🐾Serverless cost pitfalls and how to avoid them🐾

🤓 Serverless services like AWS Lambda, DynamoDB, and SQS are often praised for being cost-effective — you pay only for what you use, and there’s no infrastructure to manage. But in practice, I’ve seen teams misuse these tools and end up surprised by unexpectedly high bills. Let’s walk through common mistakes I’ve seen on projects I worked with, and how to avoid them.

❌ Choosing Serverless when it doesn’t fit

It can be the case that you shouldn’t choose Serverless in the first place. Serverless can become expensive — especially under high-throughput workloads with frequent invocations. It’s ideal for bursty, spiky, or event-driven traffic — but for constant high-volume systems, it may not be the best fit.

Let’s imagine workload where Lambda function polls messages from SQS queue. It reads the message, do some simple transformations and writes data to a DynamoDB table.

Even modest SQS throughput becomes expensive at scale — and combined with Lambda and DynamoDB, the bill can reach thousands per month. That’s why you should be really careful while choosing serverless for high-throughput workloads.

✅ Tip: If you use ECS for such workloads, your cost may drop by 70–80%, depending on configuration.

❌ Over-provisioning

Over-provisioning happens when you assign more resources than needed, not because the system demands it — but because defaults weren’t evaluated. This can silently increase cost across your entire architecture.

Let's imagine you have 100 DynamoDB tables and prefer to use provisioned capacity. In practice each workload has approximately 1 read and 1 write per minute.

The same applies to other Serverless services as well, in majority of them you are responsible for resources configuration and therefore should be careful with over provisioning.

❌ Excessive logging

CloudWatch logging seems cheap until you scale. Logging at DEBUG level for every request, across dozens of services, can balloon your bill — and you may not even notice it.

Imagine a microservices application hosted as 25 ECS containers, each generating 2KB/sec, because you set logging on DEBUG level. Your services are working 24/7, so logs are generated all the time.

Monthly cost = 2KB/sec × 3600 sec × 720 hours × 25 tasks × $0.50 per GB = $64.80

Now scale that across environments and services — it adds up quickly.

✅ Tip: Use INFO or WARN in production, and set log retention policies to auto-delete old logs.

❌ Synchronous invocation

Serverless isn’t free when you're paying for idle time. A common mistake I see is using Lambda functions that wait synchronously for other operations to complete — sometimes for several seconds or even minutes. It waits for other Lambda function, or for some other service to complete.

Let’s imagine one Lambda function calls another and waits for it to complete. It takes 5 seconds to respond and there are 10’000 requests per hour.

Monthly cost = (10’000 req/hr × 5 sec × 512MB / 1024) × 720 hrs × $0.00001667 ≈ $307

✅ Tip: Use asynchronous patterns with SQS, SNS, or Step Functions to break dependencies and avoid paying for idle time.

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Thank you for reading, let’s chat 💬

💬 Would you be interested in a post about when to choose serverless?
💬 Do you use synchronous invocations in your workloads?
💬 Do you use provisioned capacity for DynamoDB tables?

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