Hyperscale clouds budgets, LLM APIs, and data pipelines: how to optimize real AI workloads without grinding your team to a halt.
About this session
Organizations are under pressure to cut cloud costs, but many approaches to “cloud savings” end up sacrificing performance, slowing teams down, or introducing fragility. This session shows how to do cost optimization the right way: with data, clear models, and architecture changes that are aligned with business goals.
Drawing on real customer architecture reviews and large-scale internal data pipelines, Mor will walk through:
How to build a savings model that ties cloud spend directly to business outcomes
Practical patterns for optimizing compute, data, and AI services without degrading performance
How open-source tools like Hyperscaler Radar can surface hidden cost drivers and guide decision-making
A concrete, evidence-based framework teams can use to cut spend while preserving (or improving) performance and reliability
Attendees will leave with actionable strategies and mental models they can immediately apply to their own cloud environments, whether they’re running traditional workloads or AI-infused systems.
Speaker
Key takeaways
- Key Takeaways How to design and use a savings model that connects cloud spend to real business value A set of concrete optimization patterns for compute, data, and AI services How to use open-source tooling and data pipelines to make cost decisions evidence-based A repeatable framework for reducing cloud spend without sacrificing performance, reliability, or team velocity