A practical, step-by-step guide for AWS + Kubernetes teams to cut waste without breaking production
Cloud costs rarely spike because you made one “bad” decision.
They grow because of default choices that quietly pile up:
- a node group sized for last year’s traffic
- logs retained forever “just in case”
- NAT egress used for everything
- dev environments running all weekend
- oversized requests in Kubernetes forcing extra nodes
- orphan volumes and snapshots nobody remembers
This checklist is your engineer’s punch list: 30 proven wins across compute, storage, and data with real examples, risk level, and how to implement safely.
No theory. No fluff. Just the stuff that actually moves bills.
The 15-minute setup that makes optimization 10x easier
Step 1: Create a “Cost Action Log” (simple template)
Use any doc/sheet with columns:
- Item # (from this checklist)
- Owner (team/person)
- Effort (S/M/L)
- Risk (Low/Med/High)
- Baseline cost (last 7–14 days)
- Expected impact (monthly)
- Result (after 7–14 days)
- Notes / Rollback plan
If you do only this, you’ll avoid random changes and you’ll build momentum fast.
Step 2: Choose one “unit cost” metric (your secret weapon)
Pick one that matches your product:
- Cost per 1,000 requests
- Cost per order
- Cost per active user
- Cost per batch job
Why this matters: if cost goes up but unit cost stays stable, traffic likely grew. If unit cost rises, you shipped inefficiency.
Step 3: Start where money actually hides
Begin with: always-on compute + databases + log volume + data transfer
That’s where the biggest wins live.
Section A — Compute (12 wins)
1) Right-size EC2/VM instances using real usage
Why it saves: You stop paying for idle headroom.
Do this: Look at 7–14 days of CPU + memory. If peak CPU sits under ~30–40% and memory has room, downsize one step.
Example: A service runs on 8 vCPU but peaks at 1.5 vCPU. Move to 4 vCPU and monitor.
Risk: Low–Med (do gradually) | Effort: S
2) Right-size Kubernetes requests (this is the hidden EKS bill killer)
Why it saves: Requests drive node scaling. Oversized requests force more nodes.
Do this: Compare actual usage vs requests. Reduce requests by 20–30%, observe, repeat.
Example: A pod requests 1000m CPU but uses 80m. That single setting can add entire nodes.
Risk: Med (watch throttling) | Effort: S–M
3) Use autoscaling for services with variable traffic (HPA + cluster scaling)
Why it saves: You pay for demand, not fear.
Do this: Add HPA for pods; ensure nodes can scale too (Cluster Autoscaler/Karpenter).
Example: Scale pods 3 → 12 during peak, return to 3 at night.
Risk: Med | Effort: M
4) Schedule non-prod off-hours (dev/stage should not run 24/7)
Why it saves: Non-prod can easily waste 20–50% of prod spend.
Do this: Shutdown schedules for node groups, databases, and test clusters.
Example: Dev runs only weekdays 8am–8pm → instant bill drop.
Risk: Low | Effort: S
5) Move interrupt-tolerant workloads to Spot (batch, CI, async workers)
Why it saves: Spot pricing is often dramatically cheaper.
Do this: Put batch/CI into Spot node groups; add retries/checkpointing.
Example: CI runners on Spot; if interrupted, job retries automatically.
Risk: Med | Effort: M
6) Use Graviton/ARM where compatible
Why it saves: Better price/performance for many workloads.
Do this: Build multi-arch images, test one service, expand gradually.
Example: Stateless APIs often switch smoothly and cut compute costs.
Risk: Med | Effort: M
7) Remove “always-on” bastions and jump boxes
Why it saves: Small constant spend + security improvement.
Do this: Replace with on-demand access patterns; shut down outside usage.
Example: Bastion used 2 hours/week but runs 168 hours.
Risk: Low | Effort: S
8) Tune serverless (Lambda/Functions): memory, timeouts, cold starts
Why it saves: You pay for execution time × memory × invocations.
Do this: Lower memory where possible, reduce dependencies, cache connections.
Example: Function set to 1024MB “just in case” but uses 256MB.
Risk: Low–Med | Effort: S
9) Reduce over-provisioned redundancy in non-critical systems
Why it saves: HA patterns multiply costs.
Do this: Keep strong HA for prod critical paths; simplify internal tools.
Example: Internal dashboard running 6 replicas across zones → drop to 2.
Risk: Low | Effort: S
10) Kill zombies: orphan load balancers, old clusters, idle services
Why it saves: Zombies are pure waste and never stop billing.
Do this: Weekly “zombie hunt” using tags/owners; delete after verification.
Example: Old ALB from migration still routes nothing → delete.
Risk: Low–Med | Effort: S
11) Standardize instance families (reduces sprawl, improves commitments)
Why it saves: Better bulk purchasing and simpler operations.
Do this: Choose a small set of preferred instance types per workload.
Example: Too many families makes commitments hard and increases waste.
Risk: Low | Effort: M
12) Commit smartly (Savings Plans/RIs) only for stable baseline usage
Why it saves: Deep discounts—if you don’t overcommit.
Do this: Commit for steady prod baseline; keep experiments on on-demand/Spot.
Example: Commit for always-on core services, not for spiky jobs.
Risk: Med (commitment risk) | Effort: S–M
Section B — Storage (10 wins)
13) Add lifecycle policies: hot → cool → archive → delete
Why it saves: Most data becomes cold quickly.
Do this: Define tiers per data type.
Example: App logs: hot 7 days, cool 30 days, archive 180 days, delete after compliance.
Risk: Low | Effort: S
14) Delete unattached volumes and stale snapshots (monthly cleanup)
Why it saves: Common, silent waste.
Do this: Find unattached EBS volumes, old snapshots, unused AMIs.
Example: Scaling down nodes leaves volumes behind.
Risk: Low | Effort: S
15) Switch EBS to gp3 and right-size IOPS/throughput
Why it saves: You stop paying for performance you don’t use.
Do this: Move from older/default configurations to gp3 and tune.
Example: Workload doesn’t need high IOPS; gp3 saves monthly.
Risk: Low–Med | Effort: S
16) Reduce non-prod backup frequency and retention
Why it saves: Backups are a storage multiplier.
Do this: Keep prod strong; make dev/stage lighter.
Example: Dev DB backed up hourly and retained 30 days → change to daily, retain 7 days.
Risk: Low | Effort: S
17) Compress backups and verify restore (don’t skip restore testing)
Why it saves: Storage shrinks; transfer shrinks.
Do this: Enable compression + periodic restore drills.
Example: Backup size drops 50–70% with compression.
Risk: Low | Effort: S
18) Remove duplicate copies of datasets across environments
Why it saves: Duplicates are invisible because “it feels normal.”
Do this: Use controlled access patterns instead of copying data everywhere.
Example: Raw dataset copied into dev/stage/prod buckets “for convenience.”
Risk: Low–Med | Effort: M
19) Put TTL on temporary artifacts (exports, build outputs, uploads)
Why it saves: “Temporary” becomes permanent without TTL.
Do this: 7/14/30-day expiration rules.
Example: Nightly exports kept forever → huge storage growth.
Risk: Low | Effort: S
20) Reduce log volume BEFORE storage (filter, sample, structure)
Why it saves: You pay to ingest + store + query.
Do this: Turn off debug in prod, keep structured errors, sample noisy endpoints.
Example: Debug logs during a high-traffic event can multiply cost instantly.
Risk: Low | Effort: S–M
21) Clean container registries (old images, layers, caches)
Why it saves: Registries grow with every deployment.
Do this: Keep last N images per service; purge older automatically.
Example: Keeping 500 builds per service adds up fast.
Risk: Low | Effort: S
22) Database storage and performance tiers: right-size and prune
Why it saves: DB performance settings can cost more than compute.
Do this: Tune storage size, IOPS tier, remove unused replicas, review indexes.
Example: A read replica exists “just in case” but serves no reads.
Risk: Med | Effort: M
Section C — Data & Network (8 wins)
23) Reduce cross-AZ traffic (chatty services are expensive)
Why it saves: Inter-AZ data adds cost and latency.
Do this: Co-locate chatty services when possible; reduce calls; batch requests.
Example: Service A calls Service B 20 times per request across AZs.
Risk: Med | Effort: M–L
24) Reduce NAT egress by using VPC endpoints for common services
Why it saves: NAT can become a “tax” on everything if misused.
Do this: For internal service access patterns, route privately where appropriate.
Example: Nodes pull images, write logs, access storage via NAT unnecessarily.
Risk: Low–Med | Effort: M
25) Add caching where read traffic is high (CDN/app cache)
Why it saves: Fewer expensive reads, lower DB load, fewer bytes transferred.
Do this: Cache hot endpoints and static content.
Example: Catalog endpoint hits DB every time → cache for 60 seconds.
Risk: Low–Med | Effort: M
26) Compress payloads + batch network calls
Why it saves: Less transfer + fewer requests + less CPU overhead.
Do this: Enable compression, switch to efficient formats, batch telemetry.
Example: Sending 1 event/request vs 100 events/batch reduces overhead.
Risk: Low | Effort: S–M
27) Remove duplicate telemetry pipelines (logs shipped twice)
Why it saves: Ingestion is often billed per GB/event.
Do this: Ensure you don’t have sidecar + node agent both collecting the same logs.
Example: Two collectors ingest identical logs → double cost forever.
Risk: Low | Effort: S
28) Fix high-cardinality metrics (the silent observability bill)
Why it saves: Cardinality explodes storage and query costs.
Do this: Never label metrics by userId/sessionId/requestId.
Example: latency{userId=...} creates millions of time series.
Risk: Low | Effort: S–M
29) Optimize analytics queries (pay-per-scan is brutal)
Why it saves: Many analytics platforms charge by bytes scanned.
Do this: Partition, cluster, filter early, avoid SELECT *, materialize aggregates.
Example: Daily report scans entire dataset to compute yesterday’s numbers.
Risk: Low | Effort: M
30) Turn retention into policy (not habit)
Why it saves: Default “keep forever” becomes your most expensive strategy.
Do this: Define retention by type: logs, traces, events, backups, raw data.
Example: Keep raw events 30 days, aggregates 12 months, audits per compliance.
Risk: Low | Effort: S
The “Start Here” path (so you see results in 7 days)
If you want quick wins without drama, do these in order:
Day 1–2: Instant cleanup
- #10 Kill zombies
- #14 Delete unattached volumes/snapshots
- #21 Cleanup old images
- #19 TTL for temp exports/artifacts
Day 3–4: Big baseline reductions
- #4 Schedule non-prod off-hours
- #20 Reduce log volume + retention
- #13 Lifecycle storage tiers
Day 5–7: Structural wins
- #2 Fix Kubernetes requests
- #1 Right-size top instances
- #24 Reduce NAT egress patterns
You’ll usually see bill movement within the next billing cycle—often sooner.
Safety rules (optimize without breaking production)
- Change one thing at a time
- Reduce gradually (20–30% steps)
- Measure before/after (7–14 days)
- Keep rollback ready (especially for DB/storage performance)
- Protect latency/error rate (cost is never worth a reliability outage)
Make it stick: 3 lightweight habits (Operate mode)
Weekly (30 minutes)
- top spend changes
- anomalies
- assign 3 optimization actions
Monthly (60 minutes)
- unit cost trend
- top 10 services cost share
- savings realized vs expected
Always-on guardrails
- enforce tags/labels
- default TTL on temporary data
- default retention limits
- size limits for non-prod resources
SEO-ready packaging for CloudOpsNow (no links)
Suggested titles (pick one)
- Cloud Cost Optimization Checklist: Top 30 Wins for Compute, Storage, and Data
- How to Cut Cloud Bills Safely: 30 Practical Optimization Wins Engineers Trust
- The CloudOps Cost Playbook: 30 Proven Ways to Reduce Cloud Spend Without Downtime
Suggested meta description
A step-by-step cloud cost optimization checklist with 30 high-impact wins across compute, storage, and data transfer. Practical examples, low-risk actions, and a 7-day plan engineers can follow.
Suggested URL slug
cloud-cost-optimization-checklist-top-30-wins
Suggested keywords (natural usage)
- cloud cost optimization checklist
- reduce cloud bill
- kubernetes cost optimization
- EKS cost optimization
- cloud storage lifecycle policy
- data transfer cost optimization
- logging cost reduction
- right sizing cloud resources
“People also ask” style FAQs (great for traffic)
- What is the fastest way to reduce cloud costs without risk?
- Why are Kubernetes costs so high even when CPU usage is low?
- How do I find unused cloud resources safely?
- What’s the best way to cut log and observability costs?
- Why does NAT cost so much and how do I reduce it?
- Should I use Spot instances in production?
- How do I right-size databases without downtime?
- What is high-cardinality metrics and why is it expensive?
- How do I control storage growth long-term?
- What should I measure to prove cost optimizations worked?