inference uptime 99.99% · last 90 days

AI infrastructure
for teams that
actually ship.

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

cloudindustry — deploy live

$ cloud deploy llama-3-70b

first token, p50
99.99%inference SLA
210edge regions
inference uptime — one bar per day, last 90 dayslast 30 days 99.99%

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.

0teams running production AI
0median first-token latency
0support rating from 3,000+ reviews
0egress fees — your weights leave free
Services

Everything your AI stack needs.
Nothing it doesn't.

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.

ai/gpu

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
ai/serverless

Inference Endpoints

Serverless, OpenAI-compatible APIs for any model. Scale to zero when idle, autoscale when you're on the front page.

  • Scale-to-zero
  • Warm pools, no cold starts
  • OpenAI-compatible API
ai/models

Model Hosting

Llama, Mistral, Qwen and hundreds of open models — plus your own fine-tunes — deployed from a registry, versioned like code.

  • 300+ open models
  • Your weights, versioned
  • Instant rollback
ai/train

Fine-tuning & Training

Managed training jobs with checkpoints, spot-interruption recovery and cost estimates before you press run — not after.

  • LoRA & full fine-tunes
  • Auto checkpointing
  • Upfront cost estimate
ai/rag

Vector & RAG Infra

Managed vector search and embedding pipelines that stay in sync with your data — retrieval that doesn't hallucinate your reputation away.

  • Managed vector DB
  • Embedding pipelines
  • Hybrid search built in
ai/agents

Agent Runtime

Long-running agents with tool sandboxes, queues and human-in-the-loop checkpoints. Built for work that takes hours, not tokens.

  • Sandboxed tool use
  • Durable queues
  • Approval checkpoints
ai/private

Private AI

Dedicated GPU clusters in your VPC with EU data residency. Your prompts and weights never share a machine with anyone.

  • Single-tenant clusters
  • VPC peering
  • EU data residency
ai/obs

Observability & Evals

Token-level tracing, latency percentiles, eval suites and spend alerts. The same dashboards our engineers watch, in your console.

  • Token-level traces
  • Continuous evals
  • Hard budget caps
Switching

Leaving big-cloud GPUs feels risky.
So we removed the risk.

The only reason teams keep overpaying for inference is the fear of moving production traffic. Here's how we make that fear irrational:

step 1

Show us your stack

Endpoints, models, weights, traffic profile. Read-only access or a description is enough — that's the entire extent of your work.

step 2

We mirror it, free

Our ML engineers stand up an identical deployment — same models, same context lengths — and benchmark it side by side against your current provider.

step 3

Flip traffic when the numbers win

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.

Pricing

Honest pricing.
Every feature that matters, on every plan.

Observability, budget caps, DDoS protection and real support are included everywhere — we don't hold your production safety hostage for an upsell.

Build

For power users & side projects

$59 29/mo

Choose Build
  • Serverless inference, all open models
  • 5M tokens included monthly
  • Scale-to-zero endpoints
  • Token-level tracing
  • 24/7 engineer chat
Most popular — 7 in 10 choose this

Scale

For products with real traffic

$399 199/mo

Choose Scale
  • Dedicated GPU-hour pool
  • Autoscaling endpoints, warm pools
  • Fine-tuning jobs included
  • Managed vector DB & RAG pipelines
  • Free parallel migration
  • Priority 24/7 support

Enterprise

For private, regulated & high-scale AI

$1,999 999/mo

Talk to an engineer
  • Everything in Scale
  • Private single-tenant GPU cluster
  • VPC peering & EU data residency
  • 99.99% SLA with automatic credits
  • Dedicated ML engineer

Prices 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.

Support

Talk to an ML engineer.
Not a script.

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.

  • Live chat24/7/365 · median first reply under 2 minutes
  • Ticketsfull resolution, median 1.4 hours
  • Phoneon Scale & Enterprise plans — a human picks up
0median first response, live chat
0issues solved on first contact
24/7real engineers, every timezone
0chatbots between you and help
Customers

People don't love their AI infra.
Ours do — loudly.

"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."
CTOAI SaaS, migrated from a hyperscaler
"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."
ML leadruns 30+ fine-tuned models
"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."
Founderconsumer AI app, Scale plan

Try us with zero risk. All of it is ours.

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.

Start now
FAQ

Everything people ask
before they switch.

Do you train on my prompts or my data?+

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.

What about cold starts?+

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.

Which GPUs do you run, and is capacity actually available?+

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.

Am I locked in?+

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.

How do I keep costs from exploding?+

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.

What does the 99.99% inference SLA actually mean?+

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.

Your users are waiting on your model.
Don't make them.

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