Case Study: Primer AI achieves 60–70% AWS cost savings and flexible Kubernetes deployments with Spot

Primer AI - Customer Case Study

Primer AI builds NLP/NLU/NLG engines (Primer NLX) that run on customers’ cloud and on‑prem infrastructure. They faced the challenge of deploying and maintaining consistent, scalable AI pipelines across heterogeneous environments while keeping engineering overhead and compute costs low, so they turned to Spot and its Spotinst service to help manage infrastructure complexity.

By running Primer’s Kubernetes clusters through Spot’s Spotinst connector, Primer AI gained pod-driven autoscaling, automated Spot instance management, and a dashboard for instance and pod allocation. Spot enabled 60–70% savings on AWS spend, eliminated extra engineering effort for instance orchestration, improved capacity decisions, and let Primer AI focus engineering resources on model development while offering customers cost‑effective, scalable compute.

Primer AI

William Jimenez

Manager, Solution Architecture