Give every engineer (or student) their own GPU budget, bill it all to one shared wallet, get one GST invoice — and pay as little as ~10% of what AWS charges. Data stays in India.
Most of your team's GPU work isn't frontier training — it's experimentation, onboarding new hires, prototyping, fine-tuning small models, eval runs. That long tail is bursty and cost-insensitive to location, and it's massively overpaying on hyperscalers. That's exactly what GridShare captures.
Every new ML engineer needs GPU access to ramp. Give each one a capped budget — a fraction of handing out AWS keys, and finance keeps control.
Test an idea, fine-tune a 7B, run an eval, prototype a feature. None of it needs an H100 cluster — or hyperscaler prices.
Reserved quota full or a deadline hit? Elastic overflow capacity, on demand, cheap.
The part RunPod and Vast don't do: real org administration.
Set a monthly GPU cap for each member. A junior can't torch the bill; launches are blocked at the limit.
Fund the org once; every member draws from it. No personal cards, no expense reports.
One GST-compliant invoice for the whole team's usage — input-credit friendly.
Your admin creates each member's login and hands over credentials. Off-boarding deactivates them instantly.
Compute runs on Indian machines — lower latency, DPDP-friendly residency.
Spin up in minutes, no annual contract. Postpaid billing — pay within 30 days — available for enterprises.
For the long-tail work your team does every day.
| What matters | Hyperscalers (AWS/GCP) | P2P (RunPod/Vast) | GridShare |
|---|---|---|---|
| Price (on-demand) | ₹300+/hr datacenter | cheap | up to ~90% less |
| Per-person budgets | complex IAM | none | built in |
| One GST invoice | USD billing | USD, no GST | yes, INR + GST |
| Admin-provisioned team logins | — | — | yes |
| Priority when GPUs are busy | pay more | none | subscribers first |
| Data residency (India) | region-dependent | mostly US | India |
| Setup | contracts | self-serve | minutes |
The plan is a small platform fee for admin controls, priority, and support — not a markup on compute. Metered GPU time stays the same low rate everyone pays (a fraction of hyperscaler cost).
Colleges, universities, and training centers use the same org tools: provision a login per student, cap each one's budget, and bill it all to the institute. Verified students get an extra discount. Ideal for ML courses, hackathons, research labs, and bootcamps.
Each learner gets a capped allowance — no surprise bills.
Verified students get 15% extra credit on top.
PyTorch, Jupyter, TensorFlow — ready for coursework, zero setup.
For interruptible and experimental work — yes. Persistence and auto-save keep work alive across interruptions. We don't pitch it as 24/7 SLA-critical infrastructure; use it for the long-tail work that doesn't need that guarantee.
On Business and Enterprise plans, your team's launches jump the dispatch queue and start ahead of free-tier and individual jobs — no extra per-launch charge (it's included in the plan). It's faster placement when GPUs are busy, not a guaranteed reservation. If you need capacity guaranteed on tap, that's a reserved-capacity arrangement on Enterprise — talk to us.
Every workload runs in an isolated, sandboxed container with no access to the host's files. Credentials never touch provider machines, traffic is relayed (providers never see your IP), and compute stays in India.
Consumer/prosumer cards (RTX 3090/4090-class). Comparable per-GPU for single-node work; not multi-node frontier scale. Great for fine-tuning small/mid models, inference, notebooks, and prototyping.
That's our strength — your admin provisions each login, sets a per-person monthly cap, and everyone draws from one shared wallet with one GST invoice. Launches are blocked when a member hits their cap.
Yes — pilot one onboarding cohort or one team for a month, no commitment. Measure the cost delta, then expand.
Book a 20-minute demo, or spin up an organisation and try it with one team this week.