VoltageGPU vs RunPod

RunPod runs two product lines side by side. Community Cloud is a peer-supplied marketplace where independent operators rent out their own GPUs; pricing is cheapest, isolation is whatever the host configured, and the workload runs on hardware the buyer does not control or audit. Secure Cloud is RunPod-operated tier-3 datacenter capacity with standard hypervisor isolation, RunPod-managed networking, and per-region availability. Both are excellent for what they are — raw GPU rental on a US-anchored marketplace with very competitive pricing — and neither is positioned as a confidential-computing platform.

VoltageGPU is the opposite shape. The operator is VOLTAGE EI in Solaize, France, the hardware is dedicated NVIDIA H100/H200/B200 running inside Intel TDX guest VMs with AES-256 memory encryption, the PCIe bus between CPU and GPU is encrypted by NVIDIA Protected PCIe, and every confidential session ships with an Intel DCAP attestation quote that can be re-verified offline against the Intel root certificate. There is no peer marketplace layer; there is no variable-isolation host; the operator is mathematically constrained from reading workload memory. That is what "Confidential AI Infrastructure" means in our positioning — a property the silicon enforces rather than a property the operator promises.

On the pricing table this difference shows up cleanly. NVIDIA H100 80GB is $2.77/hr on VoltageGPU (TDX-sealed) versus $2.79/hr on RunPod Secure Cloud (no TDX). NVIDIA H200 141GB is $3.60/hr versus $3.49/hr. The RunPod side is $0.11/hr cheaper on H200; the VoltageGPU side ships the GPU inside an Intel TDX guest with attested hardware encryption end-to-end. The eleven-cent delta is the entire confidential-compute stack, and for a buyer whose workload is not GDPR-sensitive there is no reason to pay it. For everything else, that is the price the regulatory framework actually requires.

RunPod offers a Data Processing Agreement and supports EU-region deployment of Secure Cloud capacity. That is the same regulatory posture every US GPU marketplace exposes — a contract that names the legal protections, plus an option to pin workloads to European data centers. It is sufficient for the majority of AI workloads, including most fine-tuning, evaluation, internal research, and any pipeline where the data was already public or already pseudonymized before it touched GPU memory. For those workloads RunPod is a perfectly correct choice, and the price advantage on H100/H200/A100 is real.

It is not sufficient where the technical measures clause of an Article 28 DPA needs to be backed by hardware evidence that the operator cannot read workload memory. A contractual DPA does not constrain the host operator at the silicon layer; if a US administrator with legitimate console access wants to introspect a running VM, the standard hypervisor permits it. For workloads under bar-association secrecy (RIN art. 2.2 for French avocats), under HDS-certified health-data processing, under PCI DSS for cardholder data, or under the new EU AI Act provisions on high-risk processing of sensitive categories, CNIL and equivalent authorities have begun to require the technical measure be enforced by hardware. That is the requirement Intel TDX + NVIDIA Protected PCIe were built to satisfy.

VoltageGPU's answer is therefore not "RunPod's DPA is bad" — it is "the DPA route runs out of room exactly where the silicon route begins". The data physically does not leave French infrastructure, the operator entity is a French sole proprietorship registered under SIREN 943 808 824, the encrypted memory key is ephemeral and per-VM, and the attestation quote is cryptographic evidence delivered fresh for every confidential session. Article 28 is enforced at the silicon layer, not at the contract layer. Reasonable buyers can pick either side of that line; the page exists so the line is visible before the purchase.

It is not a marketing exercise to admit a competitor is the right tool for many jobs. RunPod is the right tool for many jobs. The GPU catalogue is larger by an order of magnitude — 32+ active SKUs versus our 8 confidential SKUs — covering RTX 4090, RTX A6000, L40S, A40, A100 40GB, A100 80GB, and several Ada-generation cards we do not list. Community Cloud spot pricing is the cheapest entry point on the market for non-sensitive workloads. Serverless inference endpoints with auto-scaling on cold-start optimized base images are a category VoltageGPU does not compete in today. Multi-region availability across US, Canada, EU, and Asia regions exceeds our footprint.

On raw price for non-confidential workloads RunPod wins outright on several SKUs. NVIDIA A100 80GB on RunPod is $1.19/hr versus VoltageGPU at $2.02/hr — for academic research, public-model fine-tuning, or any workload where the input data is not under a confidentiality obligation, that 83-cent-per-hour gap is the entire decision and the buyer should choose RunPod. NVIDIA H200 is also $0.11/hr cheaper on the RunPod side. Where the comparison flips: NVIDIA RTX 4090 is $0.68/hr TDX-sealed on VoltageGPU versus $0.74/hr on RunPod Secure Cloud, NVIDIA B200 192GB is in stock on VoltageGPU at $7.50/hr per-second-billed and not yet generally available on RunPod, and the OpenAI-compatible confidential inference API plus the 8 Confidential AI Agents are categories RunPod does not offer at all.

The honest summary is product-shape, not provider-quality: RunPod is a great US GPU marketplace and remains the right answer for unconstrained workloads on the lowest-cost path; VoltageGPU is European confidential infrastructure and is the right answer when the technical measure clause needs to be enforced by hardware rather than by contract. Buyers who try to use either as the wrong-shape tool will be unhappy with both.