Built around the GPUForge V1 inference story. Every tier routes through the same tenant-aware scheduler, prefix-cache layer, and DCGM-backed health gate — pick the depth of fleet and the compliance posture that fits your workload.
तुलना देखें →Play with the live V1 fleet before committing — no credit card, no contract, no sales call.
Standard V1 inference fleet on shared bare-metal tiers — for teams running live LLM serving.
Dedicated fleet on the Baremetal Service tier — for regulated, multi-region, or sovereign workloads.
At-a-glance skim of how Trial, Team, and Enterprise differ across the five dimensions the V1 inference story is built on.
| TrialSee the explanation | TeamSee the explanation | EnterpriseSee the explanation |
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The three tiers map to the V1 inference story: Trial opens the V1 story end-to-end through the /demo walk-through, Team picks up where the demo stops — standard fleet on shared bare-metal tiers with tenant-aware routing and prefix caching — and Enterprise layers in a dedicated hardware-quarantined fleet with SLURM+K8s multi-cluster orchestration and the compliance posture required for regulated or sovereign workloads.
The upgrade path matches the V1 inference story too: a Trial tenant explores the prefix-cache-aware routing and atomic allocation layer, a Team tenant goes live on the standard V1 inference fleet with per-job cost attribution, and an Enterprise tenant rolls the same V1 story onto a dedicated fleet with multi-cluster orchestration, an audit trail, and a dedicated platform engineer. Every tier runs the same scheduler, the same DCGM telemetry surface, and the same GPU-rate primitives — the tier you pick decides the depth of fleet, not the breadth of the inference story.
Annualized H100 vs H200 vs B200 TCO for a DIY GPU cluster — utilization, hidden costs, and when GPUForge wins on price.
Read the comparison → FAQ — ObjectionsThe five AI-cloud objections we hear on every call — isolation, lock-in, egress, support, compliance — answered in one page.
Read the FAQ →