#cloud cost

this catches a lot of teams off guard. costs go up, nothing obviously changed on the product side, and yet the invoice is higher than last month. it's a pretty common pattern with kubernetes on google cloud and it usually isn't one big problem, it tends to be several smaller ones running quietly at the same time.

i've spent a good amount of time reading through how GKE billing actually works and where waste tends to hide. this post is the explanation i wish had existed when i first started trying to understand it.

(kubernetes is supposed to make infrastructure efficient. i know. believe me, i know.)

okay so. the core thing nobody tells you upfront

google bills you for what you've reserved, not necessarily what you're running. a node sitting at 3am with literally zero pods doing anything useful? still billing. full price. the meter never stops.

when i actually broke down our bill it was three things:

compute- this was the big one. nodes we weren't actually using

storage- persistent volumes that devs had provisioned at maximum "just in case" sizes and never touched again

network egress- honestly the sneakiest one. data leaving google's network costs money and it hides in the bill really well

👀 the fee that got usevery single cluster costs $0.10 per hour just to keep the lights on- that's roughly $73/month per cluster, whether you're using it or not. google waives this for one cluster per billing account. it's extremely common for teams to have several forgotten dev or test clusters sitting there. multiply $73 by however many you find. the number is usually uncomfortable.

stuff you can fix this week, no drama

i'm putting the easy wins first because honestly these alone cut a noticeable chunk out of our bill before we even touched anything complicated:

N1 → E2 machine types.E2 runs up to 31% cheaper for standard web workloads with comparable performance for most applications. it's a config change, not a rewrite.

turn on the optimize-utilization autoscaler profile.by default the autoscaler kind of... waits around politely before shutting down empty servers. this setting makes it actually aggressive about packing pods and killing unused nodes fast.

go find your zombie clusters right now.if something has had zero external traffic for a week it's a zombie. teams regularly find forgotten test environments that have been billing for months. deleting them stops the cost immediately.

~10%

of GKE clusters sitting completely idle at any moment, per google's own internal data. someone on your team left a test environment running. probably more than one someone.

the over-provisioning thing- this one is genuinely psychological

i don't want to be mean about this but the reason so many pods are over-provisioned is fear. engineers (including me, i've done this) ask for way more CPU and memory than the app needs because crashes are embarrassing and nobody wants to be the person who under-provisioned.

completely understandable. also extremely expensive at scale.

the fix - called rightsizing- is just looking at two weeks of actual usage history and setting Pod Requests based on what the app genuinely uses rather than worst-case guessing. wild concept i know.

⚠ this one bites people in productionif a dev doesn't set both a Request AND a Limit on a pod, kubernetes quietly gives it a classification called "Best Effort." sounds fine. it's not fine. when a node runs low on memory, the system starts killing pods and Best Effort pods go first, always. if your checkout flow or anything revenue-critical is running as Best Effort... it dies during your traffic spike. engineers then add more servers to "fix the instability." the actual fix was just setting proper limits from the start.

the three tiers are: Guaranteed (both Request and Limit set, equal) → Burstable (Request lower than Limit) → Best Effort (nothing set, living chaotically). most orgs have way more Best Effort pods in production than anyone on the team realizes.

the autoscaler conflict situation — genuinely chaotic when it happens

this one took me a while to understand because it seems like it shouldn't be a problem. like why would two autoscalers fight each other? but they do and it's bad.

HPA (Horizontal Pod Autoscaler) adds more copies of your app when load goes up. VPA (Vertical Pod Autoscaler) gives more power to existing copies when load goes up. if you enable both on the same CPU metric- which is easy to accidentally do- they literally work against each other. HPA spins up pods, VPA is simultaneously resizing them, neither gets to finish what it started, and you're paying for the chaos the whole time.

the answer is the Multidimensional Pod Autoscaler- it divides responsibilities properly. HPA owns CPU scaling (number of pods), VPA owns memory scaling (size of pods). they stop arguing. your bill stops spiking randomly.

the bigger architectural stuff - worth it if you're operating at any real scale

these take more planning but the savings are significant:

Spot VMs for background work- google will terminate these without warning, which sounds bad but if you're running batch jobs, report generation, data pipelines- stuff that can restart cleanly- they're up to 91% cheaper than normal nodes. just never run customer-facing services on them. taints and tolerations handle the routing.

Pause Pods- honestly clever. you park a lightweight placeholder pod on a node. when a real high-priority pod needs to spin up instantly during a traffic spike, it evicts the pause pod and takes the space. your users get zero cold-start delay. the cluster quietly boots a new node in the background to replace the evicted pause pod.

preStop hooks in your app code- when kubernetes wants to remove a node to save money it sends SIGTERM to your app. if your app ignores that and just drops live connections, you'll have 5xx errors, angry users, and an on-call engineer who will turn off cost-saving automation to make the errors stop. preStop hooks let the app drain connections gracefully before shutting down. this is what makes safe automated scale-down actually possible.

NodeLocal DNSCache- small one but real. stops pods from firing external DNS queries constantly. less network traffic. less egress cost. adds up across a big cluster.

discounts- and why the order you do things matters

two types worth knowing:

Sustained Use Discounts are automatic, run a VM for more than 25% of the month and google gradually discounts the price. nothing to configure.

Committed Use Discounts are contracts, you promise google you'll use a certain amount for 1 or 3 years, they give you a significant price cut. the mistake people make is signing CUDs before rightsizing, which means they're committing to paying for more than they actually need. rightsize first. get an honest baseline. then commit.

💡 the order matters more than people thinkrightsize, establish real baseline, buy CUDs at that baseline. buying CUDs first is like signing a lease on an apartment before you know how many rooms you actually need. only commit to your minimum, not your peak- peaks are unpredictable, baselines aren't.

being honest: doing all of this manually is genuinely a lot

everything above is real and it works. but across a big org with hundreds of workloads and multiple teams constantly deploying things, doing all of this by hand is basically a part-time job that nobody officially has. pod requests drift. someone spins up a cluster for a demo and it just... stays. CUD timing is hard to get right when your baseline keeps moving.

there are tools built specifically for this- Costimizer being one of them — that continuously scan for the stuff described above, flag it, and can execute low-risk fixes automatically once you trust the recommendations. the part that actually matters to engineering teams isn't just "the savings number", it's reclaiming the hours that would otherwise go toward cloud cost archaeology every month instead of building actual product.

anyway if you read this whole thing- genuinely go count your clusters right now. open the billing console. i'll be here.

Many teams feel confident about their infrastructure… until they open their cloud billing dashboard.

For organizations using Google Cloud, the first detailed billing review can sometimes reveal unexpected costs.

Not because something is broken.

But because cloud resources are incredibly easy to launch- and just as easy to forget.

Across many GCP environments, common patterns often show up:

• idle virtual machines running longer than expected • unattached disks quietly accumulating storage costs • oversized instances handling small workloads • data sitting in expensive storage tiers long after it should have been archived

None of these issues are unusual.

In fact, they are some of the most common causes of cloud overspending.

Cloud platforms make infrastructure flexible and scalable, but without proper visibility and cost monitoring, expenses can grow faster than teams anticipate.

That’s why GCP cost management is less about cutting spending and more about improving visibility and control.

Once organizations understand where their cloud spend is going, optimization opportunities become much clearer:

rightsizing compute resources

removing unused infrastructure

setting budgets and cost alerts

optimizing storage classes

adopting FinOps practices for ongoing cost governance

For teams looking to understand how cloud spending works and how to manage it effectively, this GCP cost management guide breaks down common challenges, optimization strategies, and practical ways organizations can control their GCP costs.

Because cloud infrastructure should scale your applications…

Hot take: If your FinOps tool just gives you a pretty dashboard and a Jira ticket, you don’t have a "solution", you have a second job. 🙄

We’ve all seen the Finout hype. The MegaBill is cool, sure. But it’s 2026. Seeing where you’re losing money is like having a fitness tracker that tells you you’re out of shape but doesn't actually help you lift the weights.

If you’re tired of "Cost Intelligence" that just leads to more engineering homework, it’s time to pivot to Agentic AI.

Costimizer: The "Autopilot." It doesn't just find waste; its AI agents actually fix it (rightsizing, auto-parking, etc.). Plus, it’s mobile-friendly because who wants to be tethered to a desktop to approve a budget exception? 📱✨

Cast AI: For the Kubernetes-obsessed. It handles bin-packing and node reshuffling so your clusters aren't bloated with "zombie" resources.

Vantage: The multi-cloud reporting king. If you just need the cleanest data across AWS, Azure, and Snowflake, this is the one.

CloudZero: Great if you’re trying to build a culture of unit economics and want your engineers to actually care about the cost of a single feature.

The Reality Check: Finout is a map. Costimizer is a self-driving car. One tells you where to go; the other actually takes you there. 🏁

Cloud infrastructure is getting more complex every year.

What started as simple EC2 + RDS deployments has evolved into Kubernetes clusters, multi-cloud setups, cross-region redundancy, and cost optimization layers. And yet, many teams still rely on static diagrams or basic visualization tools.

That’s where tools like Holori entered the picture.

But here’s the real question:

👉 Is architecture visualization alone enough in 2026?

The Real Problem Isn’t Diagrams - It’s Visibility

Most cloud teams don’t struggle with drawing architecture. They struggle with:

Understanding cost impact before deployment

Connecting architecture design with actual cloud billing

Tracking multi-cloud resource sprawl

Optimizing Kubernetes clusters in real time

Aligning DevOps and Finance (FinOps)

Visualization is helpful. But without cost intelligence, it becomes documentation, not optimization.

What Modern Teams Actually Need

When evaluating cloud architecture tools today, technical teams look beyond diagram builders. They want:

✔ Real-time multi-cloud monitoring ✔ Cost anomaly detection ✔ Kubernetes-aware cost breakdown ✔ Forecasting before scaling ✔ Optimization recommendations

In short: architecture + cost intelligence combined.

The Shift From “Design Tools” to “Optimization Platforms”

Holori is widely known for visual cloud architecture modeling.

But many engineering teams are now evaluating platforms that:

Map infrastructure automatically

Detect unused resources

Optimize workloads dynamically

Provide FinOps-ready dashboards

Instead of just showing infrastructure - they help control it.

That’s a fundamental shift.

Why This Matters for 2026

Cloud waste still averages around 30% across organizations.

With rising cloud bills and CFO scrutiny increasing, DevOps teams can no longer separate:

Architecture Cost Performance

The future belongs to tools that connect all three.

If you're exploring platforms beyond traditional visualization tools, this comparison guide may help:

In the modern cloud-driven world, organizations often struggle to balance innovation with cost efficiency. As cloud usage grows, so does the complexity of managing expenditures across multiple accounts, departments, and cloud providers. Cloud financial management is the key discipline that enables businesses to gain visibility, control, and accountability over their cloud spending.

CloudThrotte, a leading cloud cost optimization company based in the USA, specializes in helping organizations implement robust financial management practices that reduce waste and optimize resources. Here’s how cloud financial management can drive effective spend control:

1. Gain Complete Visibility into Cloud Spend

The first step in controlling cloud costs is understanding where your money is going. Many organizations lack centralized reporting and rely on fragmented billing, leading to surprises in monthly invoices.

With cloud financial management tools, you can:

Track costs by service, project, or department.

Identify unused or underutilized resources.

Forecast future spending based on historical patterns.

By having a clear picture of cloud expenditures, organizations can make informed decisions and prevent unnecessary overspending.

2. Implement Budgeting and Forecasting

Effective cloud spend management requires proactive budgeting and forecasting. By setting realistic budgets for each department or project and comparing actual spend against projections, organizations can detect deviations early.

Best Practices:

Set monthly or quarterly cloud budgets aligned with business goals.

Use automated alerts for budget overruns.

Continuously refine forecasts based on usage trends and upcoming projects.

This approach ensures that cloud costs remain predictable and aligned with financial planning.

3. Optimize Resource Usage

A significant portion of cloud spend often comes from idle or misconfigured resources. Cloud cost optimization focuses on identifying these inefficiencies and implementing strategies to eliminate them.

CloudThrotte assists organizations in:

Rightsizing virtual machines and storage.

Scheduling non-critical workloads to reduce peak-time costs.

Decommissioning outdated or redundant services.

Optimizing resources ensures that you only pay for what you actually use, giving businesses greater control over spend.

4. Enforce Governance and Policies

Strong governance is essential for cloud financial management. Without clear policies, teams may provision resources without oversight, leading to cost leaks.

Key Steps:

Centralize cloud account management to monitor resource allocation.

Define roles and permissions to prevent unauthorized spending.

Establish guidelines for provisioning, tagging, and scaling resources.

Governance ensures that spending is controlled, transparent, and accountable across the organization.

5. Leverage Automation for Spend Control

Automation can significantly enhance cloud financial management by reducing manual intervention and enforcing cost-saving measures automatically.

Examples of automation include:

Auto-scaling compute resources to match demand.

Scheduling shutdowns for non-production environments.

Using AI-powered recommendations for cost optimization.

By combining automation with robust cloud spend management practices, businesses can maintain ongoing control over cloud costs while freeing teams to focus on innovation.

6. Partner with Experts for Strategic Oversight

Managing cloud spend effectively requires expertise in both financial management and cloud operations. Partnering with a specialized firm like CloudThrotte brings the experience, tools, and methodologies needed to implement successful cost optimization projects.

CloudThrotte provides end-to-end support—from analyzing current spend and implementing cloud financial management tools to continuous monitoring and optimization—helping businesses reduce costs, improve efficiency, and achieve sustainable ROI.

Conclusion

Cloud financial management for spend control is no longer optional—it’s a critical practice for organizations seeking to scale efficiently in the cloud. By implementing visibility, governance, budgeting, optimization, and automation strategies, businesses can take full control of their cloud expenditures.

Cloud bills creeping higher? 😩 You’re not alone. Even if you’re using nOps for AWS cost optimization, many teams still spend way too much on idle resources, oversized infrastructure, and inefficient compute- and dashboards alone won’t fix that. What you really need in 2026 isn’t another chart… it’s tools that actually save money for you. 💰

1. Costimizer - The Autopilot That Fixes Waste

This one doesn’t just show where you’re wasting money - it actually fixes it using Agentic AI. Idle servers? Rightsizing? Budget enforcement? Yup - it executes solutions. Best part? You only pay if you save. 💪

✨ Good for: teams who want guaranteed savings and actual automation ⚡ Standout: proactive cost cutting, not just reporting

2. Vantage - Master of Multi-Cloud Visibility

If you’re running cloud workloads across AWS, Azure, GCP, and more, Vantage gives you a single financial view. It’s all about clarity and reporting, mapping costs to real business metrics. Best for CFOs and finance teams wanting sharp forecasting.

📌 Good for: multi-cloud enterprises 🔍 Standout: business-aligned cost visibility

3. Cast AI - Kubernetes Wizards

Got massive Kubernetes clusters? Cast AI aggressively manages node pricing and autoscaling. It reshapes your infrastructure inside the cluster and finds the most cost-efficient setup.

🔥 Best for: Kubernetes-heavy workloads ⚠️ Note: More focused on containers - not full cloud optimization

4. CloudZero - Engineer-Focused Cost Intelligence

Not into autopilot? CloudZero gives engineers deep insight into why cloud spend happens. It maps costs back to features, products, or teams so you can drive accountability and culture change.

👀 Good for: understanding cost impact across your org ⚡ Standout: unit economics and cost attribution

5. ProsperOps - Commitment Optimization Ninja

Focusing on Salesforce Plans and Reserved Instances like a pro, ProsperOps automates commitment management so you get the most out of AWS contracts.

💼 Best for: teams deeply bidding on committed discounts 🔑 Standout: automated RI & SP trading

6. ScaleOps - The Pod-Level Right-Sizer

Your Kubernetes nodes may be efficient, but what about the pods themselves? ScaleOps dynamically rightsizes containers so workloads aren’t over-provisioned.

Ever feel like CAST AI is holding your Kubernetes budget hostage? That "% of savings" model hits different when your startup 10x's traffic. 😩

We ditched it for tools that don't tax your wins. Here's the real MVPs:

1. Costimizer – The smart one. AI agents optimize spots/rightsizing but show you the trade-offs (20% safe save vs 40% spicy). Group buys = enterprise discounts for scrappy teams. Multi-cloud (AWS/Azure/GCP). Try For Free

2. Kubecost – Visibility king. Breaks costs by namespace/pod. Open source vibes. Perfect if your FinOps team loves manual control.

3. Spot.io – Spot wizardry for big dogs. Predictive + failover. Complex setup, enterprise $$ 💰

Our pick? Costimizer. Transparent pricing, engineer dashboards, no black box. Saved 30% first month while keeping SLAs tight.

Cloud costs shouldn't fund the tool vendor's yacht.

Full comparison if you're optimizing K8s rn.

Cloud adoption has reached a point where using more than one cloud provider is common. Teams choose multi-cloud setups for flexibility, resilience, and the ability to match workloads to the right platforms. As this approach becomes standard, the way cloud costs are planned is also starting to change. 

In multi-cloud environments, cost estimation isn’t just about checking prices. It involves understanding how compute, storage, and networking come together in real infrastructure setups. Even small changes in configuration or usage can affect overall spend, which makes early planning more important than ever. 

Most teams still begin with provider-specific calculators and spreadsheets. These tools are useful, but they often focus on individual services rather than complete environments. When workloads span multiple platforms, cost planning can quickly turn into a repetitive and manual exercise. 

This is where newer, more practical approaches are beginning to stand out. 

Recently, Teleglobal introduced an AI-powered Multi-Cloud Pricing Calculator designed to help teams think about costs differently. Instead of relying on static pricing tables, the platform models cloud environments based on how they are typically configured and used. 

By focusing on commonly used services such as compute, storage, and networking, the calculator helps teams estimate costs in scenarios that more closely reflect real deployments. This makes it easier to forecast expenses during planning, rather than adjusting budgets after workloads are already live. 

What’s changing isn’t just the tooling, it’s the mindset. Cloud cost planning is gradually shifting from reactive optimization to proactive understanding. When teams have clearer visibility earlier, they can make better architectural decisions and plan growth with more confidence.