Organizations building advanced AI applications are facing growing demands around performance, scalability, and cost control. Clarifai, a leader in computer vision and multimodal reasoning, recently shared its approach to these challenges in a detailed case study highlighting its work with Vultr.

The case study outlines how Clarifai deployed its reasoning engine on Vultr’s GPU-accelerated infrastructure to support real-time, multi-region workloads. By integrating with Vultr’s managed Kubernetes control plane and cluster autoscaler, Clarifai was able to orchestrate distributed inference more efficiently while reducing operational overhead.

Key insights include their use of NVIDIA GH200 Grace Hopper and HGX B200 systems, along with AMD GPUs, to achieve consistent performance across global environments. The case study also examines Clarifai’s focus on tail-latency tracking, batching, compression, and pipeline parallelism, offering practical visibility into how they optimize high-volume AI workloads.

One of the most compelling outcomes is the measurable improvement in cost and speed. Clarifai reports delivering inference at twice the performance and half the cost of traditional hyperscalers, with independent validation from Artificial Analysis. Predictable pricing, transparent billing, and responsive support also played a crucial role as Clarifai expanded into new regions and scaled customer workloads.

For teams evaluating multi-cloud strategies, GPU capacity planning, or AI workload orchestration, this case study provides a clear and informative look at how one of the industry’s leading AI platforms approaches modern infrastructure design.

Read the complete case study to explore the results and technical considerations behind Clarifai’s deployment.