Every new cloud provider a company adopts usually means another set of Terraform configurations, another deployment guide, and yet another way of doing things. Costs start growing faster than the number of providers. Platform engineering helps break this dependency: the golden path becomes the interface, while the specific cloud becomes just an implementation detail. A developer describes what they need — a service and a production database — and the platform decides how that should be provisioned in a hyperscaler versus a local cloud provider. In the live demo, a developer creates a new service through a self-service portal. One template, one Kubernetes manifest — and the application is deployed to two clouds at the same time. The same PostgreSQL request becomes a managed database service in one cloud and an operator-managed database running in Kubernetes in another. We’ll also look at the limits of this approach. IAM, storage, and backups are not completely hidden by the abstraction layer — and I’ll explain why that is actually a good thing. After the talk, you’ll understand how the “request → composition” pattern works for databases across different clouds, how to start building your first golden path without creating your own PaaS, and which differences between cloud providers should remain visible to developers.

AI has already become part of the modern engineering landscape, but things are much more complicated in high-load production systems. GenAI works well in demos and copilots, but is it ready for real-time processing, heavy workloads, and critical production scenarios? In this panel discussion, we’ll talk about why AI has yet to become the standard for high-load architectures, where the line between ML and GenAI lies, why inference is expensive, and why FinOps is becoming a new headache for engineering teams. We’ll discuss on-prem vs. cloud for AI workloads and real-world production constraints.

Have you ever wondered what’s really going on under the hood of distributed systems? Not those “sort of a cluster” setups with 3 nodes, I mean the real deal. The exabyte-scale beasts. In this talk, we’ll peek behind the curtains of modern infrastructure. How do systems that crunch mountains of data actually work? What patterns, principles, and engineering decisions are hiding behind truly scalable architectures? Here’s what we’ll dive into: - What the inner life of a distributed system really looks like - How a distributed app is different from a distributed system - How data storage patterns evolve into modern DBs, queues, and logs - Why “PostgreSQL in the cloud” isn’t really PostgreSQL anymore - Why Northguard might just outshine Kafka - And how new players like NewSQL are changing the game If you’re an architect, tech lead, developer or just curious about why infrastructure scales the way it does - come join! I’ll share insights you might use in your own projects (or at least see them from a new angle). P.S. Yep, there’ll be a bit of magic ✨ and a whole lot of hard truths about the distributed systems powering our world. ?

We will explore how e-commerce projects prepare for the busiest time of the year, which key aspects to focus on, and what to expect. We’ll share our experience in setting up auto-scaling, load balancing, and discuss the loads that Silpo handles, as well as the solutions that help us navigate this season without failures.

At the conference, I will present an approach to extending the capabilities of Kubernetes, transforming it into a fully-fledged private Cloud with support for virtualization, isolated networks, and multi-organization authentication.