Serverless Architecture: Pros, Cons and When to Use It

"Serverless" doesn't mean there are no servers. It means you don't manage them. Your code runs in short-lived functions that the cloud provider starts, scales and stops automatically — and you typically pay only for the time your code actually runs.

This guide explains how serverless architecture works, where it shines and where it doesn't.

How serverless works

In a serverless architecture you write small functions that respond to events, such as:

• an HTTP request to your API;
• a file uploaded to storage;
• a message arriving in a queue;
• a scheduled time (for example, every night at 2am).

The provider — such as AWS Lambda, Google Cloud Functions or Azure Functions — runs the function when the event happens. Serverless architectures usually combine functions with managed services: an API gateway, a managed database such as DynamoDB, object storage and queues.

The benefits

Automatic scaling

Functions scale up when traffic spikes and down to zero when nobody's using them. You don't need to predict capacity or keep spare servers running.

Pay for what you use

With pay-per-use pricing, quiet periods cost very little. For workloads with uneven traffic — campaigns, seasonal businesses, background jobs — this can be much cheaper than always-on servers.

Less operations work

No operating system patches, no server capacity planning. Your team focuses on features instead of infrastructure.

Fast to build event-driven features

Image resizing, sending emails, processing webhooks and scheduled reports are natural fits for functions.

The trade-offs

Cold starts

When a function hasn't run recently, the provider needs a moment to start it. This "cold start" adds latency to the first request. It's often small, but it matters for latency-sensitive APIs. Provisioned capacity and lightweight runtimes reduce it.

Execution limits

Functions have limits on run time and memory. AWS Lambda, for example, has a maximum timeout of 15 minutes, so long-running jobs need a different approach or splitting into steps.

Costs at constant high load

Pay-per-use is great for spiky traffic. For heavy, constant workloads, containers or dedicated servers can work out cheaper. It's worth modelling your expected usage.

Vendor lock-in

Serverless apps often rely on one provider's services. Infrastructure as code (for example Terraform) and keeping business logic separate from provider-specific code reduce the effort of moving later.

Debugging and monitoring

Distributed functions are harder to trace than one application. Good logging, tracing and alerting are essential from day one.

When serverless is a great fit

• APIs and backends with unpredictable or spiky traffic.
• Event-driven processing: uploads, webhooks, notifications.
• Scheduled jobs and automation.
• MVPs and startups that want to launch without managing infrastructure.

When to consider something else

• Constant, heavy workloads where reserved capacity is cheaper.
• Long-running processes, such as video encoding beyond function limits.
• Apps needing persistent connections, unless your platform supports them.
• Strict requirements to run anywhere without provider-specific services.

Serverless done well

A solid serverless project usually includes:

1. Infrastructure as code — reproducible environments with Terraform or similar.
2. CI/CD — automated testing and deployment (see our guide to CI/CD pipelines).
3. Observability — logs, metrics, tracing and alerts.
4. Cost monitoring — budgets and alerts so there are no surprises.

Thinking about going serverless?

We design and build serverless applications on AWS and Google Cloud, with infrastructure as code and cost optimisation built in. Learn more about our serverless architecture service or contact us to see if serverless is right for your project.