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AI is changing the FinOps mandate. Discover FinOps+, a modern framework for navigating growing complexity with greater visibility and scalable financial controls that support your innovation agenda.

Learn how FinOps+ extends traditional FinOps to:

Improve financial accountability across cloud, SaaS, and AI spend
Reduce manual effort through automation and AI-driven workflows
Move teams from reactive cost management to proactive financial governance

“We didn’t just need FinOps, we needed FinOps Plus. Kion gave us automation, forecasting, and policy in one platform.”
— Matt Cofran, Director of Cloud Operations, Insulet

Your AWS bill for compute looks fine. Then the total arrives and it's way higher than expected. The extra cost is not a mistake in AWS pricing. It comes from small charges that quietly stack up in the background.

Data moving between services adds up fast. Traffic between Availability Zones or across regions costs money, even though it feels like "free" internal traffic.

NAT Gateways charge for every byte processed. Simple fixes like VPC Endpoints for S3 and DynamoDB can make these charges disappear completely.

Old EBS volumes and snapshots keep charging even after you delete the server. It is like paying rent on a storage unit long after you moved out.

CloudWatch logging costs can quietly balloon without retention limits. Ten gigabytes of daily logs can cost $150 a month in ingestion fees alone.

Since February 2024, every public IP address costs money, even ones sitting unused.

These charges are the price of convenience that managed cloud services offer. Finding them early through tagging, cost alerts, and regular reviews keeps your AWS bill matching what your team actually expects to pay.

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Discover how Cost Goblin enables local AWS cost analysis without additional infrastructure, third-party tools, or per-user dashboard costs. In this Summit Series interview, Victor speaks with Etienne, the creator of this open-source tool powered by DuckDB.

Join FinOps professionals at the FinOps Weekly Summit 2026 and discover how to:

Transform FinOps from reactive cost control into strategic business value

Build scalable unit economics for the AI era

Learn proven strategies from organizations managing billions in cloud spend

📅 October 20 & 21, 2026

Amazon Bedrock now supports cost allocation by IAM principal through the bedrock-mantle endpoint, making it easier to tag and track AI usage by team or role in Cost Explorer or CUR 2.0.

AWS extended Extended Support for legacy Elasticsearch and OpenSearch versions through November 7, 2027, but added a surcharge, so teams should factor this into upgrade budgets.

OpenSearch Serverless raised its per-collection-group limit from 1,500 to 10,000, helping teams consolidate workloads and lower cost per collection.

Read All AWS Updates

Cloud Billing added an "Originating products" filter and a Gemini Enterprise preset report, giving teams clearer visibility into AI-related spend.

Flexible committed use discounts for G2 and G4 GPUs are now generally available, letting customers apply spend-based commitments across Compute Engine, GKE, and Cloud Run, and switch machine series or regions as needs change.

Read All GCP Updates

No major FinOps updates this week.

Read All Azure Updates

AWS bills you by the node. Kubernetes actually spends by the pod. That mismatch is why most teams cannot answer a simple question: which team or service caused last month's cloud bill to go up.

It is important to know what OpenCost does and does not do. It measures spending. It does not fix, resize, or automate anything. The savings only happen when people actually look at the numbers and change behavior.

The tool charges based on the higher of what a team requested or what they actually used. This makes waste visible for the first time.

Idle spending, meaning paid capacity nobody used, can be tracked separately or spread across teams depending on your policy.

Connecting this data to Grafana turns cost into a normal metric next to performance data, not a separate finance report.

A new AI layer, called MCP, lets engineers ask plain questions like "why did our bill jump 22 percent" and get real answers in seconds.

Real savings come from four places: rightsizing, reducing idle capacity, teams changing habits once they see their own costs, and cleaning up forgotten resources.

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AI spending has grown fast in the past two years. It went from a small test project to one of the biggest costs in many companies. A new report from Harness surveyed 700 leaders and found some surprising numbers.

The spending is huge and growing

One in five large companies now spends over $1 million a month on AI. Two out of three companies spend more than $250,000 a month. Almost all of them use more than one AI provider, like OpenAI, Azure, or AWS Bedrock.

Nobody owns the AI budget

More than half of companies say no one person or team owns AI costs. Engineering teams make most of the spending decisions. But finance and FinOps teams get blamed for the bill.

Surprises are common

72% of companies got a surprise AI bill in the last year. Worse, many companies take days or even a week to find out why costs spiked. Over 40% still track this fast-moving cost using spreadsheets.

Waste is built in

Companies encouraged teams to use AI a lot, without watching the return. This "use more AI" mindset wasted about 26% of total AI spend. On a $1 million budget, that is $260,000 lost every month for no business value.

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Join this FinOps for AI webinar to learn how to control AI costs, improve visibility, implement governance, and justify AI investments with confidence.

📅 August 27, 2026
🕚 6:00 PM Spain / 12:00 PM ET

Agentic AI is changing how companies need to think about cloud costs. Instead of tracking steady compute and storage costs, teams now need to watch token usage, model choices, and data movement between clouds.

Model choice matters a lot. Using a heavy, expensive AI model for simple questions can waste up to 75% more money than using a lighter model.

Save the powerful models for hard problems only. Watch where your data travels. Moving data between different cloud providers or from the cloud to your own servers can create big hidden fees.

Keeping systems in one place cuts costs and speeds things up. Autonomous AI agents can also become costly fast.

Every time an agent "thinks" through a problem, it can resend the whole conversation history to the model.

This can snowball token costs by 150% to 250% or more compared to simpler, rule-based systems.

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The European Union is banning egress fees, the charges cloud providers use when customers want to leave. The rule starts January 12, 2027. It sounds like a big win for companies stuck with one cloud provider. But the numbers tell a different story.

Egress fees only make up about 10 to 15 percent of a typical cloud bill. The real cost of leaving a cloud provider comes from somewhere else entirely.

When the company 37signals left AWS, its costs only dropped after its reserved contracts expired, not when data actually moved.

This means checking seven things for every workload: data volume, archive retrieval costs, how tied you are to specific vendor tools, database migration paths, contract commitments, identity and licensing systems, and staff retraining needs.

Companies like Hermes Germany saved 25 percent by planning ahead and running two clouds in parallel before switching.

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