magnascale
Menu
FROM THE BLOG

How to Slash Your Cloud Costs by 90% in Just 2 Weeks (For Free!)

November 24, 2024Magnascale

Let’s be honest: cloud bills can get out of control fast. If you’ve ever looked at your AWS or Google Cloud invoice and thought, “This can’t be right!”, you’re not alone. The good news? You can cut up to 90% of your cloud costs in as little as two weeks without spending a dime upfront. Yes, you heard that right—no extra software, no fancy tools required.

In this post, I’ll walk you through step-by-step how to spot the waste in your cloud account, clean it up, and keep those savings rolling in. We’ll use examples for AWS and Google Cloud, and I’ll even throw in some tools like AutoSpotting for AWS that can take your savings to the next level.

Why Cloud Costs Get Out of Hand

Before we dive into the fixes, let’s quickly talk about why cloud bills grow faster than weeds in your backyard:

  • Idle Resources: These are instances, databases, or other services you’re paying for but barely using.
  • Over-Provisioning: You’ve reserved far more capacity than you actually need, just to be safe.
  • Data Transfer Costs: Moving data between regions or out of the cloud can get very expensive.

The good news? Fixing these issues is easier than you think. Let’s jump into the first week of your cost-cutting journey.

Week 1: Find and Fix the Biggest Cloud Wasters

Step 1: Audit Your Cloud Usage

When auditing your cloud usage, the goal is to get a clear, detailed picture of where your money is going. This step is crucial because it sets the foundation for everything else you’ll do to cut costs. Here’s how to make the most of your audit:

Break Down Your Cloud Bill

Cloud bills can be intimidating, with dozens (or even hundreds) of line items. To make sense of it, start by categorizing your expenses:

  • Compute Costs: These are usually your largest expense and include instances (like EC2 on AWS or Compute Engine on GCP), container services, and serverless computing (like Lambda or Cloud Functions).
  • Storage Costs: Look at your data storage solutions, such as S3 buckets, RDS databases, or Persistent Disks.
  • Data Transfer Costs: Pay close attention to charges for moving data between regions, zones, or out of the cloud entirely (egress costs).
  • Networking Costs: If you’re using load balancers, NAT gateways, or content delivery networks (CDNs), these can add up quickly.

Pro Tip: AWS’s Cost Explorer and GCP’s Billing Reports can automatically break down your bill into these categories.

Analyze Trends Over Time

Don’t just focus on your current bill—look at your costs over the last few months. This helps you spot trends like:

  • Sudden Spikes: Did costs jump unexpectedly? Check if new resources were spun up without proper monitoring or tagging.
  • Persistent Over-Spending: Are certain services consistently over budget? These could be prime candidates for optimization.
  • Seasonal Patterns: If your workload varies seasonally (e.g., more traffic in December for retail businesses), use this data to plan scaling strategies.

Pro Tip: Use the forecasting feature in AWS Cost Explorer to predict future spending and identify areas to optimize.

Look for High-Cost Resources

Focus your attention on the top spenders in your cloud environment. These are usually:

  • Large Instances: High-performance EC2 or Compute Engine VMs running 24/7 can be major cost drivers.
  • Unoptimized Databases: RDS instances on AWS or Cloud SQL on GCP that are over-provisioned or idle.
  • High-Volume Data Storage: S3 buckets or Persistent Disks holding large amounts of rarely accessed data.
  • Networking Costs: NAT gateways, load balancers, or traffic-heavy CDNs can generate significant costs, especially if resources span multiple regions.

Pro Tip: Tag your resources! AWS and GCP allow you to tag resources (e.g., department=marketing or project=test-environment), making it easier to identify and group high-cost items by function or team.

Set Up Alerts for Future Overspending

To stay ahead of your costs, set up alerts for when spending exceeds your budget:

  • AWS Budgets: Use this tool to create cost or usage thresholds for specific services or accounts. You’ll get an email alert when you’re nearing the limit.
  • GCP Billing Alerts: Create notifications for when your monthly spend approaches a pre-set amount.

Pro Tip: Combine budget alerts with detailed monitoring tools like AWS CloudWatch or GCP Monitoring to understand what triggered unexpected costs.

Dive Into Resource Utilization Metrics

Cost isn’t the only thing you should be looking at—utilization is just as important. Many high-cost resources may not actually be doing much work. Here’s what to check:

  • Compute Instances: Check CPU and memory utilization. If an instance is running at 10% CPU and 15% memory, it’s probably oversized.
  • Databases: Look at metrics like read/write IOPS (Input/Output Operations Per Second). Idle or low-traffic databases are prime candidates for downsizing or consolidating.
  • Storage: Evaluate how often data in your buckets or disks is accessed. Cold data can be moved to cheaper storage tiers.

Pro Tip: Use AWS Trusted Advisor (link) and Google Cloud Monitoring (link) to get utilization insights.

Check Your Free Tier Usage

If you’re using AWS or GCP’s free tier, make sure you’re not accidentally exceeding the limits, which can lead to unexpected charges. For example:

  • AWS Free Tier: Includes 750 hours/month of EC2 t2.micro or t3.micro usage, plus 5GB of S3 storage. Going over these limits incurs charges.
  • GCP Free Tier: Offers 1 F1-micro instance per month, 5GB of storage, and 1GB of network egress from North America.

Pro Tip: Keep a close eye on services that started as “free” but grew over time, silently crossing free-tier limits.

Take Notes and Prioritize

As you go through your audit, take notes on the biggest cost drivers and the easiest opportunities for savings. For example:

High-Impact Opportunities: Services like load balancers or Spot Instances that can dramatically cut costs with some configuration tweaks.

High-Cost, Low-Effort: An idle EC2 instance or orphaned Persistent Disk that you can shut down immediately.

High-Cost, Medium-Effort: A large RDS database that could be downsized or moved to a more cost-effective pricing plan.

Step 2: Shut Down Idle Resources

Now that you’ve audited your cloud usage and have a list of underutilized resources, it’s time to clean house. Idle resources are like leaving the lights on in an empty room—they’re costing you money without providing any value. Shutting these off is one of the fastest ways to see an immediate reduction in your cloud bill.

Here’s a deeper look at how to identify and handle idle resources in AWS and Google Cloud.

Why Idle Resources Are a Problem

Idle resources are common in cloud environments, especially in organizations with multiple teams or projects. Here’s why they can go unnoticed:

  • Temporary Test Environments: These are often spun up for testing or development and then forgotten.
  • Old Projects: Resources from past projects may still be running long after they’re no longer needed.
  • Poor Monitoring Practices: Without proper monitoring and tagging, idle resources can easily slip through the cracks.

Real Impact: I once helped a client shut down a set of test instances they forgot about. This simple cleanup saved them $1,000 per month—money that was essentially going down the drain.

Step 2.1: Spot Idle Resources in AWS

AWS provides several tools to help you identify idle or underutilized resources. The most helpful are:

  1. AWS Trusted Advisor:
    • What It Does: Highlights idle or underutilized resources like EC2 instances, RDS databases, and Elastic Load Balancers (ELBs).
    • How to Use It:
      1. Log in to the AWS Management Console.
      2. Open Trusted Advisor under the “Management and Governance” section.
      3. Check the “Cost Optimization” tab for recommendations, such as stopping or resizing unused EC2 instances.
    • Example: Trusted Advisor might flag an EC2 instance running at 5% CPU utilization for weeks.
  2. EC2 Usage Reports:
    • What It Does: Displays all running instances, along with their usage metrics.
    • How to Use It:
      1. Navigate to the EC2 dashboard.
      2. Filter instances by tags (e.g., environment=dev) to isolate non-production resources.
      3. Cross-check utilization data to identify idle instances.
  3. Elastic Load Balancers (ELBs):
    • Idle Balancers: ELBs that are no longer attached to active EC2 instances still incur costs.
    • How to Identify:
      1. Go to the ELB dashboard.
      2. Check for balancers with no active targets or low request volumes.

Quick Tip: Use AWS Resource Groups to group and manage resources by tags, which can make finding unused assets much easier.

Step 2.2: Spot Idle Resources in Google Cloud

Google Cloud offers powerful tools to identify idle resources, with an emphasis on real-time monitoring and recommendations:

  1. Google Cloud Monitoring:
    • What It Does: Provides insights into resource usage, including Compute Engine VMs, storage buckets, and databases.
    • How to Use It:
      1. Open Cloud Monitoring in the GCP Console.
      2. Set up a custom dashboard to view usage metrics for VMs, such as CPU and memory utilization.
      3. Look for VMs with consistently low utilization over time (e.g., <10% CPU for 30 days).
  2. Idle Persistent Disks:
    • Why It Matters: Disks attached to deleted VMs often remain active and incur storage costs.
    • How to Identify:
      1. Open the Disks page in the Compute Engine section of the GCP Console.
      2. Look for disks marked as “unattached” and consider deleting or reassigning them.
  3. Cloud SQL Databases:
    • Idle Databases: These databases can sit idle while still racking up hourly costs.
    • How to Identify:
      1. Navigate to the Cloud SQL section in GCP.
      2. Check for databases with low query rates or little to no activity over weeks.

Quick Tip: Use Cloud Asset Inventory to get a bird’s-eye view of all resources in your GCP account and quickly identify unused assets.

Step 2.3: Automate the Cleanup Process

For larger organizations with hundreds of resources, manually shutting down idle assets can be time-consuming. Consider automating cleanup tasks:

  • AWS Instance Scheduler: Automatically stop or terminate instances based on a schedule (e.g., shut down non-critical environments after business hours).
  • Google Cloud Recommender: Provides actionable recommendations to shut down or resize underutilized resources.

Pro Tip: Combine these tools with tagging policies to ensure future resources are easy to track and manage. For example, tag resources with owner=team-name or environment=dev to quickly identify and clean up non-critical assets.

Step 2.4: Validate Before Deleting

Before shutting down or deleting any resource, double-check its purpose. A resource may appear idle but could be critical in specific scenarios. To avoid accidental downtime:

  • Review resource tags to see if they indicate ownership or purpose.
  • Consult with your team or check documentation to confirm whether the resource is still in use.
  • Use tools like CloudTrail (AWS) or Cloud Logging (GCP) to verify activity logs.

Real Example: One client accidentally terminated a backup server labeled as idle because it wasn’t actively processing requests—but it was critical for disaster recovery. A quick check of their tags and activity logs could’ve prevented this mistake.

Step 2.5: Immediate Wins from Cleaning Up

Here’s what to expect once you shut down idle resources:

  • Lower Monthly Costs: You’ll notice an immediate reduction in your monthly bill, sometimes within hours.
  • Simpler Cloud Environment: Fewer resources make it easier to manage and optimize your infrastructure.
  • Reduced Security Risk: Shutting down unused assets decreases the attack surface for potential cyber threats.

Real Impact: A client of mine shut down several unused Compute Engine VMs and Persistent Disks, saving $3,500 in the first month alone.

Cleaning up idle resources is one of the easiest, fastest ways to reduce cloud waste. Once you’ve eliminated these unnecessary costs, you’re ready to move on to Step 3: Right-Size Over-Provisioned Resources, where the real fine-tuning begins.

Step 3: Right-Size Over-Provisioned Resources

Even resources you’re actively using might be overkill for the job they’re doing. Maybe that database or virtual machine (VM) was originally provisioned for peak performance, but over time, the workload changed, and it no longer needs as much horsepower.

Right-sizing is all about matching resource capacity to actual usage. Let’s dive into how to make this work effectively.

What Does Right-Sizing Mean?

Right-sizing involves adjusting the size of your cloud resources—whether it’s compute instances, storage disks, or databases—so they’re just big enough to handle your workload without wasting money on unnecessary capacity. This applies to:

  • Compute Instances: EC2 on AWS or Compute Engine VMs on GCP.
  • Storage: EBS volumes, Persistent Disks, or database storage.
  • Databases: AWS RDS instances or Google Cloud SQL databases.

Quick Win: A single right-sizing adjustment can cut your costs by 30-50% on that resource.

How to Right-Size in AWS

AWS provides several tools to make right-sizing easy:

  1. Trusted Advisor Rightsizing Recommendations:
    • What It Does: Analyzes your EC2 and RDS usage to suggest smaller (or more efficient) instance types.
    • How to Use It:
      1. Open AWS Trusted Advisor under the Management and Governance section of your AWS Console.
      2. Go to the “Cost Optimization” tab and look for “Rightsizing Recommendations.”
      3. Review the list of instances or databases that are over-provisioned and evaluate the suggested sizes.
    Example: If you have a t3.xlarge EC2 instance with CPU utilization consistently under 20%, Trusted Advisor might suggest downgrading to a t3.medium—saving 50% or more without affecting performance.
  2. CloudWatch Metrics for EC2:
    • What It Does: Tracks instance performance metrics like CPU, memory, and disk utilization.
    • How to Use It:
      1. Open CloudWatch in the AWS Console.
      2. View performance metrics for your EC2 instances (e.g., average CPU or memory utilization).
      3. Identify instances running well below capacity thresholds.
    Pro Tip: Use CloudWatch Alarms to alert you when resource utilization consistently drops below a set threshold (e.g., <30% CPU for a week).
  3. RDS Recommendations:
    • What It Does: Suggests right-sizing or storage adjustments for AWS RDS databases.
    • How to Use It:
      1. Navigate to the RDS Dashboard.
      2. Check the “Performance Insights” section for usage patterns.
      3. If the database is underutilized, resize to a smaller instance class or switch to an optimized storage type.

How to Right-Size in Google Cloud

Google Cloud has a similarly robust system for right-sizing recommendations:

  1. GCP Recommender:
    • What It Does: Provides AI-driven recommendations for resizing Compute Engine VMs and Persistent Disks.How to Use It:
      1. Open the GCP Console and navigate to the Recommender page.Look for recommendations under “Compute Engine” or “Persistent Disks.”Apply suggested changes to optimize resource sizes.
    Example: A VM running a low-traffic web app might be over-provisioned as an n1-standard-4. Recommender could suggest scaling down to an e2-small, cutting costs by 60-70%.
  2. Google Cloud Monitoring:
    • What It Does: Displays resource performance metrics (e.g., CPU, memory, IOPS) for Compute Engine and other services.
    • How to Use It:
      1. Set up a custom dashboard to monitor underutilized resources.
      2. Check for VMs or databases that rarely exceed 10-20% CPU or memory usage.
      3. Resize these resources accordingly.
  3. Cloud SQL Right-Sizing:
    • What It Does: Provides insights into database utilization, including read/write IOPS and CPU load.
    • How to Use It:
      1. Open the Cloud SQL Dashboard.
      2. View utilization metrics to identify underutilized databases.
      3. Scale down to a smaller database instance or reduce storage size.

Right-Sizing Storage

Compute isn’t the only area where over-provisioning occurs. Storage volumes are often oversized too. Here’s how to right-size your storage:

  • AWS EBS Volumes:
    1. Open the EC2 Dashboard and navigate to your volumes.
    2. Check the “Volume Metrics” to see read/write IOPS and utilization levels.
    3. Detach or resize volumes that are significantly underused.
  • GCP Persistent Disks:
    1. Open the Disks Dashboard in Compute Engine.
    2. Look for disks with high capacity but low usage.
    3. Resize to a smaller disk type or storage tier (e.g., Standard vs SSD).

Example: A 500GB EBS volume storing backup files that haven’t been accessed in months could be resized to 100GB or transitioned to S3 Glacier.

How Right-Sizing Saves Money

Here’s a quick example of the potential savings:

  • Before Right-Sizing: A t3.xlarge EC2 instance costs approximately $165/month (on-demand pricing).
  • After Right-Sizing: Downgrading to a t3.medium costs about $42/month.

That’s 74% in savings for just one resource! Imagine the impact when you apply this across multiple services.

Right-sizing may take a bit more effort than shutting down idle resources, but the savings can be just as dramatic. Once you’ve tackled this step, you’ll be ready to explore Week 2’s Advanced Cost-Saving Techniques, like using Spot Instances and Preemptible VMs.

Week 2: Take Your Savings to the Next Level

When it comes to slashing your cloud costs, one of the most powerful tools in your arsenal is taking advantage of Spot Instances on AWS or Preemptible VMs on Google Cloud. These discounted instances offer savings of up to 90% compared to regular pricing, making them an excellent option for non-critical or flexible workloads.

Let’s explore how these instance types work, when to use them, and how to make the most of them.

What Are Spot Instances and Preemptible VMs?

AWS Spot Instances (link)

Spot Instances let you bid on spare EC2 capacity at deeply discounted rates. The tradeoff? AWS can reclaim these instances with little notice (typically 2 minutes) when demand for On-Demand capacity spikes.

They’re ideal for workloads that are:

  • Fault-Tolerant: Applications that can recover if an instance is terminated.
  • Flexible in Timing: Tasks like batch processing, rendering, or machine learning model training, where jobs don’t need to run on a fixed schedule.

Savings Potential: Spot Instances are up to 90% cheaper than On-Demand EC2 instances.

Google Cloud Preemptible VMs (link)

Preemptible VMs work similarly to AWS Spot Instances. These are short-lived Compute Engine VMs offered at significantly reduced prices. Google can terminate them at any time but typically provides a 30-second warning.

They’re perfect for:

  • Stateless Applications: Apps that don’t depend on continuous uptime.
  • Batch Workloads: Such as ETL pipelines, rendering, or CI/CD pipelines.
  • Distributed Computing: Jobs spread across multiple nodes that can handle occasional interruptions.

Savings Potential: Preemptible VMs can reduce costs by up to 80% compared to regular Compute Engine VMs.

How to Use Spot Instances in AWS

  1. Launch Spot Instances in EC2 Auto Scaling Groups:
    • Auto Scaling Groups can automatically manage the mix of Spot and On-Demand instances, balancing cost savings with availability.
    • How to Configure It:
      1. In the EC2 dashboard, create or edit an Auto Scaling Group.
      2. Select a “Mixed Instances Policy” and specify the percentage of Spot Instances vs On-Demand instances.
      3. Define instance types for flexibility—this increases your chances of getting Spot capacity.
  2. Use Spot Fleet for High Availability:
    • Spot Fleet manages a group of Spot Instances across multiple Availability Zones. It can launch replacements if instances are terminated.
    • How to Configure It:
      1. Go to the EC2 dashboard and choose “Spot Fleet Requests.”
      2. Specify instance types, regions, and pricing preferences.
  3. Set Max Bids (Optional):
    • You can set a maximum bid price to ensure you only pay a certain amount for Spot capacity.

Example: A batch processing workload running on 20 c5.large Spot Instances saved one client $2,200/month by replacing On-Demand instances.

How to Use Preemptible VMs in Google Cloud

  1. Launch Preemptible VMs in Compute Engine:
    • When creating a new VM instance in Compute Engine, select “Preemptibility” in the instance settings.
    • How to Configure It:
      1. Go to the Compute Engine Instances page in the GCP Console.
      2. Click “Create Instance” or modify an existing one.
      3. Under “Management, security, disks, networking, sole tenancy,” enable “Preemptibility.”
  2. Use Preemptible VMs with Kubernetes (GKE):
    • For containerized workloads, use GKE Node Pools configured for Preemptible VMs.
    • How to Configure It:
      1. In GKE, create a Node Pool with Preemptible VMs enabled.
      2. Use tolerations and taints to ensure only appropriate workloads run on these nodes.
  3. Combine with Autoscaling:
    • Use Google Cloud’s autoscaling features to automatically spin up more Preemptible VMs when needed and scale down when jobs are complete.

Example: A data analytics pipeline running on Preemptible VMs reduced monthly costs by 75%, saving over $1,800.

Best Practices for Spot and Preemptible Instances

  1. Diversify Instance Types:
    • Use a range of instance types and sizes to increase your chances of getting capacity. For example, instead of requesting only c5.large, include options like m5.large or t3.large.
    • AWS: Use Auto Scaling Groups or Spot Fleet.
    • GCP: Use machine families like E2, N1, or N2 for flexibility.
  2. Use Checkpointing for Long Jobs:
    • Save progress periodically so jobs can resume from where they left off if an instance is terminated.
    • Example: Use AWS S3 or Google Cloud Storage to save intermediate outputs.
  3. Blend On-Demand and Spot/Preemptible:
    • For production environments, keep a portion of instances as On-Demand to ensure reliability.
  4. Monitor Instance Availability:
    • Use AWS Spot Instance Advisor (link) to check interruption rates for different instance types.
    • In GCP, monitor Preemptible instance lifetimes to plan workloads around termination patterns.

Real-World Example: Batch Processing Job on AWS

A client was running a large-scale data processing job on 50 On-Demand EC2 instances, costing $2,500/month. By migrating the workload to Spot Instances and enabling Auto Scaling, they reduced their monthly compute costs to $300. The job completion time remained the same because Spot capacity was readily available during off-peak hours.

Using Spot Instances and Preemptible VMs is one of the fastest ways to drive significant savings on your cloud bill. With the right setup, you can enjoy dramatic cost reductions while maintaining performance for the workloads that matter. Next, we’ll explore even more ways to save with Step 6: Leverage Savings Plans and Committed Use Discounts. But, we did include an optional step 5 below for AWS users!

Step 5 (Optional!): Try AutoSpotting for AWS

For AWS users with AutoScaling Groups, AutoSpotting is a game-changer. Here’s how it works:

  1. Install it using CloudFormation or Terraform (takes less than 5 minutes).
  2. Tag your AutoScaling groups with spot-enabled=true.
  3. AutoSpotting automatically replaces your expensive On-Demand instances with Spot Instances.

It’s production-safe, keeps some On-Demand capacity for stability, and charges only 10% of the savings it generates (which is usually 90%+ savings!). For companies with big AWS spend, it pays for itself and then some. We highly suggest checking out their website, they also have an appliance in the AWS Marketplace!

Step 6: Lock in Savings with Commitments

Once you’ve optimized your cloud environment by cleaning up idle resources, right-sizing over-provisioned ones, and leveraging discounted instance types, it’s time to take things one step further: locking in savings. If your workloads are predictable, committing to long-term usage can provide significant discounts, sometimes upwards of 70%.

Let’s dive into how to lock in savings on AWS and Google Cloud, and when committing makes the most sense for your business.

What Does “Commitment” Mean in the Cloud?

Cloud providers offer discounts if you commit to using a certain amount of resources over a set period, typically 1 or 3 years. These commitments come in two flavors:

  • Flexible Commitments: You pay for a certain amount of usage but can change instance types, services, or regions within the plan. AWS Savings Plans fall into this category.
  • Fixed Commitments: You commit to using specific resources (like VM sizes) for the duration of the term. Google Cloud’s Committed Use Discounts follow this model.

Why Commitments Work: Providers offer these discounts because they can better plan capacity with predictable usage. In return, you get significantly lower prices.

How to Lock in Savings on AWS

  1. AWS Savings Plans:
    • What It Is: A flexible pricing model that applies to EC2, Lambda, and Fargate workloads.
    • How It Works:
      1. Choose a Compute Savings Plan (works across regions and instance types) or an EC2 Instance Savings Plan (specific to certain EC2 families and regions).
      2. Commit to a certain usage level (e.g., $100/month) over 1 or 3 years.
      3. Pay upfront, partially upfront, or monthly based on your budget.
    • Savings Potential: Save up to 72% compared to On-Demand pricing.
    Best For: Workloads with consistent, predictable usage, like always-on applications or long-term projects.Example:
    A client running EC2 instances for their web servers saved $4,000/year by switching to a 3-year Compute Savings Plan. Their instance family didn’t change often, so the commitment made perfect sense.
  2. Reserved Instances (RIs):
    • What It Is: The original commitment model for AWS, where you reserve specific EC2 instances for 1 or 3 years.
    • Why Use RIs Over Savings Plans? Reserved Instances might make sense for very specific, unchanging workloads, but they lack the flexibility of Savings Plans.
  3. How to Choose Between On-Demand and Commitments:
    • Review your AWS Cost Explorer usage patterns over the last 3-6 months.
    • Identify workloads that are stable or grow predictably (e.g., 24/7 databases, production servers).
    • Start with smaller commitments and increase as you gain confidence in your usage patterns.

How to Lock in Savings on Google Cloud

  1. Committed Use Discounts:
    • What It Is: A program that provides discounts in exchange for committing to a specific amount of Compute Engine resources for 1 or 3 years.How It Works:
      1. Commit to using a certain amount of vCPUs, GPUs, and memory (e.g., 4 vCPUs and 16GB of RAM).Discounts apply automatically to eligible resources.
      Savings Potential: Save up to 57% compared to On-Demand pricing.
    Best For: Teams with highly predictable workloads, such as web applications, databases, or analytics pipelines.Example:
    A data analytics team running GCP Compute Engine instances saved $3,200/year by committing to a 1-year Committed Use Discount for their batch processing jobs.
  2. Preemptible Commitments:
    If you already use Preemptible VMs for cost-saving purposes, you can combine them with Committed Use Discounts for even lower costs.
  3. BigQuery Reservations:
    If you rely on BigQuery, consider reserving query slots. This is similar to Committed Use Discounts but specifically for BigQuery workloads.

When to Commit?

Committing makes sense if your workloads meet these criteria:

  • Consistent Usage: Resources that are required 24/7 or have predictable scaling patterns.
  • Stable Workloads: Applications unlikely to change significantly in size or architecture during the commitment period.
  • Long-Term Projects: Projects expected to run for at least a year or more, like hosting production databases or running analytics pipelines.

Pro Tip: If your workloads vary significantly, consider shorter commitments (1-year plans) or flexible plans like AWS Compute Savings Plans.

How to Maximize Commitment Savings

  1. Use Free Tools to Evaluate Savings:
    • AWS: Use the Savings Plan Calculator (link) to estimate potential savings.
    • GCP: Check the Billing Reports to see historical usage and predict future commitments.
  2. Start Small:
    • If you’re new to commitments, start with a smaller commitment (e.g., $100/month) to test the waters. You can scale up later as you gain confidence.
  3. Mix Commitments and On-Demand:
    • Keep some of your workloads on On-Demand pricing to maintain flexibility. For example, commit to predictable production servers but leave dev/test environments on On-Demand or Spot pricing.
  4. Combine Commitments with Spot/Preemptible Instances:
    • Save on your core workloads with commitments while using Spot or Preemptible Instances for secondary tasks like batch processing.

Key Actions for Step 6

  1. Analyze usage patterns with AWS Cost Explorer or Google Cloud Billing Reports to identify predictable workloads.
  2. Choose the right plan: AWS Savings Plans for flexibility or GCP Committed Use Discounts for fixed resources.
  3. Start with shorter-term (1-year) commitments if uncertain about future workloads.
  4. Monitor usage over time and adjust commitments as needed.

Real-World Impact

By locking in commitments for just a handful of predictable workloads, you can achieve thousands of dollars in annual savings. A client running a steady database workload on AWS EC2 saved $4,000/year by switching to a 3-year Savings Plan. Similarly, a team using Google Cloud saved $3,200/year by leveraging Committed Use Discounts for their analytics pipeline.

With your commitments locked in, you’ve completed one of the most effective steps in reducing cloud costs. Next up, we’ll wrap up with strategies to track and sustain your savings.

Other Quick Wins

  • Enable Auto Scaling: Use AWS Auto Scaling or GKE to match capacity with demand.
  • Optimize Storage: Move cold data to cheaper tiers like S3 Glacier or GCP Nearline Storage.
  • Reduce Data Transfer Costs: Keep resources in the same region to avoid high egress fees.

Tracking and Sustaining Your Savings

Congratulations—you’ve taken significant steps to reduce your cloud costs! But cost optimization isn’t a one-time task. Cloud environments are dynamic, and without proper monitoring, it’s easy for costs to creep back up. The key to lasting savings is tracking your spending and regularly revisiting your cloud setup to ensure you’re staying on track.

Here’s how to set up alerts, dashboards, and routines to sustain your savings over the long term.

Step 1: Use Alerts to Catch Spending Spikes Early

AWS Budgets (link)

AWS Budgets is a simple yet powerful tool to track your spending and get notified when you’re nearing your predefined limits.

How to Set It Up:

  1. Open the AWS Budgets Dashboard in the Management Console.
  2. Click “Create Budget” and select a budget type (Cost, Usage, or Reservation).
  3. Set a threshold—for example, a monthly spend of $2,000—and choose how you’d like to be alerted (email, SNS notification, etc.).
  4. Optionally, set specific filters, like tracking only EC2 costs or specific accounts.

Why It Works:

  • Immediate visibility into rising costs helps you take corrective action before exceeding your budget.
  • Notifications keep your team accountable for managing resources efficiently.

Pro Tip: Combine AWS Budgets with Cost Anomaly Detection (link) to identify unusual spending patterns automatically.

Google Cloud Billing Alerts (link)

Google Cloud offers built-in billing notifications to keep your spending in check.

How to Set It Up:

  1. Go to the Billing Console in GCP.
  2. Navigate to “Budgets & Alerts” and click “Create Budget.”
  3. Define a budget and set thresholds for alerts (e.g., 50%, 75%, and 90% of your monthly budget).
  4. Add email recipients or use Pub/Sub notifications for automated actions.

Why It Works:

  • Alerts let you act on cost overruns quickly, whether it’s shutting down unused resources or scaling back unnecessary workloads.
  • You can set alerts for individual projects, making it easier to track costs for multiple teams.

Pro Tip: Pair billing alerts with GCP Recommender (link) to get actionable suggestions for reducing costs.

Step 2: Build Dashboards to Visualize Costs and Trends

AWS Cost and Usage Reports (CUR):

  • Use CUR to generate detailed reports on your spending and usage patterns.
  • Visualize trends using AWS Cost Explorer or a third-party BI tool like Tableau or Power BI.

How to Set It Up:

  1. Enable Cost and Usage Reports in the AWS Billing Dashboard.
  2. Use Athena to query the CUR data directly for custom insights.
  3. Create dashboards in QuickSight or your preferred visualization tool to track trends over time.

Google Cloud Billing Dashboards:

  • Create custom dashboards in Google Cloud Monitoring (link) to track key metrics like cost per project, service, or region.
  • Combine this data with custom alerts for comprehensive visibility.

How to Set It Up:

  1. Open the Google Cloud Monitoring console.
  2. Add billing metrics to a custom dashboard, such as spending by project or cost trends over time.
  3. Share the dashboard with team members to ensure transparency and accountability.

Pro Tip: Use GCP’s BigQuery Export for Billing (link) to analyze detailed spending data for large environments.

Step 3: Schedule Regular Reviews

Why Reviews Matter:

The cloud environment changes constantly—teams spin up new resources, workloads grow, and new services get introduced. Regular reviews ensure you’re catching new sources of waste and adapting your strategies to evolving needs.

Quarterly Reviews Checklist:

  1. Audit New Resources: Check for untagged or underutilized resources created in the last quarter.
  2. Review Rightsizing Opportunities: Use AWS Trusted Advisor or GCP Recommender to see if workloads can be downsized.
  3. Spot Growth Patterns: Look for services or projects with rising costs and investigate whether they’re justified.
  4. Evaluate Commitment Plans: As workloads grow or stabilize, consider expanding your AWS Savings Plans or GCP Committed Use Discounts.
  5. Update Policies: Ensure your team is following tagging and shutdown policies to prevent waste.

Pro Tip: Assign ownership of the review process to a cloud cost champion or FinOps team to ensure consistency.

Step 4: Enable Cost Awareness Across Teams

Make cost optimization a shared responsibility across teams by:

  • Setting Budgets per Team or Project: Use AWS Organizations or GCP Projects to allocate budgets and track costs by team.
  • Training Teams on Cost Management Tools: Ensure developers and architects know how to use tools like Cost Explorer, Billing Reports, and Recommender.
  • Implementing Tagging Standards: Require tagging for all resources (e.g., owner=team-name or project=customer-portal) to improve visibility and accountability.

Example: A client reduced waste by 15% in a quarter after implementing tagging policies and creating team-specific budgets.

Step 5: Automate Wherever Possible

To reduce manual work and improve consistency:

  • Use AWS Lambda or Google Cloud Functions to automate resource cleanup (e.g., shutting down idle instances after hours).
  • Leverage third-party tools like AutoSpotting to automatically replace On-Demand instances with Spot Instances.
  • Implement auto-scaling policies to ensure resources match workload demands dynamically.

Key Actions for Tracking and Sustaining Savings

  1. Set up budget alerts in AWS and GCP to catch spending spikes early.
  2. Build dashboards to visualize cost trends and track progress over time.
  3. Schedule quarterly reviews to identify new optimization opportunities and adjust strategies.
  4. Promote cost awareness across teams through training, tagging, and project-specific budgets.
  5. Automate routine tasks like resource cleanup and scaling to maintain efficiency.

Why Sustaining Savings Is Key

Cost optimization isn’t a one-time project—it’s an ongoing process. By continuously tracking your spending and refining your cloud setup, you can ensure that the savings you worked so hard to achieve stick around for the long haul.

Ready to take your cloud optimization to the next level? Contact us at Magnascale for expert help managing your cloud costs and keeping your environment lean and efficient.

Conclusion

Cutting your cloud costs doesn’t have to be hard, complicated, or time-consuming. By following these simple steps, you can start eliminating unnecessary expenses and optimizing your resources for up to 90% savings in just two weeks. From shutting down idle resources to right-sizing instances and using cost-efficient options like Spot Instances or Preemptible VMs, the opportunities to save are everywhere.

The best part? Most of these optimizations can be done without spending a dime upfront. All it takes is a little effort, the right tools (many of which are free), and a commitment to making cost-efficiency part of your cloud strategy.

Remember, cloud optimization isn’t just about saving money—it’s about creating an environment that’s lean, scalable, and ready for growth. Whether you’re managing a small team or running a large enterprise, these steps can have a massive impact on your bottom line.

If you’re ready to take your savings even further or want expert guidance tailored to your specific needs, contact us at Magnascale. Let’s work together to optimize your cloud spend, reduce waste, and keep more money where it belongs: in your pocket.

Start saving today—your cloud bill will thank you!