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.
Use Nvidia, AMD or Tenstorrent based on your business needs with our smart Co-Pilot.
Deploy dedicated and Serverless endpoints on Nvidia, AMD, and Tenstorrent for Inference.
Focus on Pre-Training and Fine-tuning with Nvidia.
Leverage Fractional GPUs, Multi-tenancy and GPU Autoscaling to meet your performance and cost requirements.
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AI Workload Orchestration Platform
FlexAI Co-Pilot
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.
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.
Hit your latency SLOs by matching hardware class to workload profile (pretraining, finetuning, inference, RAG).
If a region is constrained, Co-Pilot moves the job to available capacity across providers—no code changes.
Keep control of models and data while running on the hardware and cloud you choose.
New: Inference Sizer gives deployment-ready GPU plans before you run.
One platform. Any cloud. Any hardware. Anywhere.
Get Started with $100 Credit
