Salad
Salad is a distributed GPU cloud offering low-cost, geo-distributed compute for AI, inference, training, and other GPU-heavy workloads.
Salad
Distributed GPU cloud for low-cost AI workloads
What is Salad?
How to use Salad?
- 1Create a Salad account and contact sales if you need discounted high-volume pricing.
- 2Choose the GPU type and quantity that fit your workload.
- 3Package your app as a Docker container for Salad Container Engine.
- 4Deploy the workload to SaladCloud and monitor availability, scaling, and interruptions.
- 5Scale up or down as demand changes without managing individual VMs.
Salad Key Features
- Distributed GPU cloud with geo-distributed nodes
- Docker container deployment via Salad Container Engine
- Usage-based pricing with low starting rates
- High-scale inference and batch workload support
- Multi-cloud compatible deployment
- Automatic workload reallocation when nodes go offline
- Security isolation with encrypted containers
- No VM management required
Salad Use Cases
- AI inference at scale
- Model training and fine-tuning
- Text-to-image generation
- Speech-to-text transcription
- Computer vision workloads
- LLM deployment
- Batch processing and rendering
- HPC-style GPU workloads
Salad Pricing & Free Credits
Salad currently operates on a Paid, Custom Pricing model.
Salad Pros & Cons
Pros
- Very low starting GPU pricing
- Large distributed GPU network
- Good fit for scalable AI inference
- Docker-based deployment simplifies setup
- Usage-based pricing with no prepayments
Cons
- GPU availability can be interrupted like spot capacity
- Longer cold starts than typical cloud GPUs
- Highest vRAM on the network is limited to 24 GB
- Not ideal for extremely low-latency workloads
What is Salad best for?
- AI teams needing low-cost GPU inference
- Startups scaling model workloads quickly
- Developers deploying containerized GPU apps
- Businesses seeking cheaper alternatives to major clouds
- Workloads that can tolerate spot-like interruptions
Salad FAQ
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