SaladCloud
Cut cloud GPU costs by up to 90% with distributed computing
Target Audience
- AI startups needing affordable scaling
- Researchers processing large datasets
- Enterprises optimizing cloud costs
- PC owners monetizing idle GPUs
Hashtags
Overview
SaladCloud connects businesses needing AI/GPU computing power with individuals worldwide who share their idle GPUs. It offers dramatically lower prices than traditional cloud providers while providing massive scalability for AI training, inference, and rendering workloads. The platform uses a unique 'computesharing' model that turns underutilized consumer and enterprise GPUs into an affordable, sustainable cloud alternative.
Key Features
Usage-Based Pricing
Pay from $0.02/hour with no upfront commitments
Global Edge Network
Access 1M+ nodes across 180+ countries
Security First
SOC2 certified with container isolation & intrusion detection
Massive Scalability
Deploy to thousands of GPUs without managing VMs
Sustainability Focus
Utilizes existing hardware to reduce e-waste
Use Cases
Run AI inference at 10X lower cost
Process GPU-heavy batch jobs & rendering
Scale language models cost-effectively
Deploy geo-distributed AI applications
Monetize idle GPUs (provider side)
Pros & Cons
Pros
- Up to 90% cheaper than AWS/Azure GPUs
- No ingress/egress fees or VM management
- Automatic workload redistribution if nodes fail
- Ethical alternative to big cloud monopolies
Cons
- Longer cold start times than traditional clouds
- Max 24GB vRAM per GPU (consumer-grade hardware)
- Not ideal for ultra-low latency applications
Pricing Plans
Pay-As-You-Go
hourlyFeatures
- Access to RTX/GTX class GPUs
- Global edge network
- Container isolation security
- Usage-based billing
Pricing may have changed
For the most up-to-date pricing information, please visit the official website.
Visit websiteFrequently Asked Questions
What GPUs are available?
Exclusively Nvidia RTX/GTX consumer-grade GPUs with up to 24GB vRAM, rigorously tested for AI workloads.
How secure is my data?
Containers run in isolated environments with encryption at rest/transit. Intrusion attempts trigger automatic shutdowns.
Can I run latency-sensitive workloads?
Not recommended - cold starts and potential interruptions make it better for batch processing than real-time apps.
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