AI-Powered Content • Cloud FinOps • Benchmarks

Cloud cost insights, generated at scale.

CloudCostLab is a research-style content site focused on **cloud cost management**: real-world benchmarks, playbooks and calculators for AWS, Azure, GCP, Kubernetes and SaaS. No sales pitches, just signal.

Explore topic hubs How CloudCostLab works

Built as a fully AI-assisted, programmatic SEO lab focused on cloud cost & FinOps niches.

What you’ll find here

  • 🧪 Deep-dive cost breakdowns by service & pattern
  • 📊 Benchmarks for common cloud architectures
  • 📉 Practical saving scenarios & trade-offs
  • 🧮 Calculators & cheatsheets for quick decisions

Made for FinOps, engineering and finance teams who care about real numbers, not vendor slides.

What is CloudCostLab?

CloudCostLab is **not** a consultancy or a tool. It is a content-first lab where cloud cost questions are answered with data, examples and reproducible assumptions.

The site is built as a collection of tightly-focused niche pages (programmatic SEO) that go after very specific questions such as:

  • “Azure vs AWS cost for this exact workload pattern”
  • “What is a realistic discount if I commit for 1/3 years?”
  • “How much does it really cost to run this AI workload?”

Guiding principles

  • Transparent assumptions. No hidden magic. We show formulas and thinking.
  • Vendor-agnostic. Optimisation before discounts, architecture before “credits”.
  • Action-able. Every page should help you make one concrete decision.

Topic hubs under construction

CloudCostLab will grow as a network of niche hubs. Each hub contains dozens of tightly scoped articles, benchmarks and calculators around a single theme.

AWS Cost Patterns

EC2, S3, RDS, EKS and AI services. From lift-and-shift baselines to optimised architectures with commitments and savings plans.

Planned: EC2 families, S3 storage classes, EKS node mix, spot usage.

Azure Cost Patterns

Virtual Machines, AKS, PaaS databases, storage and analytics. Focus on realistic enterprise scenarios and FinOps-ready views.

Planned: reserved instances, hybrid benefits, Fabric & Synapse scenarios.

GCP & Vertex / Gemini

GCE, GKE, storage and AI/ML services. Token-based pricing and GPU-hour modelling for AI workloads.

Planned: inference vs training costs, autoscaling patterns.

Kubernetes & SaaS

Kubernetes (AKS/EKS/GKE) and key SaaS platforms such as Datadog, MongoDB Atlas and GitHub from a cost & unit economics perspective.

Planned: cluster rightsizing, node pools, SaaS unit cost benchmarks.

How CloudCostLab works

The project is intentionally built as a **programmatic SEO** experiment: most pages are generated and maintained with AI agents and reproducible templates.

1

Define high-intent questions

Start from real stakeholder questions (FinOps, engineering, finance) that involve cost, risk and trade-offs. Break them into structured templates.

2

Model scenarios with data

Use public pricing, sizing and usage patterns to build scenario models. AI helps to generate variations while preserving the core model.

3

Generate & review content

AI agents draft pages (copy, tables, formulas). Human review checks assumptions and sanity, then publishes at scale.

4

Iterate based on signals

Pages are updated based on search signals, new services and pricing changes to keep content usable over time.

Who is this for?

  • FinOps practitioners & Cloud CoEs looking for examples and benchmarks.
  • Engineering teams needing quick cost sanity checks in design phases.
  • Finance & procurement asking how commitments, AI workloads and SaaS impact budgets.

Planned resource types

  • Benchmark articles and decision playbooks.
  • Simple web calculators & spreadsheets.
  • Visual cheatsheets for quick reference.
  • Opinion pieces on FinOps, unit economics and AI cost.