SaladCloud
Distributed consumer-GPU compute for containerized AI inference and batch jobs, with usage-based rates starting at $0.02 per hour.
About SaladCloud
SaladCloud is a distributed GPU cloud for containerized AI and machine-learning workloads. Salad Container Engine places Docker containers across a network of unused consumer compute. Users select GPU classes, RAM, vCPUs, and replica counts without managing each host themselves. It fits inference, batch processing, image generation, object detection, and other workloads that can operate on a distributed network. Usage-based pricing starts at $0.02 per hour, with rates varying by card. Listed examples include RTX 5060 Ti at $0.07 per hour, RTX 4090 at $0.16 per hour, and RTX 5090 at $0.25 per hour. Ready-to-deploy recipes include FLUX.1-Schnell, Stable Diffusion XL, YOLOv8, Llama 3.1, and Mistral 7B. The architectural tradeoff matters: inexpensive consumer GPUs and broad capacity may work well for horizontally scalable jobs, but teams still need to containerize their workloads and account for hardware variability, availability, networking, and distributed-cloud behavior when setting latency and reliability targets.
Best for
Best use cases
Use it when a containerized inference service can scale horizontally across several GPU replicas.
Use it for batch jobs where throughput and compute cost matter more than uniform data-center hardware.
Use a prepared model recipe when testing image generation, object detection, or language-model deployment.
Compare before choosing
Compared with hyperscaler GPU instances, SaladCloud emphasizes lower usage rates on distributed consumer hardware.
Compared with managing independent GPU hosts, Salad Container Engine provides container scheduling and replica controls.
Compared with a uniform data-center fleet, hardware variability and distributed-network behavior need more workload testing.
Features
Container orchestration
Deploy Docker workloads through Salad Container Engine without managing individual GPU hosts.
Configurable compute
Choose GPU class, RAM, vCPUs, and replica counts for each container workload.
Model recipes
Start from recipes for FLUX.1-Schnell, Stable Diffusion XL, YOLOv8, Llama 3.1, and Mistral 7B.
Pros
- Usage-based GPU rates start at $0.02 per hour, which can reduce compute cost for suitable scalable workloads.
- Docker-based deployment and replica controls reduce the need to operate individual consumer-GPU hosts.
- Multiple GPU classes and ready-made model recipes support varied inference, vision, and batch workflows.
Cons
- Workloads must be packaged as Docker containers, adding migration work for applications not already containerized.
- A distributed consumer-GPU fleet can introduce hardware variability that requires workload-specific testing.
- Rates vary by GPU and configuration, so the advertised $0.02 hourly starting point is not the price for every workload.
Pricing Plans
Pay as you go
- Usage-based billing
- Configurable GPU class, RAM, and vCPUs
- No contract or prepayment required
* Pricing information is for reference only. Please refer to the latest announcement on SaladCloud's official website.