Cloud GPU Pricing for AI Workloads with Updated Provider Rates

Artificial Intelligence is rapidly transforming industries—by 2025, global AI adoption is expected to surpass $200 billion. Underpinning this surge is a quiet workhorse: the GPU. Whether you’re training large language models, running real-time image recognition, or deploying autonomous systems, GPUs are essential. But owning this hardware is expensive—and inflexible.

That’s where cloud GPU pricing comes in. Pay-as-you-go access to high-performance GPUs eliminates heavy capital expenditure while offering scalable compute. However, rates vary dramatically across providers—and choosing wisely can cut costs by 70% or more.

This knowledge-based guide dives into cloud GPU pricing for AI workloads, offering a detailed breakdown of on-demand and spot rates, provider comparisons, and expert budgeting tips. Let’s start by seeing what the pricing landscape looks like in mid-2025.

Current Cloud GPU Prices: On-Demand and Spot

Here’s a snapshot of current rates (USD/hour) as of mid-2025:

Hyperscalers (AWS, Azure, GCP)

A 2025 comparison shows:

NVIDIA V100 (32 GB)

AWS/Azure: ~$3.06/hour

Google Cloud: $2.48/hour on-demand; $1.116/hour with committed use

NVIDIA A100 (40–80 GB)

Azure: $3.67–$14.69/hour, depending on GPU count

NVIDIA H100 (80 GB)

Azure (NC40ads): $6.98/hour

H200 series (successor to H100): ranges from $3.72 to $10.60/hour

Non-Hyperscaler Providers

For deep-learning workloads:

DataCrunch: V100 at $0.39/hr; H100 at $3.35/hr

OVHcloud: V100 around $2.19/hr, A100 and H100 near $3.35–$3.39/hr

Thunder Compute: A100 at $0.66/hr; T4 at $0.29/hr

Spot/Marketplace offers: GT 730 at $0.04/hr, A5000 at $0.16–$0.29/hr

Specialized Indian Provider: Cyfuture Cloud

Cyfuture Cloud offers GPU-accelerated infrastructure in India. Their NVIDIA GPU Cloud includes management and hosting features, priced as low as $8/month in some configurations—suggesting hourly rates comparable or better than hyperscaler spot pricing 

Breaking Down the Cost Variables

To forecast your monthly GPU cost accurately, here are the main pricing drivers:

1. GPU Model and VRAM

Entry-level: NVIDIA T4 (~$0.35/hr on GCP)

Mid-range: V100 or A100, around $2.19–$3.67/hr

High-end: H100/H200, $3.72–$10.60/hr

2. Usage Model

On-demand: High flexibility, full price

Spot/preemptible: Discounts of 60–90%, but may be interrupted

3. Instance Scale

Most providers bundle GPUs into instances of 1, 4, or more GPUs—divided per GPU cost may differ.

4. Commitment Tier

Hyperscalers offer cheaper hourly rates with 1- to 3-year commitments or sustained use discounts 

5. Add-ons

Beyond GPUs, remember to budget for:

CPU/RAM attached to the GPU

Storage (SSD/NVMe volumes)

Networking and data transfer

Managed platform or support add-ons

Estimating Monthly GPU Costs: Sample Scenarios

Let’s estimate costs for common AI usage patterns:

A. On-Demand Training with A100 (1 GPU)

GCP: $2.48/hr × 100 hrs = ~$248

OVH: $3.35/hr × 100 hrs = ~$335

AWS/Azure: ~$3.67–$14.69/hr depending on config = $367–1470/hr ×100 = $367–1470

B. Spot Training with A100

Thunder Compute: $0.66/hr × 200 hrs = $132

Google Spot: near on-demand at $0.60/hr × 200 hrs = $120

C. High-performance Inference at Scale (H200)

Jarvislabs: $3.80/hr × 200 hrs = $760

GCP spot: $3.72/hr × 200 hrs = $744

AWS p5: $10.60/hr × 200 hrs = $2120

D. Continuous GPU Access (Managed Instance)

Cyfuture Cloud at $8/month for basic GPU packages—likely for low-usage or idle inference setups

Choosing the Right GPU for Your AI Workload

Here’s a simple guide:

Pro Tips:

For flexible workloads, spot instances provide 60–90% savings .

Committed use discounts (1–3 years) reduce cost by 40–60% .

On-prem costs (buying H200): ~$30–40k each ; comparative to 1-year rentals at $3.80/hr.

Why Cyfuture Cloud is a Strong Contender for Indian AI Workloads

Cyfuture differentiates itself with:

Local Pricing & Billing: INR-based, transparent monthly fees

Low Hourly Rate: GPU hosting from ~$8/month with dedicated support

Simpler Infrastructure: No GPU clustering or complex scale-up; best for continuous inference and lightweight training

Full-stack Hosting: Combines GPU, network, storage, and backup under a single provider

Hybrid users—running heavy training globally and inference locally—can benefit most from such architecture.

Conclusion: Plan Smart, Train Smarter

Navigating cloud GPU pricing in 2025 doesn't need to cost a fortune—or time in guesswork. Here's what to remember:

Match GPU model to workload: Use T4/A5000 for tests, A100 for training, H200 for large models.

Utilize spot and reservation discounts: Savings of 60–90% are realistic

Account for full stack cost: CPU, RAM, storage, network, support matter too.

Leverage local providers: Cyfuture Cloud offers transparent, budget-friendly GPU hosting with INR billing—a compelling option for inference and steady workloads.

By combining smart cost planning with the right provider strategy, you can power your AI projects effectively—without breaking the bank.