AI Infrastructure

GPU orchestration maximizes utilization across training and inference workloads. Model serving platforms optimize inference latency and cost. Vector databases enable semantic search and RAG. Evaluation frameworks measure AI model quality and safety. Fine-tuning platforms adapt foundation models to specific domains. AI observability tools monitor model performance, drift, and reliability in production.

GPU orchestration platforms reduce inference costs by 50-70% through batch processing and hardware optimization. Model evaluation frameworks test AI systems for accuracy, bias, and safety before production deployment. Vector databases power the RAG architecture that grounds LLM responses in external knowledge. AI observability platforms detect model drift and performance degradation in production.

AI infrastructure is one of the most intensely funded venture categories, as every dollar spent on AI applications requires multiple dollars of infrastructure investment. With 23 funders specifically tagged, the sector spans GPU orchestration, model serving, training pipelines, MLOps, and specialized AI hardware. The hyperscaler AI infrastructure buildout alone represents hundreds of billions in investment.