The way we build software is evolving fast—and at Zarego, we’ve leaned into the shift. Over the past few years, we’ve transitioned most of our modern platforms to cloud-native, serverless, and stateless architectures. Not just because it’s trendy, but because it works—for us and for our clients.

This article is an opinionated breakdown of why we build this way. It’s a reflection of hard-earned lessons from real-world projects, spanning industries like fintech, healthcare, logistics, and green-tech. And it’s our take on how to stay scalable, secure, and cost-efficient in today’s fast-moving digital landscape.

The Old Way Was Holding Us Back

Let’s start with a familiar scene.

You deploy a new feature. A traffic spike hits. Suddenly, your server’s CPU maxes out. The database crawls. Alerts go off. You scramble to provision more resources, adjust load balancers, or patch memory leaks. Or worse—your product just goes down.

For many teams (especially early-stage startups or lean enterprise teams), managing infrastructure has become a constant juggling act. Uptime. Cost. Performance. Scalability. Complexity.

That’s what drove us to change how we build.

What “Serverless” Actually Means

Let’s be clear: serverless doesn’t mean “no servers.” It means you, as a developer or product team, don’t manage the servers. Cloud providers like AWS handle the provisioning, scaling, and infrastructure so you can focus on code.

At the heart of our approach is AWS Lambda—a serverless compute service that runs your code in response to events. You don’t think about instances. You don’t keep anything “running.” You just write the logic, set the triggers, and deploy.

Paired with DynamoDB (a fully managed, serverless NoSQL database), this gives us a truly event-driven and stateless architecture.

Stateless by Design

Why stateless?

In traditional architectures, a lot of problems stem from holding state in the wrong place—memory-heavy web servers that need to remember sessions, or monolithic backends that create dependencies between different components.

By contrast, stateless functions start fresh on every invocation. That means:

• No session baggage
  
• No “stuck” processes
  
• Better fault tolerance
  
• Easier parallelism
  

If a Lambda crashes, the system keeps going. If traffic spikes, functions scale horizontally without limits. And if one part of the system fails, it doesn’t drag the whole platform down with it.

In essence: stateless systems are more resilient by default.

Why We Love Lambda

We’ve used AWS Lambda across multiple client projects, and here’s what keeps us coming back:

Simple Mental Model

Each function has a single responsibility. That forces clean separation of concerns—no god-objects, no tangled dependency chains. It aligns beautifully with modern engineering principles.

Instant Elasticity

Whether it’s 5 users or 5,000, Lambda functions auto-scale in milliseconds. There’s no pre-warming, no load balancing setup. This is especially crucial for event-driven workloads or bursty traffic.

Pay-as-You-Go

You’re billed per invocation, measured in milliseconds. Clients love it because the cost curve tracks real usage. No more paying for idle servers.

Secure by Default

Since each function runs in its own sandboxed environment, the attack surface is much smaller. You can define granular IAM policies for each function, reducing lateral risk.

Why We Use DynamoDB (and Sometimes MongoDB)

Relational databases have their place—but for many real-world use cases, DynamoDB just makes more sense.

It’s fast (sub-10ms reads), massively scalable (millions of requests per second), and requires zero ops. It’s ideal for:

• User sessions or tokens
  
• IoT data ingestion
  
• Event logs and audit trails
  
• Product catalogues
  
• Usage counters or analytics
  

Since DynamoDB is also stateless from the application’s perspective, it works seamlessly with AWS Lambda. We don’t worry about connection pooling, provisioning read/write capacity (we use on-demand), or managing backups (it’s automatic). We simply define access patterns and let the system handle the rest.

That said, we also use MongoDB—especially in projects where a more flexible document model is required or where teams already have experience with it. MongoDB’s schema-less nature works well for:

• Prototyping or early-stage products
  
• Applications with highly nested or variable data
  
• Systems requiring full-text search or aggregation pipelines
  
• Cross-platform apps with shared JSON structures
  

We typically deploy MongoDB using MongoDB Atlas, which gives us managed hosting, autoscaling, and easy integrations with cloud services. For some hybrid stacks, we’ve even used MongoDB in conjunction with DynamoDB—each playing to its strengths.

The bottom line: we choose the database that best matches the access patterns, scalability needs, and complexity of the product. In many cases, DynamoDB is the default. But when flexibility and developer speed are critical, MongoDB earns its place too.

The Cloud-Native Mindset

Beyond the tools, it’s really about the mindset. Building cloud-native means:

• Designing for failure
  
• Embracing automation
  
• Observing everything
  
• Scaling horizontally
  
• Decoupling services
  

We use services like API Gateway, Step Functions, EventBridge, and SQS to orchestrate flows and handle async events. We embrace CI/CD pipelines with zero-downtime deployments. We bake observability in from the start using CloudWatch, X-Ray, and structured logging.

This isn’t just for tech’s sake—it’s to ship faster, break less often, and adapt as businesses grow.

Case Study: Green Delivery at Scale with VGreat

VGreat is a sustainable delivery platform that connects eco-conscious consumers with plant-based brands. For their launch, they needed a backend that aligned with their values—scalable, lightweight, and low-impact—without compromising on speed or flexibility.

We built their infrastructure using AWS Lambda, DynamoDB, and EventBridge, which allowed us to:

• Support dynamic delivery routing and vendor onboarding
  
• Handle order spikes and flash promotions without pre-provisioning servers
  
• Automate real-time status updates and notifications through event-driven flows
  
• Eliminate idle infrastructure to reduce both costs and energy consumption
  

By going fully serverless and stateless, VGreat was able to scale their operations quickly while staying true to their mission of low-footprint logistics. The result: a green stack powering a green business.

Misconceptions About Serverless

Despite all this, serverless still gets a bad rap. Let’s debunk a few myths:

“It’s only for small apps.”

False. Companies like Netflix, Coinbase, and iRobot use serverless at scale. It’s especially powerful when paired with microservice architectures and event-driven flows.

“Cold starts will kill my UX.”

Cold starts have improved significantly. With Node.js or Python, cold starts are now often under 200ms. You can also mitigate them with provisioned concurrency for critical paths.

“You can’t run background tasks.”

Wrong. We use EventBridge, Step Functions, and SQS to build robust asynchronous workflows—everything from email sequences to invoice reconciliation.

“Vendor lock-in is a deal-breaker.”

Yes, you’re tying into AWS—but you gain huge velocity. And abstractions like the Serverless Framework or AWS CDK make it easier to migrate later if needed.

When Not to Go Serverless

We’re not dogmatic. Serverless isn’t a silver bullet. It may not be the best fit when:

• You need long-running processes (e.g. video encoding > 15 mins)
  
• You’re doing heavy in-memory computation (ML training, large matrix ops)
  
• You require stateful sockets or persistent connections (e.g. multiplayer gaming)
  

For these, we’ve used Fargate, ECS, or even traditional EC2 setups when necessary.

The key is: use the right tool for the job. Just don’t default to the old model without questioning it.

What This Means for Our Clients

When we propose a serverless stack to clients, here’s what they typically gain:

• Lower Total Cost of Ownership (TCO) – no idle infra, less overhead
  
• Faster time to market – deployment in days, not weeks
  
• Greater reliability – built-in fault tolerance and retry logic
  
• Elasticity from day one – launch with confidence, scale without rewrites
  

And most importantly: less operational burden. Your developers build features. Your system scales itself. Your business grows.

Final Thoughts

We don’t build serverless apps because it’s fashionable. We build them because they’re faster to launch, cheaper to run, easier to scale, and more resilient under pressure.

Statelessness isn’t a constraint—it’s a superpower. When you stop depending on fragile state, everything else gets easier to reason about.

Serverless isn’t less powerful—it’s power without the weight.

And cloud-native isn’t just a buzzword—it’s a discipline. A way of thinking. A long-term investment in flexibility and speed.

Ready to scale without the overhead?

Let’s talk about how to bring serverless flexibility to your product 🚀
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