Open by Design: Cost-Efficient Private AI Platforms

Private AI platforms are gaining momentum as organizations seek greater control over costs, data, and technological dependencies. Instead of relying exclusively on managed AI services, many teams are exploring open, self-controlled platforms tailored to real-world requirements.

This talk draws on selected project insights and architectural experience to discuss how Private AI platforms can be designed with a strong focus on cost efficiency and concrete use cases. We examine how to assess which AI workloads actually require specialized hardware and when CPU-based environments may be sufficient, as well as pragmatic approaches to capacity planning and cost optimization. Typical trade-offs and challenges encountered when moving toward production are highlighted.

The session also emphasizes the importance of openness, leveraging open interfaces, formats, and frameworks to avoid vendor lock-in and preserve long-term flexibility. It illustrates how accessible Private AI has become, from local development setups to production-ready platforms in on-premises or managed environments.