# Crusoe Cloud

> Cloud platform for training, fine-tuning, and deploying AI models with a focus on performance and cost efficiency

- **Canonical URI:** https://www.vioscale.ai/software/crusoe-cloud
- **Category:** GPU Cloud
- **Homepage:** https://crusoe.ai
- **Also known as:** crusoe-cloud
- **Profile claimed by vendor:** no
- **Last updated:** 2026-08-25T12:20:46.386Z

## Vioscale score

**54.6 / 100**, confidence 65% (medium). Computed 2026-09-01. _(updating — computed under an earlier model version or past its freshness window.)_

Composite of weighted, independently-sourced signals (no user reviews, no vendor payment).

| Signal | Score | Weight | Contribution | Evidence present |
|---|--:|--:|--:|:--:|
| price_level | 80 | 0.052 | 4.2 | ✓ |
| reliability | 25 | 0.073 | 1.8 | ✓ |
| capabilities | 81.2 | 0.04877777777777778 | 4 | ✓ |
| integrations | 0 | 0.04066666666666666 | 0 | - |
| security_posture | 25 | 0.073 | 1.8 | ✓ |
| pricing_transparency | 75 | 0.084 | 6.3 | ✓ |

## Pricing

_As of 2026-08-25, [verify at source](https://www.crusoe.ai/cloud/pricing). Independently observed._

from $0.06/GiB object storage/mo · Hybrid

> Hybrid usage-based model: on-demand GPU compute ($1.50–$4.29/GPU-hr), spot and reserved options, serverless inference and fine-tuning (per-token pricing), storage ($0.06–$0.10/GiB/month). No stated free tier.

| Plan | Price | Free | Commitment |
|---|---|:--:|---|
| GPU Instances — On-Demand | From $1.50/GPU-hr to $4.29/GPU-hr on-demand; spot and reserved options available | - | monthly |
| CPU Instances | $0.04–$0.09/vCPU-hr | - | - |
| Storage | $0.06–$0.10/GiB/month | - | - |
| Serverless Fine-Tuning | $0.40–$10.00 per 1M input tokens | - | - |
| Serverless Inference | Input/output token pricing varies by model; from $0.05 to $5.00+ per 1M tokens | - | - |
| Self-Serve Deployments | $5.50/GPU-hr; volume and monthly rates available | - | - |
| Tailored Deployments | Contact sales | - | - |
| Provisioned Throughput | Contact sales | - | - |
| Managed Kubernetes | Per-cluster-hour pricing (specific rates not listed) | - | - |

### GPU Instances — On-Demand

Pay hourly for GPU compute without lock-in

- Latest high-performance GPUs
- Unthrottled compute
- NVIDIA GB200 NVL72
- AMD MI355X support

### CPU Instances

Hourly billing for CPU-based compute

- Data processing
- Model checkpointing
- Cluster orchestration

### Storage

- Persistent disks
- Shared disks
- Container registry
- Object storage

### Serverless Fine-Tuning

Customize models with proprietary data; billed by model size and token count

- One-click deployment
- No cluster provisioning
- No surprise bills
- Full model portability

### Serverless Inference

Run LLMs and generative models with pay-per-token pricing

- DeepSeek V3, V4 Pro, V4 Flash
- Gemma, GLM, GPT-OSS, Kimi K2.6
- Llama 3.3, Nemotron 3 family
- Qwen 3
- Up to 9.9x faster time to first token
- 5x higher throughput vs vLLM

### Self-Serve Deployments

Dedicated endpoints for open and fine-tuned models; no sales engagement required

- Deploy in minutes
- Open or fine-tuned models
- No infrastructure overhead

### Tailored Deployments (Contact sales)

Dedicated endpoints with optimization and benchmarking support

- Direct team support
- Highest optimization
- Benchmarked endpoints
- Bring your own model

### Provisioned Throughput (Contact sales)

Guaranteed throughput for generative AI applications using AI Model Units (AMUs)

- Guaranteed throughput
- Discounts for longer commitments

### Managed Kubernetes

Fully managed cluster for deployment and scaling across GPU and CPU resources

- Full cluster management
- GPU and CPU orchestration
- Autoscaling
- Fault tolerance via AutoClusters

## About

A cloud infrastructure provider offering GPU compute, managed Kubernetes, and serverless services for large-scale AI workloads. Users can run open-source models, fine-tune proprietary models, or serve inference endpoints with published pricing and flexible commitment options.

_Independently observed._

## Platform & deployment

- **Platforms:** CLI, Web
- **Deployment:** Cloud / SaaS

## Capabilities

_The capabilities that matter for GPU Cloud. "-" = undocumented, not absent._

| Capability | Supported |
|---|:--:|
| **Capabilities** | |
| Deployment model | - |
| Interconnect | - |
| Max cluster scale | Multi node cluster |
| Kubernetes support | ✓ |
| Slurm support | ✓ |
| Spot preemptible pricing | ✓ |
| Per second billing | ✗ |
| Reserved capacity contracts | ✓ |
| Amd GPU support | ✓ |
| SOC2 | - |
| Multi region | ✓ |
| Pricing model | On demand published |

## Facts

Every value below carries its source and our confidence. Facts are re-crawled on a freshness schedule.

### security

| Attribute | Value | Source | Retrieved | Confidence |
|---|---|---|---|---|
| security.iso27001 | yes | [link](https://crusoe.ai) | 2026-08-17 | 48% (low) |

### market

| Attribute | Value | Source | Retrieved | Confidence |
|---|---|---|---|---|
| market.availability | `{"primaryMarkets":[],"availabilityScope":"global","availableCountries":[],"notAvailableCountries":[]}` | [link](https://www.crusoe.ai/cloud/pricing) | 2026-08-25 | 75% (high) |

### pricing

| Attribute | Value | Source | Retrieved | Confidence |
|---|---|---|---|---|
| pricing.free_tier | no | [link](https://www.crusoe.ai/cloud/pricing) | 2026-08-25 | 60% (medium) |
| pricing.model | commercial | [link](https://www.crusoe.ai/cloud/pricing) | 2026-08-25 | 60% (medium) |
| pricing.price_level | low | [link](https://www.crusoe.ai/cloud/pricing) | 2026-08-25 | 60% (medium) |
| pricing.starting_price | `{"amount":0.06,"currency":"USD"}` | [link](https://www.crusoe.ai/cloud/pricing) | 2026-08-25 | 60% (medium) |
| pricing.transparent | yes | [link](https://www.crusoe.ai/cloud/pricing) | 2026-08-25 | 60% (medium) |

### reliability

| Attribute | Value | Source | Retrieved | Confidence |
|---|---|---|---|---|
| reliability.sla_pct | 99.5 | [link](https://www.crusoe.ai/cloud/pricing) | 2026-08-25 | 60% (medium) |

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*Source: Vioscale (https://www.vioscale.ai/software/crusoe-cloud). Independent, evidence-based software intelligence. Cite the canonical URI.*
