Elliot Foreman and Andrew DeLave from ProsperOps joined Yuliia and Dumky to discuss automated cloud cost optimization through commitment management. As Google go-to-market director and senior FinOps specialist, they explain how their platform manages over $4 billion in cloud spend by automating reserved instances, committed use discounts, and savings plans across AWS, Azure, and Google Cloud. The conversation covers the psychology behind commitment hesitation, break-even point mathematics for cloud discounts, workload volatility optimization, and why they avoid AI in favor of deterministic algorithms for financial decisions. They share insights on managing complex multi-cloud environments, the human vs automation debate in FinOps, and practical strategies for reducing cloud costs while mitigating commitment risks.

Kir Titievsky, Product Manager at Google Cloud with extensive experience in streaming and storage infrastructure, joined Yuliia and Dumky to talk about streaming. Drawing from his work with Apache Kafka, Cloud PubSub, Dataflow and Cloud Storage since 2015, Kir explains the fundamental differences between streaming and micro-batch processing. He challenges common misconceptions about streaming costs, explaining how streaming can be significantly less expensive than batch processing for many use cases. Kir shares insights on the "service bus architecture" revival, discussing how modern distributed messaging systems have solved historic bottlenecks while creating new opportunities for business and performance needs.Kir's medium - https://medium.com/@kir-gcpKir's Linkedin page - https://www.linkedin.com/in/kir-titievsky-%F0%9F%87%BA%F0%9F%87%A6-7775052/

Serhii Sokolenko, founder at Tower Dev and former product manager at tech giants like Google Cloud, Snowflake, and Databricks, joined Yuliia to discuss his journey building a next-generation compute platform. Tower Dev aims to simplify data processing for data engineers who work with Python. Serhii explains how Tower addresses three key market trends: the integration of data engineering with AI through Python, the movement away from complex distributed processing frameworks, and users' desire for flexibility across different data platforms. He explains how Tower makes Python data applications more accessible by eliminating the need to learn complex frameworks while automatically scaling infrastructure. Sergei also shares his perspective on the future of data engineering, noting in which ways AI will transform the profession.Tower Dev - https://tower.dev/Serhii's Linkedin - https://www.linkedin.com/in/ssokolenko/

Deepti Srivastava, Founder of Snow Leopard AI and former Spanner Product Lead at Google Cloud, joined Yuliia to chat what's wrong with current approaches to AI integration. Deepti introduces a paradigm shift away from ETL pipelines towards federated, real-time data access for AI applications. She explains how Snow Leopard's intelligent data retrieval platform enables enterprises to connect AI systems directly to operational data sources without compromising security or freshness. Through practical examples Deepti explains why conventional RAG approaches with vector stores are not good enough for business-critical AI applications, and how a systems thinking approach to AI infrastructure can unlock greater value while reducing unnecessary data movement.Deepti's linkedin - https://www.linkedin.com/in/thedeepti/Snowleopard.ai - http://snowleopard.ai/

John Grubb is the Sr. Director of FinOps and Cost Modeling at Platform.sh. With experience as a former Data Platform Director, Director of BI & Analytics, and Director of Customer Care, John brings a sharp perspective on why cloud costs matter. He knows how to align financial and engineering teams and believes that FinOps is about maximizing the value of every cloud dollar rather than just cutting costs.Follow John on Linkedin- https://www.linkedin.com/in/johnnygrubb/John's blog - https://www.thefinoperator.com/