Cloud computing has revolutionized the way teams build and scale software—but it’s also made costs a lot harder to pin down.
What used to be predictable capital expenditures have turned into sprawling, variable line items. One month, everything looks fine. The next, you’re over budget and can’t quite explain why. It’s not just frustrating—it’s risky for the business.
That’s why more engineering and finance leaders are turning to Total Cost of Ownership (TCO) as a practical framework to get clarity on cloud costs. But TCO isn’t a product. It’s a mindset. And like most valuable things, it requires some work.
This guide walks through how to think about TCO in a cloud-native world, what challenges to expect, and how to get started—with or without a fancy tool.
What Is TCO in the Cloud?
TCO is a way to account for the entire cost of delivering your application or service—not just what shows up on your monthly cloud bill.
It includes:
- Direct infrastructure costs: Compute, storage, data transfer, load balancers, etc.
- Hidden costs: Idle resources, over-provisioned capacity, inter-region traffic, third-party tooling.
- Operational overhead: Engineer time spent maintaining infrastructure, managing tags, troubleshooting cost anomalies, coordinating across teams, and maintaining compliance or audit-readiness.
The goal is to go beyond “how much are we spending?” and ask:
“What are we spending on, and how does that connect to the value we deliver?”
A well-built TCO model helps you answer questions like:
- What does it cost to serve one customer?
- How much cloud spend is tied directly to revenue-generating activity?
- Where are we overinvesting—or underinvesting?
Why It’s Harder Than It Sounds
Building a TCO model from scratch takes time—and the challenges are real. Let’s break them down.
Limited Visibility
Most cloud providers give you a bill broken down by service (e.g. EC2, S3, EKS), but not by business context. You know you spent $40K on compute, but not whether that powered critical customer workloads or a forgotten staging cluster.
How to get around it:
- Start simple. Export cost and usage data using AWS CUR, GCP BigQuery billing exports, or Azure Cost Management exports.
- Bucket by business function. Use spreadsheet pivots or SQL queries to group resources by project, product, or team.
- Use cost categories. AWS has a native feature called Cost Categories that lets you define logical groupings of linked accounts, tags, or services.
Even if you only manage to tag and bucket the top 20% of your spend, that’s often enough to uncover your biggest blind spots.
Inconsistent or Missing Tags
Tagging is the backbone of cloud cost allocation. But in most orgs, it's an inconsistent mess.
One team tags resources meticulously. Another forgets entirely. A third uses random keys like owner_name instead of standardized tags like team or product. The result? A huge chunk of your bill ends up in “unallocated”—and finance starts asking hard questions.
How to fix it:
- Establish a minimal tagging schema. Start with just 3–4 required keys: team, environment, product, and owner.
- Codify tagging into IaC. In Terraform, use modules to automatically apply required tags to every resource.
- Set guardrails. AWS Config, Azure Policy, or GCP Org Policies can enforce tagging compliance. You can also create pre-deploy checks in CI/CD pipelines to block untagged infra.
Remember: the goal is progress, not perfection. Even if your tagging compliance goes from 40% to 70%, that’s a big step forward in building a usable TCO model.
AI and Kubernetes Add Extra Complexity
Modern workloads—especially AI and Kubernetes—make TCO tracking harder.
- AI/ML models often involve bursty GPU workloads that spin up rapidly and cost thousands per day. These aren’t always persistent, and they’re hard to forecast.
- Kubernetes workloads are co-located on shared clusters, so traditional billing tools don’t break down costs per pod, namespace, or service.
Strategies to handle this:
- For AI, use usage-based tagging or logging to correlate GPU spend with training jobs, model versions, or customer segments. If you’re using managed services like AWS SageMaker or GCP Vertex AI, look at per-job metrics and consider logging metadata alongside billing timestamps.
- For Kubernetes, consider exporting resource usage metrics using Prometheus and estimating costs by namespace, label, or deployment. You can calculate approximate costs by multiplying CPU and memory usage by the on-demand rates for the instance types powering your node pools. It’s not perfect, but it gives you directional insight—especially when you pair it with clear ownership tagging in your YAML manifests or Helm charts.
These workloads require a little more elbow grease—but the investment pays off when you can trace large spend spikes to actual projects or customers.
How to Build a TCO Practice Without a Dedicated Tool
Even without a commercial cloud cost platform, you can build a surprisingly robust TCO approach using native tools, open-source options, and some good old-fashioned spreadsheets.
Here’s a roadmap:
Step 1: Export and Audit Current Spend
Use your cloud provider’s console to export usage and cost data. Look at the top services by spend. Then ask:
- Are these tagged?
- Who owns this usage?
- Is it expected or surprising?
Just answering those questions regularly gives you a leg up.
Step 2: Normalize and Standardize Tags
Audit your existing tags. Identify the most common keys in use—and consolidate. For instance, unify teamname, Team, and group under a single team key.
Pro tip: Create a living tagging policy doc, and make it part of your onboarding for new infra teams.
Step 3: Track Cost Per Unit of Value
Pick a business-relevant unit: API calls, compute hours, transactions, active users—whatever makes sense for your product.
Then divide cloud spend by that unit each month. That gives you something finance will actually care about: cost efficiency tied to growth.
Step 4: Build Monthly Cost Reviews Into Your Rhythm
Hold 30-minute monthly reviews with engineering leads. Use this format:
- What are our top 3 cost drivers?
- Are they tagged and allocated properly?
- What changed compared to last month?
- Are we seeing any anomalies or surprises?
This cadence builds accountability and makes cost awareness a shared responsibility—not just a finance afterthought.
Bonus: Tips for Teams Managing Multiple Cloud Accounts or Business Units
If you’re managing costs across multiple environments, things can get chaotic fast. Here’s how to stay on top of it:
- Use linked billing accounts. In AWS, use Organizations and consolidated billing to centralize spend tracking.
- Assign cost center tags or billing labels per team or business unit.
- Build a master spreadsheet or data warehouse view. Use BigQuery, Athena, or even a shared Google Sheet to unify cost reporting across accounts and clouds.
Even a scrappy system can bring powerful visibility.
Bringing It All Together
Cloud costs aren’t just a finance problem. They’re a business problem—and increasingly, a product problem too. When you adopt a Total Cost of Ownership mindset, you move from reactive cost-cutting to proactive value tracking.
The key is not to wait for perfect tools or perfect data. Start where you are:
- Define what value means for your product.
- Get visibility into what’s driving your spend.
- Build tagging and accountability into your workflows.
Over time, your team will start to see cloud infrastructure not just as a cost center, but as a strategic lever to drive better decisions—and better outcomes.
