage.

EMR Serverless Cost Allocation: EMR Serverless now supports tagging and cost tracking at the individual job run level, allowing for exact benchmarking and chargeback rather than cluster-level aggregates.

Redshift Serverless Budget Controls: New queue-based query management lets you auto-abort expensive or long-running queries to enforce cost-aware SLAs.

Neptune Price/Performance: Amazon Neptune now supports Graviton3 (R7g) and Graviton4 (R8g) instances, offering ~16% lower pricing compared to previous generations.

New EC2 X8i Family: X8i instances are now GA, offering up to 6 TB of memory for data-intensive workloads.

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Our friends from Wiv are doing a session the 26th. It’s a hands-on, technical walkthrough built for the people doing the work: platform engineers, FinOps leads, and DevOps teams who deal with this mess every day. They’ll cover:

Why ownership breaks down in real-world orgs

What needs to be in place to fix it

How automation is being used in production today

A live walkthrough of real workflows

In this episode of FinOps Weekly, we talk with Borja Martinez from NTT Data about the importance of FinOps dashboards for executives.

Listen on Apple Podcast

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FinOps Job Search is hard, so we created a solution. The FinOps Weekly Jobs portal.

It offers relevant FinOps roles, freelance opportunities, and direct access to vendors globally. Our goal is to help freelancers find contracts, practitioners secure permanent roles, and companies discover the talent they need.

Stop searching through noise and start finding the right fit. Submit your post to find the perfect FinOps role.

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The programming languages your FinOps team uses say more about how you work than what tools you prefer. Python just topped the TIOBE Index again as the world's most popular programming language, while R quietly climbed back into the top 10.

Python works well because it connects data work, AI tools, and automation in one place. FinOps teams use Python to pull cost data from cloud providers, build forecasts with machine learning, spot spending problems automatically, and turn insights into actions that run on their own.

R never disappeared from statistics departments, but now it's returning to business teams too. R is built for careful statistical work, measuring confidence in predictions, and creating detailed charts that explain complex ideas. When a FinOps team needs to defend a forecast to executives or prove their cost model is solid, R provides the mathematical rigor that builds trust.

Language popularity rankings measure what developers are talking about and using, not which tool is objectively best. But when Python stays on top and R comes back, it shows that cloud finance teams are moving from static reports to data-driven decisions.

The real measure of FinOps maturity is not which programming language you pick, but how quickly your team adapts to new ways of working. The goal is not to write more code but to pull clear answers from messy data and act on them with confidence.

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This guide from the FinOps Foundation shows how to take those messy, fixed data center costs and turn them into something that looks more like cloud billing. The goal is to create rate cards that let you charge teams for compute and storage in predictable units like vCPU-hours or storage GB-months.

The guide also covers how to map your data center costs into FOCUS format, which is the standard specification for cloud cost data.

This lets you combine data center spending with cloud and SaaS costs in one unified view. That unified view is what executives need to make decisions about workload placement, capacity planning, and whether to keep workloads on-premises or move them to cloud.

When you change rates, manage it like any other significant operational change. Give teams advance notice. Explain why rates are changing. Provide modeling help so teams can understand the impact on their budgets. Rate changes that surprise people create mistrust. Rate changes that are communicated clearly and early become just another part of planning.

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FinOps Proximity Knowledge

Got the pleasure to meet Vera several times here in Madrid and FinOps X Barcelona. Wonderful chats with amazing insights on FinOps and Cloud.

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