GPU Cloud Pricing: Dedicated NVIDIA T4, Flat Monthly

GPU cloud computing is expensive — and unpredictable. Most cloud GPU providers charge by the second with no spending cap, so a forgotten instance can burn through hundreds of dollars overnight. AWS's g4dn.xlarge (NVIDIA T4) costs $0.526/hr on-demand — that's $12.62/day if you forget to stop it. Google Cloud charges $0.35/hr for the GPU alone, plus the VM cost on top. Even budget providers like Vast.ai and Lambda Cloud bill per-second with no built-in idle protection.

SnapDeploy takes a different approach: one flat monthly price for a GPU that is exclusively yours. The Dedicated GPU subscription — $499/month — gives you a physical NVIDIA T4 on a dedicated on-demand instance, running 24/7. Not shared, not spot, never interrupted, never metered. This guide breaks down exactly how the pricing works and how it compares to hourly cloud GPU alternatives.

How Dedicated GPU Pricing Works

There is exactly one number to know: $499/month (₹48,403 in India, no GST). That buys:

• NVIDIA T4 (16 GB VRAM) — one physical GPU, exclusively yours, on a dedicated on-demand g4dn.xlarge instance
• 24/7 runtime — no auto-sleep, no cold starts, model weights stay warm in VRAM
• No metering — no per-hour rates, no balance to watch, no usage anxiety
• Cancel anytime — the container stops at the end of the paid period

One subscription backs one GPU container. Want a second GPU container? Add a second subscription — each gets its own physical GPU.

Dedicated vs. Hourly: The Math

Spread over a month of continuous 24/7 runtime (~730 hours), the Dedicated GPU subscription works out to a per-hour equivalent of roughly $499 / 730h ≈ $0.68/hr — with zero DevOps. Compare that with running the same T4 yourself:

The ~$0.15/hr spread is what the fully managed platform costs: SnapDeploy provisions, patches, routes, monitors, and redeploys the GPU for you. If your time is worth anything, the DIY rate is not actually cheaper.

What About the A10G?

The NVIDIA A10G (24 GB VRAM, higher memory bandwidth — better for 7B+ parameter LLMs, Stable Diffusion XL at full resolution, and multi-model pipelines) is coming soon as its own dedicated tier with the same model: one flat monthly price, one physical GPU that is exclusively yours, running 24/7.

Why Dedicated Beats Metered for Production AI

Per-second billing sounds efficient until you run a real service on it. With the Dedicated GPU subscription:

• No cold starts — your model stays loaded in VRAM 24/7; the first request of the day is as fast as the thousandth
• No spot evictions — dedicated on-demand capacity is never reclaimed mid-inference
• No bill anxiety — a traffic spike, a runaway retry loop, or a forgotten load test cannot change what you pay
• Nothing to remember to stop — on AWS or Lambda Cloud, a forgotten always-on T4 costs $360-$380/month plus your ops time; here 24/7 is the product, at a flat $499

GPU Cloud Pricing Comparison (T4)

How SnapDeploy's GPU cloud pricing compares to other cloud GPU providers for an NVIDIA T4 (the most common GPU for inference):

Key differences:

• Billing model: SnapDeploy is the only platform listed with a flat monthly price — your bill is capped by design. On AWS or GCP, a forgotten GPU instance costs $12-$13 per day with no automatic protection.
• Dedicated hardware: the T4 is exclusively yours — one physical GPU per subscription, never shared, never spot, never interrupted.
• Setup complexity: AWS requires configuring IAM, VPCs, security groups, ECS task definitions, and GPU AMIs. SnapDeploy deploys from GitHub in one click.
• Google Cloud T4 pricing: GCP charges $0.35/hr for the T4 GPU accelerator, but you also pay for the VM instance it's attached to (~$0.10-$0.20/hr), making the total $0.45-$0.55/hr.

GPU Cloud Pricing Comparison (A10G)

Who Is This Pricing Best For?

SnapDeploy's flat-rate Dedicated GPU subscription is ideal for:

• Production AI APIs — always-warm inference with no cold starts and no eviction risk, at a price that never moves.
• Startups shipping an AI product — one predictable line item instead of a metered bill that grows with success.
• Teams without a DevOps function — deploy from GitHub in one click; no IAM, CUDA AMIs, or instance babysitting.
• Anyone burned by hourly GPU billing — no forgotten instances, no runaway meters, no surprise invoices.

If your workload is a one-off experiment measured in single hours, a per-second provider may be cheaper for that burst. SnapDeploy's value is your own GPU, fully managed, running 24/7, at a flat price.

Frequently Asked Questions

How much does GPU cloud cost on SnapDeploy?

NVIDIA T4 (16 GB VRAM): flat $499/month (₹48,403 in India) for your own dedicated, always-on GPU. NVIDIA A10G (24 GB VRAM): coming soon as its own dedicated tier.

What happens if I cancel?

Your GPU container is stopped gracefully at the end of the paid period. Your code and data are preserved. Subscribe again and restart — no data loss.

Can I use SnapDeploy GPU for free?

CPU containers are free — 10 deploys per day, no credit card required. GPU containers require the Dedicated GPU subscription ($499/month). There is no free GPU tier — a dedicated GPU has a real hardware cost that can't be subsidized.

Is the GPU really mine alone?

Yes. Each subscription is backed by one physical NVIDIA T4 on a dedicated on-demand instance. It is not shared, not virtualized across tenants, and not spot capacity that can be reclaimed.

Getting Started

1. Create a free SnapDeploy account
2. Subscribe to Dedicated GPU from the billing page ($499/month, cancel anytime)
3. Create a GPU container — your subscription attaches to it automatically
4. Connect your GitHub repository (or pick a one-click template) and deploy

For a step-by-step deployment walkthrough, see our guide on deploying AI models on GPU cloud. To deploy instantly without writing code, try our one-click GPU templates.