As data teams grow and serve multiple teams, clients, or business units from a shared platform, maintaining isolation and velocity without sacrificing either becomes a defining architectural challenge. Add AI and LLM workloads to the mix, where different tenants may need different models (open-source, fine-tuned, or proprietary) swapped in and out as dependencies, and the problem gets even more interesting.

In this Deep Dive, Dagster Labs and Brooklyn Data Co. will cover the patterns, trade-offs, and real-world implementations behind multi-tenant data platforms built on Dagster. We will explore code location isolation, multi-tenant project structures, AI model dependency management, and operational strategies that let platforms scale with the organizations they serve.
The session draws on production case studies and consulting experience across diverse industries, giving attendees practical guidance they can take back to their own teams.

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