Crusoe vs Together AI Comparison
Detailed comparison of features, pricing, and capabilities
Last updated May 1, 2026
Overview
Compare key metrics and features at a glance
Crusoe
https://www.crusoe.ai
Crusoe is an AI cloud infrastructure company that provides purpose-built cloud computing services optimized for AI workloads, including GPU clusters for training and inference. Originally founded as Crusoe Energy Systems, the company pivoted to focus on sustainable AI cloud computing, leveraging stranded and flared natural gas to power data centers, reducing carbon emissions compared to traditional grid-powered facilities. Crusoe offers high-performance computing resources tailored for machine learning, generative AI, and large-scale model training, positioning itself as an environmentally conscious alternative to hyperscale cloud providers.
Together AI
https://www.together.ai
Together AI is a cloud platform that enables developers and enterprises to run, fine-tune, and deploy open-source large language models (LLMs) at scale with high performance and cost efficiency. The platform provides access to a wide range of open-source models including LLaMA, Mistral, and others through a unified API, along with tools for custom model fine-tuning and inference optimization. Together AI also conducts AI research and has developed its own inference infrastructure designed to deliver fast and affordable generative AI capabilities.
Quick Comparison
| Detail | Crusoe | Together AI |
|---|---|---|
| Category | AI Cloud Infrastructure | AI Cloud Infrastructure |
| Starting Price | Contact Sales | Free |
| Plans Available | 5 | 6 |
| Features Tracked | 17 | 15 |
| Founded | 2018 | 2022 |
| Headquarters | San Francisco, USA | San Francisco, USA |
Features
Detailed feature-by-feature comparison
Feature Comparison
| Feature | | |
|---|---|---|
| api | ||
| OpenAI-Compatible APIs | ||
| core | ||
| 99.98% Uptime | ||
| AMD Compute | ||
| Accelerated Storage | ||
| Autoscaling GPU Clusters | ||
| Crusoe AutoClusters | ||
| Dedicated Model Inference | ||
| Elastic Scaling | ||
| Fine-Tuning Workflows | ||
| Full-Stack Observability | ||
| High-Performance Inference | ||
| Instant GPU Clusters | ||
| Kubernetes & Slurm | ||
| Managed Kubernetes | ||
| MemoryAlloy Technology | ||
| NVIDIA GPU Support | ||
| NVIDIA GPUs | ||
| Optimized Networking | ||
| Pay-As-You-Go Pricing | ||
| Self-Healing Clusters | ||
| Serverless Inference | ||
| Sustainable Energy | ||
| Zero Egress Fees | ||
| integration | ||
| Git Integration | ||
| JupyterLab Support | ||
| Multi-Cloud Support | ||
| Open-Source Model Hub | ||
| SDK Support | ||
| security | ||
| SSO Support | ||
| VPC Installs | ||
| support | ||
| 24/7 Support | ||
| Cost Tracking | ||
Pricing
Compare pricing plans and value for money
Crusoe
Contact Sales
Price Components
- NVIDIA H200 141GB HGX: $4.29/GPU-hour
- NVIDIA H100 80GB HGX: $3.9/GPU-hour
- NVIDIA A100 80GB SXM: $1.95/GPU-hour
- NVIDIA A100 80GB PCIe: $1.65/GPU-hour
- NVIDIA A100 40GB PCIe: $1.45/GPU-hour
Best For
ESG-focused AI teams training massive LLMs or running inference who prioritize sustainable, high-uptime GPU clusters with auto-failover.
Together AI
From $0/mo
Price Components
- GLM-5.1 Input Tokens: $1.4/1M tokens
- GLM-5.1 Output Tokens: $4.4/1M tokens
- Llama 3.3 70B: $0.88/1M tokens
- 1x H100 80GB: $3.99/hour
- 1x H200 141GB: $5.49/hour
Best For
Developers and enterprises needing fast, cost-efficient deployment and fine-tuning of open-source LLMs with flexible GPU clusters and serverless APIs.
Integrations
See which third-party services are supported
Supported Integrations
Coming Soon
Integration comparison data for Crusoe, Together AI is being collected and will be available soon.
Strengths & Limitations
Key strengths and limitations of each service
Crusoe
ESG-focused AI teams training massive LLMs or running inference who prioritize sustainable, high-uptime GPU clusters with auto-failover.
- Powers data centers with flare gas and solar for carbon-negative AI computing, slashing emissions versus grid-reliant hyperscalers.
- MemoryAlloy tech delivers 9.9x faster Time-to-First-Token and 5x inference throughput on NVIDIA H100/A100 GPUs.
- AutoClusters auto-remediate GPU failures for 99.98% uptime in elastic, Kubernetes-managed scaling from notebooks to clusters.
- Spot GPU instances and pay-per-1M-token inference offer cost savings over on-demand hyperscale pricing.
- Smaller scale (201-500 employees, Series C) limits global data center footprint versus hyperscalers like AWS or Azure.
- Reliance on stranded energy sources may constrain capacity expansion and geographic availability.
- Enterprise/reserved pricing for GB200/B200 requires custom sales outreach, lacking self-serve transparency.
Together AI
Developers and enterprises needing fast, cost-efficient deployment and fine-tuning of open-source LLMs with flexible GPU clusters and serverless APIs.
- Serverless inference with OpenAI-compatible APIs and up to 4x faster performance via custom optimizations differentiates from generic cloud providers.
- Instant self-service GPU clusters up to 64 NVIDIA H100/H200 GPUs deploy in minutes with zero egress fees and autoscaling.
- Fine-tuning for 200+ open-source models like LLaMA and Mistral using proprietary data, with dedicated $2,872/month inference options.
- Full-stack observability via Grafana dashboards and pay-as-you-go token-based pricing for cost-efficient scaling.
- Young company founded in 2022 with 51-200 employees may lack the enterprise maturity and global scale of hyperscalers like AWS.
- Focus on open-source models limits access to proprietary LLMs from providers like OpenAI or Anthropic.
- High entry for dedicated options at $2,872/month suits enterprises but may deter small teams preferring fully serverless.
Company Info
Company details and background
Crusoe
Together AI
Comparison FAQ
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