Compare H100, A100, L40S, and consumer GPU pricing across 8+ providers — benchmarked against your actual workload, not affiliate payouts.
Talk to a GPU architectTraining and serving modern AI workloads demands specialized GPU infrastructure, and the right cloud depends entirely on your model size, batch shape, latency targets, and budget. Most teams pick a provider based on a marketing page or a sales call — then discover too late that spot availability is poor, egress costs are brutal, or the interconnect topology doesn't support distributed training.
CB4UHost's GPU Cloud Brokerage benchmarks 8+ GPU cloud providers against your actual workload. We run your model (or a representative proxy) on Lambda, CoreWeave, RunPod, Vast.ai, AWS, GCP, Azure, and others — then report real price-per-token, throughput, queue depth, and reliability. You see the comparison data before you commit to a contract.
We don't resell GPU capacity and we don't take affiliate commissions. Our only incentive is to find you the right GPU cloud at the right price — and to show you the underlying data so you can defend the decision to your team, your finance department, and your board.
Whether you're pretraining a 7B model, fine-tuning Llama on your domain data, or serving a 70B model in production, we benchmark the providers that actually fit your workload and report the trade-offs in plain English.
3-5 GPU clouds matched to your model size, region, and budget — with current spot and on-demand pricing.
We run your model (or a proxy) on each shortlisted cloud and report price-per-token, throughput, cold-start, and queue depth.
12-month cost model including compute, storage, egress, networking, and support — across all shortlisted providers.
NVLink availability, InfiniBand support, cross-node bandwidth — critical for distributed training.
Written recommendation with the underlying data, plus a phased adoption plan if you're migrating from an existing provider.
We profile your model size, batch requirements, sequence length, latency targets, and data volume to size the right GPU and storage footprint.
Based on your workload and region, we shortlist 3-5 GPU clouds and gather current pricing, SLAs, and interconnect capabilities.
We run your workload on each shortlisted cloud and capture real metrics — not spec-sheet numbers.
We model 12-month cost including hidden fees (egress, snapshot, support tier) and report trade-offs in plain English.
You get a written recommendation, the raw benchmark data, and a 60-minute walkthrough so your team can execute.
Interactive tools related to this service.
Lambda Labs, CoreWeave, RunPod, Vast.ai, AWS (p4d/p5), GCP (A2/A3), Azure (ND-series), and others depending on your region and workload. We add new providers as they launch.
We run your model (or a representative proxy) on each shortlisted cloud and capture real price-per-token, throughput, and reliability. Pricing alone is misleading — a cheap cloud with poor spot availability can cost more in aggregate.
Typically 2-3 weeks: 3-5 days for workload profiling, 1 week for benchmark runs, and 3-5 days for the report and walkthrough. Rush engagements are available.
No. We do not resell GPU capacity and do not take affiliate commissions that bias recommendations. Our revenue comes from the consulting engagement itself.
Adapt open-source LLMs (Llama, Mistral, Qwen, DeepSeek) to your domain — with LoRA, QLoRA, instruction tuning, and safe deployment guardrails.
Production retrieval-augmented generation with vector stores, hybrid search, reranking, citation, and observability — so you can trust the answers.
End-to-end ML pipelines — experiment tracking, model registry, feature stores, CI/CD for models, drift detection, and rollback. Built on Kubernetes.
Tell us about your project. We'll come back with a scoped proposal and a fixed-fee quote.
Talk to a GPU architect