GPU Cloud
H100, A100 and L40S capacity on demand, billed per second. Spin up a node faster than your coffee order — release it just as fast.
- Per-second billing
- Reserved & spot pools
- NVLink multi-GPU nodes
inference uptime 99.99% · last 90 days
Every second of model latency loses a user, and every idle GPU burns money you'll never get back. Cloud Industry runs your models on H100-class compute with sub-25ms first tokens, a 99.99% inference SLA, and hard budget caps — so the invoice is never the surprise. We mirror your current stack free, side by side, before you move a single request.
30-day money-back guarantee · $0 egress — leave anytime · Hard spend limits
$ cloud deploy llama-3-70b▊
The amber bar? June 23 — four minutes of degraded performance, self-healed, every affected customer credited automatically. We show it because trust is built on the days that don't go perfectly.
One platform, one invoice, one team that answers for all of it. No juggling a GPU broker, a vector-DB vendor and a gateway startup when inference breaks at 3 a.m.
H100, A100 and L40S capacity on demand, billed per second. Spin up a node faster than your coffee order — release it just as fast.
Serverless, OpenAI-compatible APIs for any model. Scale to zero when idle, autoscale when you're on the front page.
Llama, Mistral, Qwen and hundreds of open models — plus your own fine-tunes — deployed from a registry, versioned like code.
Managed training jobs with checkpoints, spot-interruption recovery and cost estimates before you press run — not after.
Managed vector search and embedding pipelines that stay in sync with your data — retrieval that doesn't hallucinate your reputation away.
Long-running agents with tool sandboxes, queues and human-in-the-loop checkpoints. Built for work that takes hours, not tokens.
Dedicated GPU clusters in your VPC with EU data residency. Your prompts and weights never share a machine with anyone.
Token-level tracing, latency percentiles, eval suites and spend alerts. The same dashboards our engineers watch, in your console.
The only reason teams keep overpaying for inference is the fear of moving production traffic. Here's how we make that fear irrational:
Endpoints, models, weights, traffic profile. Read-only access or a description is enough — that's the entire extent of your work.
Our ML engineers stand up an identical deployment — same models, same context lengths — and benchmark it side by side against your current provider.
You see the p95 latency and the projected invoice before a single production request moves. If we don't win on both, you've lost nothing.
Most parallel runs are live within 48 hours. Your users never notice — except that answers arrive faster.
Observability, budget caps, DDoS protection and real support are included everywhere — we don't hold your production safety hostage for an upsell.
For power users & side projects
$59 29/mo
Choose BuildFor products with real traffic
$399 199/mo
Choose ScaleFor private, regulated & high-scale AI
$1,999 999/mo
Talk to an engineerPrices shown are introductory placeholder rates; usage beyond included quotas is billed per token/GPU-second with hard caps you set. Every plan includes the 30-day money-back guarantee — if we're not faster and cheaper than your current stack, you get every cent back. No questions, no retention scripts.
Every support seat at Cloud Industry is held by someone who has debugged a CUDA OOM at 2 a.m. — no tiers, no escalation maze, no "have you tried a smaller batch size." When your inference is down, minutes are churn, and we act like it.
"Our inference bill dropped 58% the week we flipped traffic. Same models, same latency budget — we watched the side-by-side benchmark for two weeks before believing it."
"CUDA OOM on a custom fine-tune at 2 a.m. I opened chat expecting a bot and got an engineer who'd hit the same bug. Eleven minutes, fixed, checkpoint intact."
"Launch day: 50× traffic in an hour. The endpoints autoscaled, p95 never moved, and the spend cap meant I wasn't afraid to look at the bill."
Run us in parallel with your current stack for 30 days. If we're not measurably faster and cheaper, one email gets you a full refund — and your weights leave with $0 egress. We can make that offer because almost no one takes it.
Never. Prompts, outputs, weights and datasets are encrypted, isolated per tenant, and used for exactly one thing: serving your requests. Delete anything anytime — deletion is real, not a soft flag.
Popular open models are cached close to the GPUs, and paid tiers keep warm pools, so scale-from-zero typically completes before your retry logic notices. If a model does need a cold load, the console shows you the expected warm-up before you route traffic to it.
H100, A100 and L40S pools, with reserved capacity for Scale and Enterprise plans. The console shows live availability per region — we'd rather show you the real number than promise you a waitlist.
No. Endpoints are OpenAI-compatible, weights and embeddings export anytime, and egress is $0 — leaving is a config change, not a project. We'd rather earn your renewal than trap it.
Hard budget caps, per-endpoint spend limits and anomaly alerts are built in on every plan. When a cap is hit, you choose what happens — throttle, queue, or stop — and you chose it in advance, calmly, not at 3 a.m.
It's contractual, not decorative: if measured monthly endpoint availability falls below 99.99%, you're automatically credited per our SLA — you don't have to notice, file, or fight for it.
Deploy a model in minutes, or show us your current stack and watch it run cheaper side by side within 48 hours. Either way, the guarantee means you risk nothing.
30-day money-back · Free parallel migration · $0 egress · Cancel anytime