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Salad Developer Tools

from $0.005/hr

Salad (Developer Tools): SaladCloud – rent low-cost GPU instances from $0.005/hr for AI inference, batch jobs, and rendering. Pricing: from $0.005/hr. (data verified August 2026)

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About Salad

What is Salad?

Salad is a distributed GPU cloud platform that taps into a global network of over 60,000 daily active consumer GPUs—think Airbnb for GPUs. It offers low-cost, high-scale compute for AI workloads, with prices starting at just $0.005 per hour for general-purpose instances and $0.084/hr for a batch priority RTX 3060.

Who is it for?

Salad is ideal for startups, developers, and enterprises that need to scale AI inference, batch data processing, HPC workloads, or rendering queues without the high costs of traditional hyperscalers. It’s especially useful for teams with fluctuating demand who want to avoid managing VMs. It may not suit workloads requiring dedicated, guaranteed hardware or those with strict compliance needs.

Real use cases

Customers like Civitai use Salad to serve inference on 600+ consumer GPUs, delivering 10 million images per day and training 15,000 LoRAs monthly. Blend runs 3x more scale at half the cost of A100s, and Klyne.ai accesses thousands of GPUs cost-effectively. Salad also powers automated news bots, model benchmarking, and edge applications.

Key features

  • Low-cost GPU instances — From $0.005/hr for general-purpose, with GPU options starting at $0.084/hr (RTX 3060 batch).
  • Container-based deployment — Use Salad Container Engine to deploy workloads without managing VMs.
  • Global edge network — Low-latency nodes in nearly every corner of the planet.
  • Multi-cloud compatible — Deploy alongside your existing cloud infrastructure.
  • Usage-based pricing — Pay only for runtime; no cold boot charges or initialization fees.
  • Volume discounts — Available for deployments of 50+ GPU instances.

SaaSpartout Score

7.4 /10
Ease of use 7.5
Features depth 8.0
Value for money 8.5
Support quality 6.5
Integrations 7.0
Scalability 8.5
Documentation 6.0
Onboarding speed 7.5

Editorial score from our review methodology — not user ratings.

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Salad Pricing

Salad pricing: from $0.005/hr.

For comparison: the median starting price in Developer Tools is $19.99/month, measured across 301 tools we track. See the full SaaS Pricing Index →

General Purpose Instances

Starting at $0.005/hr (1GB RAM, 1 vCPU). Fully customizable to your vCPU and RAM needs.

GPU Instances

Batch priority prices vary by GPU model: RTX 5090 at $0.294/hr, RTX 4090 at $0.204/hr, RTX 3090 at $0.124/hr, RTX 3060 at $0.084/hr, and more.

Free trial

No free trial for the container engine, but a 5-hour free transcription trial is available for the Salad Transcription API when you create an organization.

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Frequently asked questions

How much does Salad cost?
SaladCloud pricing starts at $0.005/hr for general-purpose instances and $0.084/hr for a batch priority RTX 3060 GPU. GPU instances range up to $0.294/hr for an RTX 5090.
Does Salad offer a free trial?
Salad Container Engine does not have a free trial. However, the Salad Transcription API offers a 5-audio-hour free trial automatically applied when you create an organization.
What is Salad best for?
Salad is best for AI inference, batch data jobs, HPC workloads, and rendering queues that need scalable, low-cost GPU compute. It's not ideal for workloads requiring dedicated hardware or strict compliance.
What are the top alternatives to Salad?
Alternatives include traditional hyperscalers like AWS, Google Cloud, and Azure, as well as other GPU cloud providers like Vast.ai, RunPod, and CoreWeave. Salad differentiates with its consumer GPU network and low prices.
How does Salad's pricing work?
You pay only for the time your container is running. There are no charges for cold boot or initialization, and volume discounts are available for deployments of 50+ GPU instances.
Who should not use Salad?
Salad may not suit enterprises with strict data sovereignty or compliance requirements, or workloads needing guaranteed uptime and dedicated hardware. It's best for flexible, cost-sensitive workloads.

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