Dedicated GPU · NVIDIA T4 · Flat monthly rate
One physical NVIDIA T4 (16 GB VRAM), exclusively yours, running 24/7 for a flat $499/month (₹48,403 in India, no GST). Never shared, never spot, never interrupted — and never metered by the hour.
Flat monthly rate · Cancel anytime · One physical T4 per subscription · CUDA pre-installed
Most "GPU cloud" offers time-slice, virtualize, or spot-bid the hardware. SnapDeploy's NVIDIA T4 hosting gives you the whole card on a dedicated on-demand instance — the GPU equivalent of a private office, not a hot desk.
A physical T4 on a dedicated on-demand g4dn.xlarge-class instance with 4 vCPUs and 16 GB of system RAM.
Not shared, not virtualized, not partitioned. torch.cuda.is_available()
returns True out of the box.
Your model weights stay loaded in VRAM around the clock. No cold starts, no wake-up delay, no hourly metering. The first request of the day is as fast as the thousandth.
Dedicated on-demand capacity is never reclaimed mid-inference. No spot evictions taking your API down mid-request, no capacity lotteries when demand spikes.
The bill never changes, no matter the traffic. A runaway retry loop or a viral day cannot change what you pay. Cancel anytime — the subscription stops immediately.
The subscription buys the GPU and the platform around it. You bring a repository; SnapDeploy does the rest. There is no IAM to configure, no VPC to design, no CUDA AMI to maintain, and no deploy pipeline to build — push to GitHub and your GPU container rebuilds and redeploys automatically.
Any Docker container
Deploy any CUDA workload from GitHub — SnapDeploy scans your dependencies, picks the right CUDA base image, and builds the Docker image for you. No CUDA driver installs, no GPU runtime config.
One-click AI templates
Pre-built PyTorch, TensorFlow, Hugging Face, and ONNX Runtime templates with CUDA and FastAPI pre-configured — live inference endpoints in 2-3 minutes. See the templates guide.
Domains, TLS & live logs
Every GPU container gets a public HTTPS URL with free TLS, custom domain support, live logs, and monitoring from one dashboard. Production plumbing included, not bolted on.
There is exactly one number to know: $499/month (₹48,403 in India, no GST). No per-second billing, no credits to top up, no forgotten instances quietly burning money overnight.
≈ $0.68/hr effective over ~730 hours of 24/7 runtime — fully managed.
The raw instance is cheaper per hour — but you become the platform team.
The ~$0.15/hr spread is what "fully managed" 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. Full breakdown in our GPU cloud pricing guide.
Anything that speaks CUDA. The T4's 16 GB of VRAM comfortably serves most production inference workloads:
SnapDeploy scans your dependency files before every build and blocks common mistakes early — GPU packages headed for a CPU container, or a GPU container with nothing that needs CUDA. It also auto-installs system dependencies your framework needs, like ffmpeg for Whisper or libsndfile for librosa.
Deploy AI models in three steps:
Step-by-step walkthrough with code examples in our guide to deploying AI models on GPU cloud containers.
Monthly cost of running a T4-class GPU 24/7, side by side. Competitor prices are approximate, based on published rates as of September 2026 — check their pricing pages for current numbers.
| Platform | ~Monthly (24/7) | Billing model | What you can run |
|---|---|---|---|
| SnapDeploy Dedicated GPU | $499 flat | Flat monthly, dedicated physical T4 | Any Docker container — APIs, UIs, batch jobs, full apps |
| Hugging Face Endpoints (T4) | ~$365 | Per-hour, managed endpoint | Model inference endpoints only |
| Baseten (dedicated) | ~$460 | Per-minute, dedicated deployment | Model serving only |
| Northflank (L4) | ~$584 | Per-hour, containers | Containers (L4-class GPU) |
| Replicate | ~$591 equivalent | Per-prediction / per-second | Hosted model predictions |
| Modal | ~$800 equivalent | Per-second serverless compute | Python functions and jobs |
The honest read: cheaper T4-class rates exist if you only need bursts, and some rivals undercut $499 for pure inference endpoints. SnapDeploy's difference is scope — a dedicated physical GPU that is exclusively yours, running any Docker container 24/7, with domains, TLS, logs, and GitHub deploys included, at a price that cannot surprise you.
Honesty section. Skip this part if you like surprises.
Training large models. The T4 is an inference and light fine-tuning card. Training 7B+ parameter LLMs or running Stable Diffusion XL at full resolution wants more VRAM than 16 GB. An NVIDIA A10G tier (24 GB VRAM) is coming soon as its own dedicated flat-rate tier.
Short bursts. If your workload is a one-off experiment measured in single hours, a per-second provider will be cheaper for that burst. Flat-rate dedicated GPU hosting pays off when the GPU works for a living — production APIs, always-warm inference, steady pipelines.
CPU-sized workloads. If your app doesn't use CUDA, you don't need this. SnapDeploy's CPU containers deploy free (10 deploys/day) with Always-On from $12/month — see full pricing.
One physical NVIDIA T4 with 16 GB of VRAM, on a dedicated on-demand g4dn.xlarge-class instance with 4 vCPUs and 16 GB of system RAM. CUDA comes pre-installed, and the GPU runs 24/7 with no auto-sleep and no hourly metering.
Really dedicated. Each subscription is backed by one physical T4 — not shared with other tenants, not virtualized or partitioned, and not spot capacity that can be reclaimed mid-inference.
Any Docker container that uses NVIDIA CUDA — PyTorch, TensorFlow, Hugging Face Transformers, ONNX Runtime, Whisper, Stable Diffusion, vLLM, Gradio, Streamlit, and more. Deploy your own GitHub repository, or use the one-click templates and be live in 2-3 minutes.
Replicate bills per prediction and Modal bills per second — costs scale with usage and can reach roughly $590-$800/month for T4-class capacity running 24/7 (approximate, as of September 2026). SnapDeploy is a flat $499/month for a GPU that is exclusively yours, running any Docker container — not just model endpoints.
₹48,403 per month, no GST added — the Indian-rupee equivalent of the flat $499/month rate. Same dedicated NVIDIA T4, same 24/7 runtime, cancel anytime.
Yes. Cancel from your billing page at any time — cancellation takes effect immediately and your GPU container is stopped. Your code and container configuration are preserved, so you can subscribe again later and redeploy without starting over.
One physical NVIDIA T4. Flat $499/month. Running 24/7, exclusively for you.
Cancel anytime · ₹48,403/mo in India, no GST · See full pricing