How Leveraging Serverless Observability Tools Can Slash Your AWS, Azure, and GCP Bills

Let’s talk about something that keeps every cloud developer up at night: the monthly bill. You’re not alone if your cloud costs creep up—especially with serverless. I’ve spent years as a FinOps specialist, and I’ve seen too many teams jump into serverless (AWS Lambda, Azure Functions, Google Cloud Functions) expecting savings, only to find their costs still climbing. Why? A lack of visibility. That’s where **serverless observability** comes in.

Serverless is powerful: no servers to manage, automatic scaling, pay-per-use. But its “invisible” nature cuts both ways. Without the right tools, it’s easy to lose track of what’s costing you. The magic happens when you use observability tools that dig deeper than basic monitoring. They give you the details: function performance, resource use, and even which part of your app is driving up costs. Suddenly, your spending isn’t a mystery—it’s a map for savings.

Why Serverless Observability Is the Key to Smarter Cloud Spending

Serverless means your code runs without a fixed server. That’s great, but it also means traditional cost tools don’t cut it. They can’t easily connect your Lambda function to your S3 bucket trigger or your API call. Serverless observability fixes this by:

• Showing which business process each function supports
• Spotting slow, expensive cold starts
• Tying costs back to events like API requests or file uploads
• Finding functions that run but no one uses anymore (“zombie functions”)

Think of it like this: without observability, you’re driving with a blindfold. With it, you see exactly where your money goes. That clarity lets you make smarter choices—whether you’re on AWS, Azure, or GCP.

The Real Costs of “Out of Sight, Out of Mind” Serverless

I worked with a SaaS company that moved to AWS Lambda to ditch EC2 costs. They saved 60% on compute, but their overall bill kept rising. They had no clue why. We added Datadog and AWS X-Ray. The findings were eye-opening:

• 34% of Lambda calls came from an old S3 rule—deleted, but still firing
• 12 functions were set to low memory, taking 10+ seconds (costing 4x more than needed)
• 78% of one reporting function was from a nightly job that hadn’t been used in months

Simple fixes: we adjusted memory, removed the dead trigger, and shut down the unused function. Their serverless bill dropped by 52% in just one month. No magic—just visibility.

How Observability Tools Turn Costs into Clarity (The FinOps Advantage)

FinOps isn’t just about cutting costs. It’s about making smart choices. Serverless observability gives you the data to do that. It connects your spending to real things: specific functions, versions, even the events that trigger them. Here’s how:

1. See Exactly What Each Function Costs

Tools like AWS Cost Explorer (with Lambda tags), Azure Cost Management (with resource tags), and GCP Billing (with Cloud Function labels) break costs down by function, team, or environment. But observability tools go further by:

• Showing cost per individual run
• Tracking costs to services used (like DynamoDB, Cosmos DB, BigQuery)
• Finding “expensive outliers” (functions that cost way more than similar ones)

Real example: A financial app found a fraud-check function was calling DynamoDB 150 times per request—because of nested loops in the code. Fixing the query cut DynamoDB costs by $3,200/month. Observability found the needle in the haystack.

2. Fix Cold Starts and Slow Functions

Long cold starts double your cost. Observability tools track:

• How long the first byte takes (cold start time)
• Total function run time
• Memory usage (to catch over- or under-sized functions)

What to do with this data:

• Cut memory for functions using less than half of what’s assigned
• Boost memory for CPU-heavy functions (more RAM = faster = cheaper)
• For critical, slow paths, use provisioned concurrency to eliminate cold starts

Code snippet (AWS Lambda memory tweak):


# Started with: 1024 MB RAM, took 8.2s
# Changed to: 2048 MB RAM, took 1.4s
# Result: 83% faster, 25% cheaper per run


Save Money Across AWS, Azure, and GCP

You don’t need separate tools for each cloud. Platforms like Datadog, New Relic, and OpenTelemetry give you one dashboard for all. Here’s where to focus:

AWS Cost Optimization

• Use CloudWatch Logs + Lambda insights to track speed and errors
• Link to S3, DynamoDB, and API Gateway to trace what’s triggering costs
• Set budget alerts for functions that cost too much

Azure Billing & Efficiency

• Turn on Application Insights for Azure Functions
• Use Azure Monitor to see Function App costs vs. App Service Plans
• Filter Azure Cost Management reports by function name or resource group

GCP Savings

• Use Cloud Monitoring to track Cloud Function run times
• Tag functions with labels to group costs by team or service
• For steady workloads, use commitments (like 1-year reservations) to save

Case Study: $18K/Month Saved—Just by Looking Closer

A fintech startup on AWS Lambda was spending $35K/month on serverless. Their observability dashboard showed:

• 40% of their cost came from just 3 functions processing CSV files
• These functions ran 25+ seconds because of low memory (512MB) and slow CPU
• They were triggered every 15 minutes by S3 (way too often for their data volume)

What we did:

1. Boosted memory to 1792MB → cut run time to 4.5s
2. Switched to hourly batches (cutting runs by 92%)
3. Used Step Functions to streamline, avoiding repeated DynamoDB calls

Result: $18K/month saved (51% cheaper). Same results, way less money.

Your Serverless Cost-Cutting Checklist

Ready to start? Here’s how:

1. Tag Every Function

Use clear tags like team:backend, env:prod, cost-center:marketing on every function. Use Terraform or CDK to make tagging automatic.

2. Set Alerts (Don’t Wait for a Surprise Bill)

Create alerts for:

• Functions running more than 1000 times/day
• Run times over 5 seconds
• Spending up 20% or more from last month

3. Audit Every 90 Days

Every quarter, check:

• Functions with no runs in 30+ days
• Functions using less than 60% of their memory
• Functions with lots of errors (might mean retries piling up costs)

4. Test, Don’t Guess: Right-Size Your Functions

Use tools like AWS Lambda Power Tuning or Azure Functions Profiler to test different memory and CPU settings. Find the sweet spot: fast enough, cheap as possible.

Observability: Your Best Tool for Cloud Savings

Think of it like searching for hidden value. FinOps isn’t about slashing budgets—it’s about finding waste. In 2025, the best “find” isn’t a rare coin. It’s understanding your serverless workloads. When you apply these steps, you’ll:

• Cut serverless costs on AWS, Azure, and GCP by 30–60%
• Deploy code that’s faster and more efficient
• Make teams accountable with clear cost data

The cloud’s future isn’t about spending more. It’s about spending smarter. Start your review today. The savings are already there—you just need to see them.

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