Run On Any Compute with FlexAI Co-Pilot

Use the best compute for your needs with our Workload Co-Pilot. Leverage Nvidia, enable Fractional GPUs, Autoscale, or deploy on AMD and Tenstorrent. Customers have the option to deploy Dedicated and Serverless endpoints on Nvidia, AMD, and Tenstorrent for Inference and focus on Training and Fine-tuning with Nvidia.

Use Nvidia, AMD or Tenstorrent based on business needs.
Leverage Fractional GPUs and GPU Autoscaling to meet your performance and cost requirements.

AI Workload Orchestration Platform

Smart Sizing

Smart sizer provides recommendations with performance and cost characteristics in view
Side by side comparison of the GPUs for the selected models and performance requirements
Auto-select GPUs or allow the users the option to select the GPUs of their choice

AI Compute Infrastructure

(BYO Cloud, on-prem, hybrid, FlexAI Cloud)

Premium NVIDIA GPU’s

(H100, H200, B200)

Standard NVIDIA GPU’s

(A100, L40, L4)

Other GPU’s, Accelerators

(AMD, Tenstorrent, BYO)

GPU access, pricing, and availability shift by region and provider, which pushes teams to overprovision “just in case” and leave expensive capacity idle.

Choosing between H100s, L40s, or alternatives like AMD and Tenstorrent becomes a spreadsheet exercise that steals time from engineering, while fragmented cloud tooling makes it hard to place each workload on the right hardware without lock-in or guesswork.

The solution: FlexAI Co-Pilot

Smart sizing, everywhere: Point to your model and targets (latency, RPS, budget). Co-Pilot allocates the best fit across premium NVIDIA, standard NVIDIA, AMD offerings and other accelerators (AMD, Tenstorrent, BYO).
Cross-cloud control: Run in your cloud, on-prem, hybrid, or FlexAI Cloud. Bring your own credits. Shift instantly for cost, capacity, or region.
Zero DevOps setup: Launch jobs in under 60 seconds. No YAML. No cluster tuning.
Right-size automatically: Enable fractional GPUs, auto-scale, and fallback pools to keep utilization high without manual work.

What you get

Lower cost, longer runway:

Consistently >90% GPU utilization and up to 50% lower compute costs by placing finetuning on NV where it shines and routing inference to AMD/TT when it’s more cost-effective.