2 posts tagged with "logging"

CloudWatch Bills Out of Control? A Friendly Guide to Taming Your Cloud Costs

May 7, 2025 · 5 min read

vivek

Cloud bills can feel like magic tricks—one minute, you're paying peanuts, and the next, poof!—your CloudWatch bill hits $258 for what seems like just logs and a few metrics. If this sounds familiar, don’t worry—you're not alone.

Let’s break down why this happens and walk through some practical, no-BS steps to optimize costs—whether you're on AWS, Azure, or GCP.

Why Is CloudWatch So Expensive?#

Illustration of people thinking about cloud costs

CloudWatch is incredibly useful for monitoring, but costs can spiral if you’re not careful. In one real-world case:

$258 in just three weeks
$46+ from just API requests (those sneaky APN*-CW:Requests charges)

And that’s before accounting for logs, custom metrics, and dashboards! If you're unsure how AWS calculates these costs, check the AWS CloudWatch Pricing page for a detailed breakdown.

Why You Should Care About Cloud Cost Optimization#

The cloud is flexible, but that flexibility can lead to:

Overprovisioned resources (paying for stuff you don’t need)
Ghost resources (old logs, unused dashboards, forgotten alarms)
Silent budget killers (high-frequency metrics, unnecessary storage)

The good news? You can fix this.

Step-by-Step: How to Audit & Slash Your Cloud Costs#

Illustration of a person climbing steps with a pencil, symbolizing step-by-step cloud cost reduction

Step 1: Get Visibility (Where’s the Money Going?)#

First, figure out what’s costing you.

For AWS Users:#

Cost Explorer (GUI-friendly)
AWS CLI (for the terminal lovers):
aws ce get-cost-and-usage \
--time-period Start=2025-04-01,End=$(date +%F) \
--granularity MONTHLY \
--metrics "UnblendedCost" \
--filter '{"Dimensions":{"Key":"SERVICE","Values":["AmazonCloudWatch"]}}' \
--group-by '[{"Type":"DIMENSION","Key":"USAGE_TYPE"}]'
This breaks down CloudWatch costs by usage type. For more CLI tricks, refer to the AWS Cost Explorer Docs.

For Azure/GCP:#

Azure Cost Analysis or Google Cloud Cost Insights
Check for unused resources, high storage costs, and unnecessary logging.

Step 2: Find the Biggest Cost Culprits#

In CloudWatch, the usual suspects are:
✅ Log ingestion & storage (keeping logs too long?)
✅ Custom metrics ($0.30 per metric/month adds up!)
✅ Dashboards (each widget costs money)
✅ High-frequency metrics (do you really need data every second?)
✅ API requests (those APN*-CW:Requests charges)

Step 3: Cut the Waste#

Now, start trimming the fat.

1. Delete Old Logs & Reduce Retention#

aws logs put-retention-policy \
  --log-group-name "/ecs/app-prod" \
  --retention-in-days 7  # Keep logs for just a week if possible

For a deeper dive into log management best practices, check out our guide on Optimizing AWS Log Storage.

2. Kill Unused Alarms & Dashboards#

Unused alarms? Delete them.
Dashboards no one checks? Gone.

3. Optimize Metrics#

Aggregate metrics instead of sending every tiny data point.
Avoid 1-second granularity unless absolutely necessary.
Use Metric Streams to send data to cheaper storage (S3, Prometheus).

For a more advanced approach to log management, AWS offers a great solution for Cost-Optimized Log Aggregation and Archival in Amazon S3 using S3TAR.

Step 4: Set Budgets & Alerts (So You Don’t Get Surprised Again)#

Use AWS Budgets to:

Set monthly spending limits
Get alerts when CloudWatch (or any service) goes over budget

aws budgets create-budget --account-id 123456789012 \
  --budget file://budget-config.json

Step 5: Automate Cleanup (Because Manual Work Sucks)#

Tools like Cloud Custodian can:

Delete old logs automatically
Notify you about high-cost resources
Schedule resources to shut down after hours

Bonus: Cost-Saving Tips for Any Cloud#

AWS#

🔹 Use Savings Plans for EC2 (up to 72% off)
🔹 Enable S3 Intelligent-Tiering (auto-moves cold data to cheaper storage)
🔹 Check Trusted Advisor for free cost-saving tips

Azure#

🔹 Use Azure Advisor for personalized recommendations
🔹 Reserved Instances & Spot VMs = big savings
🔹 Cost Analysis in Azure Portal = easy tracking

Google Cloud#

🔹 Committed Use Discounts = long-term savings
🔹 Object Lifecycle Management in Cloud Storage = auto-delete old files
🔹 Recommender API = AI-powered cost tips

Final Thoughts: Spend Smart, Not More#

Illustration of two people reviewing a checklist on a large clipboard, representing final thoughts and action items

Cloud cost optimization isn't about cutting corners—it's about working smarter. By regularly auditing your CloudWatch usage, setting retention policies, and eliminating waste, you can maintain robust monitoring while keeping costs predictable. Remember: small changes like adjusting log retention from 30 days to 7 days or consolidating metrics can lead to significant savings over time—without sacrificing visibility.

For cluster management solutions that simplify this process, explore Nife's Managed Clusters platform - your all-in-one solution for optimized cloud operations.

Looking for enterprise-grade cloud management solutions? Explore how Nife simplifies cloud operations with its cutting-edge platform.

Stay smart, stay optimized, and keep those cloud bills in check! 🚀

Handling Errors in C# the Easy Way

March 17, 2025 · 5 min read

Nida Sahar

You are aware that things don't always go as planned if you have ever dealt with C# or any type of online API. There are instances when you get strange JSON, when a field is missing, and when—well—things just break. The good news is that you don't have to let your app crash and burn because of such problems. We can apprehend them, record them, and continue on.

I'll demonstrate how to use a custom error response object to handle errors in C# in this post. It's similar to building a safety net for your software so that it doesn't go into full panic mode when something goes wrong.

Why Do We Care About Custom Error Responses?#

It's not always sufficient to simply log or print an error to the console when it occurs in your application. You may want to provide more information about the issue, track several faults that occur simultaneously, or simply deliver a kind, easy-to-read message to the user. A custom error answer can help with that.

With a custom error response object, you can:

Track different types of errors.
Organize your errors into categories (so you know if it's a JSON issue, a database issue, etc.).
Handle the error, log it, and then move on without crashing the app.

Setting Up Our Custom Error Object#

Let's start by setting up a basic error response object. This will hold our error messages in a dictionary, so we can track multiple types of errors.

Here's how you can do it:

public class ErrResponse
{
    public string Message { get; set; }
    public Dictionary<string, List<string>> Errors { get; set; }
}

Message: This is just a generic message about what went wrong.
Errors: This is a dictionary that'll hold all the different errors. Each key will represent an error type (like "JsonError" or "GeneralError"), and the value will be a list of error messages. This way, we can keep things organized.

Deserializing JSON and Handling Errors#

Let's say you're deserializing some JSON data, but there's a chance it could fail. Instead of letting the program crash, we can catch that error, store the details in our custom error response, and continue running. Here's how to do it:

using Newtonsoft.Json;
using System;
using System.Collections.Generic;

public class Program
{
    public static void Main()
    {
        string jsonContent = /* your JSON string here */;
        ErrResponse errResponse;

        try
        {
            // Try to deserialize the JSON
            errResponse = JsonConvert.DeserializeObject<ErrResponse>(jsonContent);

            if (errResponse != null)
            {
                Console.WriteLine("Deserialization successful.");
                Console.WriteLine($"Message: {errResponse.Message}");

                if (errResponse.Errors != null)
                {
                    foreach (var error in errResponse.Errors)
                    {
                        Console.WriteLine($"Error Key: {error.Key}, Values: {string.Join(", ", error.Value)}");
                    }
                }
            }
            else
            {
                Console.WriteLine("Deserialization resulted in a null response.");
            }
        }
        catch (JsonException ex)
        {
            // If JSON deserialization fails, log it
            errResponse = new ErrResponse
            {
                Message = "There was an issue with the JSON.",
                Errors = new Dictionary<string, List<string>>()
            };

            // Add the error to the "JsonError" category
            AddError(errResponse, "JsonError", ex.Message);
            AddError(errResponse, "JsonError", ex.StackTrace);

            Console.WriteLine($"JSON Deserialization error: {ex.Message}");
        }
        catch (Exception ex)
        {
            // Catch any other errors that might happen
            errResponse = new ErrResponse
            {
                Message = "Something unexpected went wrong.",
                Errors = new Dictionary<string, List<string>>()
            };

            // Log the general error
            AddError(errResponse, "GeneralError", ex.Message);
            AddError(errResponse, "GeneralError", ex.StackTrace);

            Console.WriteLine($"Unexpected error: {ex.Message}");
        }

        // Continue running the app, no matter what
        Console.WriteLine("The program keeps on running...");
    }

    // Utility to add errors to the response
    private static void AddError(ErrResponse errResponse, string key, string message)
    {
        if (string.IsNullOrEmpty(message)) return;

        if (errResponse.Errors.ContainsKey(key))
        {
            errResponse.Errors[key].Add(message);
        }
        else
        {
            errResponse.Errors[key] = new List<string> { message };
        }
    }
}

What's Going On Here?#

Deserialization: We attempt to create our ErrResponse object from the JSON. Fantastic if it does. If not, the error is detected.
Catching JSON Errors: If the JSON is incorrect, we detect it and use a JsonError value to add it to our Errors dictionary. The error notice and stack trace are then displayed for simpler debugging.
General Error Handling: We detect and record any unexpected events (such as database problems or network failures) under the GeneralError key.
Program Doesn't Crash: The software continues to operate after the problem has been handled. Thus, without ruining anything, you can log issues, alert someone, or simply go on.

Why This Is Useful#

It Keeps Things Neat: We store errors in an organised manner that makes it simple to see what's wrong, as opposed to simply throwing them around.
Multiple Errors? No Problem: We don't have to overwrite or overlook anything when we use a dictionary to track numerous faults at once.
No App Crashes: In the event that something breaks, the program continues to operate. You recognise the mistake, correct it, and move on with your life.

Conclusion#

Error management doesn't have to be difficult. You may effortlessly handle failures, record crucial information, and maintain the functionality of your program by utilising a custom error response object in C#. There are ways to deal with issues like a broken JSON string or an unplanned crash without everything exploding.

Therefore, bear in mind to identify the mistake, manage it politely, and continue working on your program the next time something goes wrong.

If you're looking for cutting-edge features for cloud deployment, check out what Oikos by Nife has to offer.

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