Roundtable CAST AI episode: Serving LLMs in Production: Performance, Cost & Scale.

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// Abstract

Experimenting with LLMs is easy. Running them reliably and cost-effectively in production is where things break.

Most AI teams never make it past demos and proofs of concept. A smaller group is pushing real workloads to production—and running into very real challenges around infrastructure efficiency, runaway cloud costs, and reliability at scale.

This session is for engineers and platform teams moving beyond experimentation and building AI systems that actually hold up in production.

// Bio

Ioana Apetrei

Ioana is a Senior Product Manager at CAST AI, leading the AI Enabler product, an AI Gateway platform for cost-effective LLM infrastructure deployment. She brings 12 years of experience building B2C and B2B products reaching over 10 million users. Outside of work, she enjoys assembling puzzles and LEGOs and watching motorsports.

Igor Šušić

Igor is a founding Machine Learning Engineer at CAST AI’s AI Enabler, where he focuses on optimizing inference and training at scale. With a strong background in Natural Language Processing (NLP) and Recommender Systems, Igor has been tackling the challenges of large-scale model optimization long before transformers became mainstream. Prior to CAST AI, he worked at industry leaders like Bloomreach and Infobip, where he contributed to the development and deployment of large-scale AI and personalization systems from the early days of the field.

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Website: https://cast.ai/

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Serving LLMs in Production: Performance, Cost & Scale // CAST AI Roundtable