Imagine setting up a test project with a billing budget of $7, a free database plan on the Google Cloud Platform and spending $72,000 overnight. A startup founder received a massive bill due to a mistake in configuring the Firestore NoSQL database deployment: The database made 116 billion reads and 33 million writes to Firestore. The project team was on the edge of bankruptcy without even going live with their MVP.

Luckily, after reviewing the incident, Google didn’t charge the startup for the services. But happy endings such as this are exceptions. Wrong tech stack or architecture choices lead to unbearably high costs of technology that can even burn the business to the ground.

In this article, we’ll talk about:

• Common mistakes teams make when selecting tech stack and an architectural approach for data-intensive AdTech applications

• How to avoid them and design a scalable and cost-effective system

To better understand how a suboptimal tech stack and a system architecture lead to scalability limitations and a high infrastructure bill, we’ll explore how Xenoss redeveloped a client’s product to lower operational costs.

Short videos from the joint webinar with Xenoss CTO Vova Kyrychenko and Aerospike’s Global Director of AdTech and Gaming Daniel Landsman will give more context to our discussion.

Vladyslav Kushka, Delivery Manager at Xenoss, also joined the conversation. Relying on his two-decade experience in software engineering and technical management, Vlad shared tips on selecting an appropriate tech stack for data-intensive AdTech solutions, particularly a codebase and a database. So let’s begin.

Read the full version here.