Reduce Backend Costs by Migrating to Serverless Microservices
Bluell AB
We are spending more on idle servers than on actual users. Our backend is bloated, slow, and nobody wants to touch it anymore.”
That’s what one founder told us before we rewired their infrastructure. And they are not alone. If your backend feels like a monolith of duct tape and cloud bills, you are not paying for performance, you are paying for waste. The good news? You can cut backend costs without cutting functionality. And serverless microservices are the play.
Let’s break down what that means, why it works, and how to make it real.
What’s Draining Your Backend Budget?
Before we jump to solutions, let’s look at the pain. Here’s where most teams are bleeding money in the backend:
Over-provisioned servers: You are paying 24/7 for compute power that peaks for 20 minutes a day.
Under-utilized instances: Some services run 5% of the time, but still hog full VM allocations.
Scaling bottlenecks: When one service breaks, the whole system drags. You scale everything, not just the hot path.
DevOps overhead: Too many moving parts = too many humans needed to babysit deployments, uptime, and logs.
Slow iteration: Every change becomes a mini-rewrite. That’s developer hours you don’t get back.
If that feels familiar, it’s time to consider a model that lets you pay only for what you use and split your backend into bite-sized, deployable units.
What Are Serverless Microservices?
Let’s keep it simple.
Microservices: Breaking your backend into small, independent components that each do one thing well (like “Send Email,” “Update Cart,” or “Authenticate User”).
Serverless: Running those components in short-lived, event-triggered containers (e.g., AWS Lambda, Azure Functions, Google Cloud Functions) so you only pay when code is running.
Combine them, and you get serverless microservices, a way to run your backend as a collection of on-demand functions that scale automatically, cost less, and reduce DevOps complexity.
Think of it like this:
You are no longer renting a building 24/7 for one meeting a day. You are renting the room only for the hour you use it, and walking out with zero maintenance costs.
Why This Actually Cuts Costs
We have seen teams cut backend infrastructure costs by 30% to 70% just by switching to a serverless microservices model.
Here’s why it works:
You stop paying for idle time. Functions only run when needed. No more zombie instances draining money.
Granular scaling. A spike in the “checkout” function doesn’t trigger scaling for the entire system. Each function scales independently.
Fewer people, fewer tools. You don’t need full-time DevOps engineers managing CI/CD pipelines, load balancers, or Kubernetes clusters. You push code, and it runs.
Built-in fault isolation. One service crashes? It doesn’t pull down the rest. That reduces debugging time and customer-facing issues.
Cheaper experimentation. Want to test a new feature? Deploy a new function in minutes without risking the core app.
This isn't theory. We have done it for startups running on fumes and scale-ups drowning in AWS bills. It works.
How to Start the Migration (Without Burning It All Down)
Let’s be real: You can’t just flip a switch and go serverless overnight. But here’s how to migrate without breaking production:
1. Identify the Low-Hanging Services
Start with parts of your backend that are:
Stateless
Event-driven
Not on the critical path (yet)
Good candidates:
User signup logic, password reset, sending emails, webhook handlers, image processing, and scheduled tasks.
You wrap those as individual serverless functions and route traffic to them gradually.
2. Set Up API Gateway and Observability
You will need a gateway (like AWS API Gateway or Azure API Management) to route requests to the right functions.
Then plug in observability, logs, metrics, and tracing because debugging serverless isn’t the same as grepping logs on a VM. Use tools like:
AWS CloudWatch, Azure Monitor, or Datadog
OpenTelemetry for tracing
Sentry or Rollbar for error tracking
3. Containerize What Doesn’t Fit (Yet)
Not every piece of your backend fits serverless right away. Some services have too many dependencies, require a long computation time, or keep connections open.
For those, containerize and run them in Fargate (AWS) or Azure Container Apps, platforms that still remove the burden of managing servers, but work for more complex logic.
4. Monitor Costs and Performance in Real Time
The whole point is to reduce cost and increase speed. So track:
Cost per function (most cloud dashboards offer this)
Cold start latency (optimize with provisioned concurrency if needed)
Error rate per function
You will know what’s working and what’s not, no guesswork.
Pitfalls to Avoid
Not everything is sunshine and savings. Here’s what to watch out for:
Cold starts: Some functions take a second or two to spin up. This can hurt UX if not managed well.
Vendor lock-in: Writing too much logic in AWS/Azure-specific syntax can make switching providers painful.
Observability gaps: If you don’t instrument early, you’ll be flying blind when something fails.
Improper granularity: Splitting every tiny task into its own function can lead to complex inter-service communications and permission hell.
But all of these are manageable, especially when you plan migration deliberately, not blindly.
Final Words: Pay for Outcomes, Not Servers
Serverless microservices are not about following trends. They are about spending money where it actually delivers value, outcomes, not uptime.
If you are tired of backend systems that burn cash but stall progress, this shift is your next move. You don’t have to rewrite everything. You just need to start small, prove value, and expand as the wins stack up.
And if you are unsure where to begin? That’s where we come in.
We have helped startups, enterprises, and product teams make this transition without downtime or drama.
Let’s talk and turn your backend into something lean, scalable, and cost-efficient.
I published this article on LinkedIn, and I'm sharing it here for educational and informational purposes only.
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