12 AWS Products for Slashing Costs

When runway is measured in months, cloud spend is one of the fastest levers you can pull. Do the runway math: at early stage, every 10% you shave off infrastructure can translate into meaningful extra months of life without hiring freezes or product slowdowns. If you decide to prioritize efficiency first, make safe, reversible changes that pay back immediately. Below are the 10 AWS tools that consistently cut costs, assembled into an efficient stack you can deploy in weeks to start saving on costs as soon as possible. Alongside those AWS defaults, Archil belongs in the conversation: making S3 feel like an infinite, local disk to keep cold data cheap, and let smaller instances do more work.

First, what is Archil?

Archil is a POSIX-compliant, S3-backed volume that turns S3 and other S3-compatible stores into infinite, local disks with instant access to large datasets. Behaving like a block storage to your apps while scaling like object storage, Archil volumes are shareable, auto-growing, and pay-for-what-you-touch.

1. Compute Savings Plan

Compute Savings Plans are 1 to 3 year commits discounts EC2, Fargate, and Lambda without locking you to a specific instance family or region. It converts steady compute (APIs, workers mounting S3-backed data) into a cheaper baseline, typically saving up to 70% in costs compared to On-Demand. Best for predictable services that regularly access datasets via Archil or S3 volumes.

2. EC2 Spot (and Fargate Spot)

EC2 Spot is spare capacity with a two minute reclaim notice at deep discounts. It slashes cost (often up to 90% off) for interruption-tolerant jobs like ETL, feature extraction, training, CI, and batch analytics that stream data from S3 or Archil volumes. Use multi-AZ, multi-size Spot fleets and add graceful interruption handlers to keep throughput high.

3. Graviton (ARM) Instances

Graviton Instances are AWS’s ARM CPUs (e.g., C7g/M7g/R7g) that deliver better price and performance than comparable x86. You save by getting more work per dollar for services that marshal or transform data near S3 or Archil volumes. Use multi-arch containers and migrate stateless tiers first, then evaluate caches and databases where supported.

4. AWS Compute Optimizer

AWS Compute Optimizer is ML-driven rightsizing for EC2, EBS, Lambda, RDS/Aurora, and ECS. It cuts waste by flagging idle and over-provisioned resources that often appear once data access is streamlined through Archil. Start org-wide, fix “idle” and “over-provisioned” first, then tune latency-sensitive paths.

5. S3 Intelligent-Tiering and Lifecycle (Glacier tiers)

S3 automatically shifts objects between access tiers, and lifecycle rules age colder data into Glacier. Storage costs fall as datasets cool and thus, no code changes occur. You can utilize on buckets backing Archil so hot bytes stay fast while historical artifacts, checkpoints, and logs drift to cheaper tiers.

6. EBS gp3

EBS gp3 is next-gen general-purpose SSD volumes with IOPS/throughput decoupled from size. It’s up to 20% cheaper and lets you buy only the performance you need for scratch space or local caches alongside Archil. Make use of EBS gp3 for most EC2 workloads that don’t require io2, and migrate gp2 to gp3 during maintenance windows.

7. EFS Infrequent Access

EFS Infrequent Access are lower-cost classes for EFS with automatic lifecycle moves. You save up to 90% on rarely accessed files in shared POSIX trees that still coexist with Archil. Use when you keep shared dirs for builds or artifacts but most content is cold; watch retrievals to avoid surprise rehydration charges.

8. VPC Endpoints (Gateway & Interface)

VPC Endpoints are private links from your VPC to AWS services (Gateway for S3/DynamoDB; Interface for others) cut costs by avoiding NAT Gateway processing and public egress, which carries hidden taxes for data-heavy stacks. Use whenever private subnets talk to S3/ECR/CloudWatch/etc.

9. CloudFront (with Origin Shield)

AWS’s CDN caches content globally; Origin Shield adds a centralized cache to reduce origin fetches. You save by lowering S3 and compute egress and origin load while speeding delivery of models, datasets, and artifacts produced or consumed alongside Archil. Use for static assets, downloads, and cacheable API responses.

10. Aurora Serverless v2

Aurora Serverless is an Aurora instance that auto-scales capacity up and down and can scale to zero when idle. It stops you paying for 24/7 database compute in environments adjacent to large S3 and Archil datasets. Use for dev/preview and spiky microservices; keep an eye on cold starts and storage growth.

11. DynamoDB Capacity Modes

DynamoDB Capacity Modes include both “On-Demand” (pay per request) or “Provisioned with autoscaling”. It saves by matching spend to actual request volume for metadata/catalog tables that reference S3 and Archil objects. Use On-Demand for unknown/spiky access and switch steady, hot tables to Provisioned with sane autoscaling.

12. AWS Budgets & Cost Anomaly Detection

AWS Budgets and Cost Anomaly Detection are budget alerts plus ML monitors that flag unusual spend. They save by catching regressions early (egress, NAT, unintended transfers), which is crucial when datasets and access patterns evolve around Archil. Use immediately, account-wide; route to email/SNS/Slack with a simple triage playbook.

The Gameplan Summary

Start by making S3 your source of truth and let prices fall as data cools. Turn on S3 intelligent-tiering and add lifecycle rules to push older objects into Glacier without any app changes. Then mount those same buckets through Archil, which gives your services a fast, POSIX “local” volume without copying terabytes onto EBS or EFS.

On compute, cover your steady, always-on footprint with Compute Savings Plans and run on Graviton to get more throughput per dollar. For bursts, lean on EC2 of Fargate Spot so you’re not paying On-Demand rates for spiky loads. When instances no longer need oversized CPU to “babysit” data, use AWS Compute Optimizer to rightsize them down. For any scratch space you still need, standardize on EBS gp3 so you’re not overpaying for gp2 and can dial throughput independently. Finally, wire up Budgets + Cost Anomaly Detection so any regression (eg. egress spikes, NAT leaks, mis-tagged resources) pings you before it becomes an invoice.

This stack works because Archil keeps hot bytes near compute and cold bytes cheap in S3, so everything above it gets smaller and more elastic. To optimize for shrinking instances: Spot becomes practical, databases idle safely, and the network stops taxing you for every hop. It’s the same workloads, just fewer copies, smarter pricing, and guardrails that keep savings locked in.

You don’t need all ten switches flipped on day one to see real savings. Think of them as a toolbox: some trim steady-state costs (Savings Plans, Graviton), some crush spikes (Spot), some make storage smarter (S3 tiers, gp3, EFS-IA), and others plug the leaks you can’t see (VPC Endpoints, CloudFront, Anomaly Detection). The question is how they work together—and where Archil fits—so you’re not just saving in one line item while overspending in another. Here’s the opinionated setup that stitches those tools into a coherent, cost-efficient stack you can roll out quickly.

Metrics to Watch

Tracking what changes behavior instead not vanity graphs will be critical in cost optimization.This includes:

Spend

Unit costs force honest decisions:

• $/request tells you if a new feature is efficient or just popular
• $/tenant shows which plan types are underwater
• $/ACU ties DB spend to actual capacity
• egress/GB surfaces hidden taxes (NAT, CDN, S3) so you fix routing or caching instead of buying more compute

Efficiency

These reveal waste you can reclaim:

• CPU/Memory headroom (at P95+) shows rightsize potential without risking latency
• Cache hit ratio when rising means fewer origin fetches and lower egress
• Spot coverage quantifies how much burst you’ve moved off On-Demand
• Savings Plans coverage and utilization confirms your commitments are the right size (high utilization) and growing with baseline.

Ops

Speed protects savings:

• Short time-to-detect anomalies catches egress/NAT leaks before the invoice does
• Short time-to-rollback keeps experiments cheap and reversibility real.

Closing Thoughts

Cutting your AWS bill requires a handful of sensible defaults you turn on and keep an eye on. The ten tools above do the heavy lifting: buy flexibility with Savings Plans, run on Graviton, use Spot for bursts, let S3 auto-tier, stick to gp3 for scratch, avoid the NAT tax with VPC Endpoints, cache at the edge with CloudFront, and only pay for database capacity when you’re actually using it.

This is also where Archil earns its keep. By making S3 feel like an infinite, shareable local disk, you move fewer bytes, keep cold data cheap, and let smaller (or Spot) instances do more work. You can do more work with the same workload, only with fewer copies and detours.

If you’re ready to put this into practice, sign up for Archil and point it at a real S3 bucket, or read the quickstart to see exactly how it fits into your stack. Flip the switches, watch the metrics, and turn waste back into runway.

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