GPU as a Service vs On-Prem GPUs: Cost Comparison Including Cloud Storage

GPU as a Service (GPUaaS) offers scalable, on-demand GPU access via the cloud, while on-premise GPUs require upfront hardware purchases and management. Cyfuture Cloud provides competitive GPUaaS with flexible pricing for AI/ML workloads, often proving more cost-effective than on-prem setups, especially when factoring in storage.

Key Insight: GPUaaS saves 50%+ over 3 years for variable workloads, with storage bundled seamlessly on Cyfuture Cloud.

Cost Breakdown: Compute

GPUaaS shifts costs from CapEx to OpEx, eliminating large initial outlays. For an 8x NVIDIA H100 setup over 36 months at 240 hours/month per GPU, Cyfuture Cloud's reserved rate of ~$4.50/hour totals ~$311,000. This covers hardware, power, cooling, and updates—no surprises.

On-prem demands $100K+ upfront per server, plus ongoing expenses. Total 3-year cost hits $595K–$695K, including spares and support. Low utilization (30–50% common in AI teams) inflates effective hourly rates to ~$7.80/GPU-hour.

Cyfuture's pricing avoids hyperscaler markups, offering H100/H200 access for Indian enterprises with clear, predictable billing.​

Storage Costs Integrated

Cloud storage is a hidden win for GPUaaS. Cyfuture Cloud bundles high-speed NVMe/object storage with no egress fees, keeping data near GPUs for low-latency AI training. Monthly costs scale with need—e.g., 10TB at ~$20/TB.​

On-prem storage adds CapEx: $25K–$50K for NVMe arrays, plus power/cooling (~$10K/year). Maintenance and scaling require IT overhead, often doubling effective costs. Cloud eliminates this, with Cyfuture providing seamless integration for ML datasets.

Over 3 years, storage pushes on-prem TCO 20–30% higher, while GPUaaS keeps it under 10% of total spend.​

Hidden Costs and Utilization

On-prem hides depreciation (3–5 years), staffing (~$50K/year IT), and power (₹25–30 lakhs/H100 unit in India). Downtime from failures adds 5–10% overhead.​

GPUaaS utilization flexibility shines: pay only for active hours. Cyfuture's models (hourly/subscription) suit bursty AI workloads, achieving 50%+ savings vs. on-prem's stranded assets.

For steady high-use (>75%), on-prem edges out—but few hit this without overprovisioning.​

Cyfuture Cloud Advantages

Cyfuture specializes in GPUaaS for India, with H100/MI300X access, 24/7 support, and no hidden fees. Enterprise security and expert ops reduce TCO further. Vs. hyperscalers, lower latency and costs make it ideal for AI/ML/HPC.

Performance matches on-prem for most workloads, with cloud scaling instant.​

Conclusion

For most users, GPUaaS via Cyfuture Cloud wins on cost—50%+ savings over 3 years, including storage—due to OpEx, flexibility, and no maintenance burden. Choose on-prem only for constant max utilization. Migrate to Cyfuture for AI efficiency in 2026.

Follow-Up Questions

1. What pricing models does Cyfuture Cloud offer for GPUaaS?
Hourly pay-as-you-go (~$5–$10/hr H100), reserved discounts ($4.50/hr), and subscriptions for predictable savings. No egress/storage surprises.

2. How does cloud storage impact total costs?
Bundled NVMe/object storage adds minimal OpEx (~$20/TB/mo on Cyfuture), vs. on-prem's $50K+ CapEx and ops. Ideal for large AI datasets.

3. When is on-prem cheaper?
Above 75% utilization over 3+ years, with in-house expertise. Rare for variable AI loads.​

4. Does Cyfuture support Indian enterprises specifically?
Yes—local data centers, INR billing, H100/H200 GPUs, and compliance for low-latency AI.​