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

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

Author: Demetrios February 19, 2026 Duration: 1:05:55

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.


// Related Links

Website: https://cast.ai/


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Hosted by Demetrios, MLOps.community is a space for honest, meandering talks about the real work of making artificial intelligence systems actually work. This isn't about hype or theoretical papers; it's about the messy, practical, and often surprising journey of taking models from a notebook into a live environment. You'll hear from engineers and practitioners who are in the trenches, discussing the tools, the frustrations, and the occasional breakthroughs that define the day-to-day. The conversations are deliberately relaxed, covering everything from traditional machine learning pipelines to the new world of large language models and even the intangible "vibes" of team culture and process. Each episode peels back a layer on what "production" really means, whether that involves deploying a predictive service, managing an agentic system, or maintaining reliability as everything scales. Tuning into this podcast feels like grabbing a coffee with colleagues who aren't afraid to dig into the technical nitty-gritty while keeping the tone conversational and accessible. It's for anyone who builds, manages, or is just curious about the operational backbone that allows AI to deliver value, offering a grounded perspective often missing from the broader conversation.
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