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.

No product team can keep up with every need and use case of the Defense Forces. At the same time, military units are already adapting existing digital products on their own — building companion apps and custom tools to address specific needs across their end-to-end combat workflows and accelerate enemy destruction. How can we turn this demand into a full-fledged platform capability? How can we give users a safe, standardized way to adapt digital products to their own battlefield needs? In this talk, we’ll share our approach to this challenge, walk through real-world cases, and demonstrate the core capabilities that are already in use today. We’ll also explore where this approach can go next: from adapting user interfaces to working with backend logic, data, and AI/agent capabilities — ultimately enabling users to build their own end-to-end workflows on top of a shared platform.