Compute costs explained: instance-hours, utilization, and hidden drivers

Compute-only estimates are almost always too optimistic. A planning-safe model includes compute time plus at least one scenario for networking (egress) and observability (logs/metrics). This hub links the strongest checklists and tools.

1) Baseline compute

Estimate instance-hours (or vCPU/GB-hours) for steady state, not peak-only.
Separate prod vs non-prod; dev/test can dominate if always on.

2) Utilization and idle waste

Right-size with real utilization (CPU, memory, and burst patterns).
For container platforms, validate packing efficiency and headroom.
Hubs: Kubernetes costs, serverless costs

3) Hidden drivers adjacent to compute

Networking: egress/NAT/transfer scales with traffic.
Load balancers: hourly + request/GB processed.
Logs: ingestion + retention can exceed compute for verbose systems.
Hubs: networking, load balancing, logs

More compute guides

Aurora pricing (what to include): compute, storage, I/O, and backups

A practical checklist for estimating Aurora costs: instance hours (or ACUs), storage growth, I/O-heavy workloads, backups/retention, and the line items that commonly surprise budgets.

AWS ECS Pricing & Cost Guide (EC2 vs Fargate drivers)

ECS cost model for compute, storage, and networking. Compare EC2 vs Fargate and identify real cost drivers.

AWS Fargate pricing (cost model + pricing calculator)

A practical Fargate pricing guide and calculator companion: what drives compute cost (vCPU-hours + GB-hours), how to estimate average running tasks, and the non-compute line items that usually matter (logs, load balancers, data transfer).

AWS RDS cost optimization (high-leverage fixes)

A short playbook to reduce RDS cost: right-size instances, control storage growth, tune backups, and avoid expensive I/O patterns.

AWS RDS pricing (what to include)

A practical checklist for estimating AWS RDS costs: instances, storage, backups, I/O, and the line items that commonly surprise budgets.

Azure Container Registry Pricing & Cost (Basic, Standard, Premium)

ACR cost model for storage GB-month, pulls, and transfer. Understand how pricing tiers affect limits and spend.

Azure Kubernetes Service (AKS) pricing: what to include

AKS cost is more than node VMs: include node pools, networking/egress, storage, and observability. Use this checklist-style model to estimate safely.

Azure SQL Database pricing: a practical estimate (compute, storage, backups, transfer)

Model Azure SQL Database cost without memorizing price tables: compute baseline (vCore/DTU), storage GB-month + growth, backup retention, and network transfer. Includes a validation checklist and common sizing traps.

Cloud cost estimation checklist: build a model Google (and finance) will trust

A practical checklist to estimate cloud cost without missing major line items: requests, compute, storage, logs/metrics, and network transfer. Includes a worksheet template, validation steps, and the most common double-counting traps.

Cloud SQL pricing: instance-hours, storage, backups, and network (practical estimate)

A driver-based Cloud SQL estimate: instance-hours (HA + replicas), storage GB-month, backups/retention, and data transfer. Includes a worked template, common pitfalls, and validation steps for peak sizing and growth.

CloudFront vs Cloudflare CDN cost: compare the right line items (bandwidth, requests, origin egress)

A practical comparison checklist for CloudFront vs Cloudflare pricing. Compare bandwidth ($/GB), request fees, region mix, origin egress (cache fill), and add-ons like WAF, logs, and edge compute. Includes a modeling template and validation steps.

Database costs explained: compute, storage growth, backups, and network

A practical framework to estimate managed database bills: baseline compute, storage GB-month growth, backups/snapshots, and the network patterns that cause surprises.

Dataflow pricing: worker hours, backlog catch-up, and observability (practical model)

Estimate Dataflow cost using measurable drivers: worker compute-hours, backlog catch-up scenarios (replays/backfills), data processed, and logs/metrics. Includes a worked template, pitfalls, and validation steps for autoscaling and replay patterns.

EC2 cost estimation: a practical model (compute + the hidden line items)

A practical EC2 cost estimation guide: model instance-hours with uptime and blended rates, then add the hidden line items that often dominate (EBS, snapshots, load balancers, NAT/egress, logs).

ECS autoscaling cost pitfalls (and how to avoid them)

A practical guide to ECS autoscaling cost pitfalls: noisy signals, oscillations, retry storms, and the non-compute line items that scale with traffic (logs, NAT/egress, load balancers).

ECS cost model beyond compute: the checklist that prevents surprise bills

A practical ECS cost model checklist beyond compute: load balancers, logs/metrics, NAT/egress, cross-AZ transfer, storage, and image registry behavior. Use it to avoid underestimating total ECS cost.

ECS EC2 vs Fargate Cost Comparison

Compare ECS on EC2 vs Fargate using compute, storage, and networking drivers. When each model is cheaper.

ECS vs EKS cost: a practical checklist (compute, overhead, and add-ons)

Compare ECS vs EKS cost with a consistent checklist: compute model, platform overhead, scaling behavior, and the line items that often dominate (load balancers, logs, data transfer).

A practical EC2 cost estimation guide: model instance-hours with uptime and blended rates, then add the hidden line items that often dominate (EBS, snapshots, load balancers, NAT/egress, logs).

Fargate vs EC2 cost: how to compare compute, overhead, and hidden line items

A practical Fargate vs EC2 cost comparison: normalize workload assumptions, compare unit economics (vCPU/memory-hours vs instance-hours), and include the line items that change the answer (idle capacity, load balancers, logs, transfer).

Serverless costs explained: invocations, duration, requests, and downstream spend

A practical serverless cost model: invocations and duration (compute time), request-based add-ons, networking/egress, and the log/metric drivers that often dominate totals.

Cloud cost estimation checklist: build a model Google (and finance) will trust

ECS cost model beyond compute: the checklist that prevents surprise bills

A practical ECS cost model checklist beyond compute: load balancers, logs/metrics, NAT/egress, cross-AZ transfer, storage, and image registry behavior. Use it to avoid underestimating total ECS cost.

ECS vs EKS cost: a practical checklist (compute, overhead, and add-ons)

Compare ECS vs EKS cost with a consistent checklist: compute model, platform overhead, scaling behavior, and the line items that often dominate (load balancers, logs, data transfer).

Related calculators

Data Egress Cost Calculator

Estimate monthly egress spend from GB transferred and $/GB pricing.

API Response Size Transfer Calculator

Estimate monthly transfer from request volume and average response size.

VPC Data Transfer Cost Calculator

Estimate data transfer spend from GB/month and $/GB assumptions.

Cross-region Transfer Cost Calculator

Estimate monthly cross-region transfer cost from GB transferred and $/GB pricing.

Log Cost Calculator

Estimate total log costs: ingestion, storage, and scan/search.

Log Ingestion Cost Calculator

Estimate monthly log ingestion cost from GB/day or from event rate and $/GB pricing.

FAQ

What usually drives compute cost?

Compute is usually priced by time (instance-hours) or resource time (vCPU-hours and GB-hours). The big mistakes are sizing to peak, leaving non-prod always on, and ignoring adjacent line items like egress, load balancers, and logs.

How do I estimate quickly?

Start with baseline capacity (instance-hours or vCPU/GB-hours) for steady state. Then run a peak scenario and add separate budgets for egress, logs, and load balancers.

What breaks estimates?

Underestimating idle capacity, bursty traffic, and downstream costs (databases, logging, networking). Commitments can help but only if you validate utilization.

Last updated: 2026-01-22