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

ECS vs EKS comparisons are often wrong because teams compare “service A” to “service B” without modeling the surrounding infrastructure. Use this checklist to compare consistently: normalize workload assumptions, pick the compute model, add platform overhead, then add the line items that dominate in production.

Step 0: normalize workload assumptions (the part most comparisons skip)

Average capacity: typical vCPU and memory used over a month (not peak only).
Burstiness: how often you hit peak (daily, weekly, incident-only).
Traffic shape: requests/sec and average response size (drives transfer and LB usage).
Logging volume: GB/day and retention (often a separate bill).

Helpful helpers: units converter and RPS → monthly requests.

Step 1: pick the compute model for ECS

ECS on EC2: pay for instance-hours (and EBS). You win when you can pack tasks efficiently.
ECS on Fargate: pay for vCPU-hours + memory GB-hours per running task. You win when you reduce idle running tasks.

ECS pricing guide ECS EC2 vs Fargate calculator ECS task sizing

Step 2: model EKS baseline and packing efficiency

EKS cost is usually dominated by node-hours, but the driver is “how well you pack pods into nodes” and “how many clusters you operate”.

Control plane: fixed hourly fee per cluster (matters for many small clusters).
Nodes: rightsize from requests and accept imperfect packing (pod limits, topology constraints).
Add-ons: CNI/ingress/observability add baseline overhead on every node.

EKS cost calculator EKS control plane cost EKS node sizing

Step 3: add the line items that dominate both (don’t skip this)

Load balancers: hourly baseline plus capacity units for each LB.
Logs & metrics: ingestion + retention; high-cardinality metrics and chatty logs can become major.
Networking: NAT processed GB, cross-AZ transfer, and internet egress.

LB cost Log cost VPC transfer

A practical decision guide (when cost tends to favor each)

ECS tends to win when you want a smaller platform surface area, run a few predictable services, and can keep average running tasks low.
EKS tends to win when you can consolidate many workloads into fewer clusters and achieve high packing efficiency with disciplined requests and autoscaling.
Either can lose if you sprawl clusters/LBs, ignore observability volume, or route internal traffic through expensive paths (NAT, cross-AZ).

Common pitfalls

Comparing peak capacity instead of average (budgets are mostly driven by average).
Ignoring cluster count: many small EKS clusters create fixed-fee and operational overhead.
“One load balancer per service” patterns creating an always-on baseline bill.
Missing NAT/cross-AZ transfer costs from service-to-service chatter.
Assuming logs/metrics are “small” without measuring GB/day and series growth.

How to validate after you choose

In billing/Cost Explorer, group costs by service and usage type: verify compute vs LB vs logs vs transfer.
Compare “average running tasks/pods” to “peak”; optimization is often reducing idle, not shaving milliseconds.
After a week, re-check: LB count, NAT processed GB, log ingestion GB/day, and cross-AZ transfer.

Sources

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.

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).

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).

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).

EKS pricing: what to include in a realistic cost estimate

A practical EKS pricing checklist: nodes, control plane, load balancers, storage, logs/metrics, and data transfer — with calculators to estimate each part.

Fargate vs EKS cost: what usually decides the winner

A practical Fargate vs EKS cost comparison: normalize workload assumptions, compare task-hours vs node-hours, include EKS fixed overhead (cluster fee + add-ons), and account for the line items that dominate both (LBs, logs, 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.

Kubernetes Cost Calculator

Estimate cluster cost by sizing nodes from requests and pricing them.

Kubernetes Node Cost Calculator

Estimate cluster monthly cost from node count and per-node hourly pricing.

FAQ

Is ECS always cheaper than EKS?

No. The winner depends on utilization and how you operate the platform. EKS can be cost-effective when you pack workloads well and amortize cluster overhead; ECS can be simpler and reduce operational load for many teams.

What’s the fastest way to compare ECS vs EKS cost?

Use the same workload assumptions (average capacity, burstiness, and traffic). Compare compute first, then add load balancers, logs, and networking to both models.

What line items do teams forget in both models?

Logs (ingestion + retention), NAT/egress, cross-AZ traffic, and the baseline cost of multiple load balancers.

Last updated: 2026-01-27