Jarvis Labs

Jarvis Labs

Jarvis Labs is a Bangalore-based GPU cloud provider that launched in 2020 with a clear focus: make deep learning infrastructure dead simple for practitioners who just want to train models, not manage infrastructure. Their signature feature — the ability to pause a running instance and resume it later without losing your work — solves one of the most frustrating pain points in GPU cloud usage.

If you’ve ever accidentally left a GPU instance running overnight while you slept, you understand why pause-and-resume matters. Jarvis Labs builds its entire experience around this idea of “train when you need to, stop when you don’t.”

Why Jarvis Labs stands out

The pause-and-resume workflow is genuinely differentiated. Most GPU clouds bill you for idle time or force you to snapshot and restore manually. Jarvis Labs makes pausing a first-class operation — your environment, your data, your running processes all persist. For researchers doing iterative experiments across days or weeks, this dramatically changes the economics.

The platform also leans hard into developer experience. Jupyter notebooks come pre-configured, Docker environments are available, and the UI has an ease-of-use score that puts it above many competitors. It’s clearly built by people who use it themselves.

Their GPU catalog covers a useful range — from mid-tier workhorses like the A5000 and A6000 up through A100s, H100s, and even H200s for large-scale training runs.

Pros

• Pause & resume is a genuine killer feature for iterative deep learning work
• Persistent storage keeps your data and environment safe across sessions
• Clean UI with high ease-of-use score — minimal friction to get started
• Multi-GPU support for scaling up when needed
• API access for programmatic instance management
• Solid mid-tier GPU selection — A5000, A6000, and RTX 6000 Ada are sweet spots for fine-tuning

Cons

• Pricing is not the draw — competitiveness scores below average; you’re paying a premium for the convenience features
• No spot instances — no way to trade reliability for cost savings on interruptible workloads
• No Kubernetes support — not a fit for orchestrated container workloads
• No SOC2 compliance — enterprise or regulated workloads will need to look elsewhere
• Credit card only — no invoicing or enterprise billing options
• Limited enterprise readiness overall — this is a tool for practitioners, not IT departments

Getting started

1. Visit Jarvis Labs and create an account with your email
2. Add a credit card — it’s the only payment method supported
3. Browse the GPU catalog and select an instance type that fits your workload
4. Choose a pre-built framework image (PyTorch, TensorFlow, JAX, etc.) or bring your own Docker image
5. Launch your instance — Jupyter will be ready within seconds
6. When you’re done for the day, pause rather than terminate — your environment will be waiting exactly where you left it

Best for: Deep learning researchers and ML engineers who prioritize developer experience and want the flexibility to pause experiments between sessions without losing state — and who are willing to pay a modest premium for that convenience.