ODSC AI 2026: Optimizing Cloud Costs With Simulation and Heuristic Search

Greg recaps his experience at the ODSC 2026 where he demonstrated agentic coding by building a cloud cost optimization simulator live, using a single prompt without any pre-written code. He describes the complexities of managing cloud infrastructure costs and how his simulator effectively balanced the use of reserved, on-demand, and spot instances to optimize costs while maintaining reliability. Greg highlights the advantages of the Zerve agent, which operates within the development environment, allowing it to adapt dynamically to data changes and code errors, thereby enhancing the efficiency of data science workflows. The simulator's adaptability was tested by increasing workload volatility, revealing that the optimal resource allocation strategy remained largely unchanged. He also discusses the architecture of Zerve, which separates code execution into individual kernels to prevent state sharing issues common in Jupyter notebooks. Greg emphasizes the effectiveness of short iterative prompts over lengthy ones and mentions the potential for Zerve to be used beyond prototyping, with capabilities for deploying models as APIs and building applications. He invites users to explore Zerve with a free tier offering and encourages feedback from the community.

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