In this presentation, we will explore a practical use case of implementing effective infrastructure autoscaling using HPA, VPA, and Cluster Autoscaler. While working with standard VPA, we encountered several limitations, including a lack of flexibility in configuring calculation intervals and conflicts when running concurrently with HPA. Consequently, we decided to develop our own custom VPA controller. In our new solution, we: - Achieved stable coexistence of VPA and HPA on the same resources. - Implemented a filtering mechanism for transient CPU spikes during the pod startup phase. - Optimized the architecture by consolidating the functionality of three standard components into a single pod. - Leveraged the new In-Place Pod Resize capabilities introduced in Kubernetes 1.33. Key result: Optimized resource consumption and a 20–40% reduction in infrastructure costs.

In high-load systems, infrastructure costs often grow not because of the load itself, but because of inefficient architectural decisions: overprovisioning, excessive use of managed services, unnecessary data movement, incorrect SLA decisions, and the lack of a transparent cost model. At the conference, we will discuss how to approach cost optimization as a full-fledged architectural practice, rather than a one-time resource reduction exercise. We will also cover workload profile analysis, identifying real bottlenecks, building unit economics for infrastructure, traffic optimization, caching, CDN, observability, and controlled service degradation. Separately, we will look at the trade-off between performance, reliability, and cost: where maximum fault tolerance is truly required, where an eventually consistent approach is sufficient, and where managed solutions should be replaced with a simpler self-hosted architecture.

How can you thrive in an industry transformed by AI? This talk covers research and hands-on demos to help secure your career and leverage AI for productivity and growth.

Imagine one day your team inherits a system that was built over 4 years by six different teams — just to test hypotheses. No documentation. Just a massive monorepo and Jenkins for deployment. That’s exactly what we were handed, and that’s when we decided to take full inventory — starting with Architecture as Code. In this talk, I’ll share how we approached architectural documentation systematically: from building C4 diagrams to creating Service Documentation, ERDs, and Sequence Diagrams. You’ll learn how we practically restored our understanding of the system, brought architectural clarity, and which tools (PlantUML, Mermaid) and methods worked best. This talk isn't just about diagrams — it's about surviving chaos, synchronizing a team, and driving architectural evolution through transparency.

Do business approaches work in the defense sector? How can convenience and security be balanced? We’ll share our experience of building, developing, and implementing the DOT-Chain infrastructure from scratch.

Let's talk about our history. How we started the project with a small vector database of less than 2 million records. Later, we received a request for +100 million records, then another +100... And so gradually we reached almost 1 billion. Standard tools were quickly running out of steam - we were running into performance, index size, and very limited resources. After a long series of trials and errors, we built our own low-cost cluster, which today stably processes thousands of queries to more than 1B vectors.

Among the countless lists of AI tools for marketing, many companies stick to the most obvious choices. But what if there are lesser-known yet far more effective tools that can completely transform your marketing approach? In this talk, I’ll share the real AI solutions my team and I use across different areas of marketing. I’ll explain how these tools help us optimize costs, boost efficiency, and significantly cut our marketing budget—honestly and without the clickbait!)