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Cloudzy vs Jarvis Labs: Pricing, Specs, Strengths & Weaknesses

Side-by-side comparison of GPU compute pricing, regions, billing models, and strengths for Cloudzy and Jarvis Labs. Rates sourced from public APIs — verify current availability and billing terms before purchasing.

Overview

Provider type
Specialist
Specialist
Founded
2008
2020
Headquarters
Dubai, UAE
San Francisco, CA
Billing model
Monthly, Annual (35% off)
On-demand (per-second)
Min commitment
1 month
None
Support tier
Standard (24/7 human support)
Community → Pro
Regions
4 regions
2 regions

Strengths & Best For

Cloudzy

Cloudzy offers dedicated GPU VPS plans with RTX PRO 6000 Blackwell (96 GB GDDR7 ECC), Nvidia A100 (80 GB HBM2e), RTX 5090 (32 GB GDDR7), and RTX 4090 (24 GB GDDR6X) in 1× to 4× configurations. Annual pricing (35% off): RTX PRO 6000 $845/mo, A100 $714/mo, RTX 5090 $649/mo, RTX 4090 $506/mo. Monthly pricing: RTX PRO 6000 $1,300/mo, A100 $1,099/mo, RTX 5090 $999/mo, RTX 4090 $779/mo. All plans include CUDA pre-installed, AMD EPYC + DDR5, pure NVMe storage, 40 Gbps uplinks, and no egress fees. 14-day money-back guarantee. 13 global locations. Independent since 2008, rated 4.6/5 on Trustpilot. Accepts crypto (BTC, XMR, USDT).

Strengths
  • RTX PRO 6000 Blackwell (96 GB GDDR7 ECC) — dedicated passthrough
  • CUDA pre-installed — PyTorch, TF, JAX ready out of the box
  • 40 Gbps uplinks, no egress fees, pure NVMe storage
  • 35% annual discount — RTX PRO 6000 $845/mo, A100 $714/mo
  • 14-day money-back guarantee
  • Accepts BTC, XMR, USDT crypto payments
Weaknesses
  • Monthly billing only — no per-hour or per-second options
  • No InfiniBand — multi-node training over standard Ethernet only
  • Smaller provider — limited scale vs hyperscalers
Best For
Teams wanting dedicated (non-shared) GPU VPSLLM inference on RTX PRO 6000 (96 GB VRAM)Long-running training jobs on dedicated hardwareCrypto-native teams
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Jarvis Labs

Jarvis Labs is an ML-focused GPU cloud offering H100, A100, and RTX instances with per-second billing, pre-configured environments for PyTorch, TensorFlow, and other popular frameworks, and a simple interface designed for machine learning engineers. On-demand GPU rental with no minimum commitment makes it easy to spin up and tear down instances for training runs, fine-tuning, and AI inference experiments. A popular choice for ML engineers who want pre-built environments and granular per-second billing.

Strengths
  • Per-second billing — no minimum commitment
  • Pre-configured PyTorch/TensorFlow environments
  • Simple UI designed for ML engineers
  • Fast provisioning
Weaknesses
  • Small provider — limited GPU availability at scale
  • No enterprise SLAs
  • Limited regions and GPU SKU variety
Best For
ML engineers wanting pre-built environmentsNotebook-based training workflowsTeams wanting per-second billing on GPU instances
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Live GPU Pricing

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Region Coverage

Cloudzy — 4 regions
USEUMiddle EastAPAC (13 locations)

Popular Comparisons

Key differences

Cloudzy

RTX PRO 6000 $845/mo, A100 $714/mo, RTX 5090 $649/mo — dedicated GPU VPS, CUDA pre-installed

Jarvis Labs

ML-focused GPU cloud with per-second billing and pre-configured environments

AttributeCloudzyJarvis Labs
Provider typespecialistspecialist
Billing modelMonthly, Annual (35% off)On-demand (per-second)
Min commitment1 monthNone
Regions4 (US, EU, Middle East, +1)2 (US, EU)
Support tierStandard (24/7 human support)Community → Pro
Best forTeams wanting dedicated (non-shared) GPU VPS, LLM inference on RTX PRO 6000 (96 GB VRAM)ML engineers wanting pre-built environments, Notebook-based training workflows
Founded / HQ2008 · Dubai, UAE2020 · San Francisco, CA