Canopy Wave vs Lyceum: Pricing, Specs, Strengths & Weaknesses
Side-by-side comparison of GPU compute pricing, regions, billing models, and strengths for Canopy Wave and Lyceum. Rates sourced from public APIs — verify current availability and billing terms before purchasing.
Overview
Strengths & Best For
Canopy Wave announced a merger with SAIHEAT. The platform offers HGX B300 (reserve), GB200 NVL72 (reserve), HGX B200 (reserve), HGX H200, and HGX H100 on-demand. Serverless inference covers DeepSeek V4.1-Flash, DeepSeek V4 Pro, GLM-5.2, Kimi K3, Kimi K2.6, MiniMax M3, and Qwen3.8-Flash-Next. Unlimited Token Plan available. Coding Plan for privacy-protected open model access. Zero data retention policy. Check canopywave.com for current rates.
- H200 and H100 HGX — competitive on-demand pricing
- Serverless inference: DeepSeek V4 Pro, Kimi K3, GLM-5.2, MiniMax M3
- Unlimited Token Plan — flat-rate token access
- Zero data retention policy
- HGX B300 and GB200 NVL72 available to reserve
- Merging with SAIHEAT for expanded capacity
- Merger with SAIHEAT creates uncertainty
- Smaller provider — limited scale vs hyperscalers
- B300/GB200 not yet on-demand (reserve only)
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Lyceum is an academic GPU cloud offering H100 and A100 instances with research-friendly policies, flexible on-demand pricing, and configurations tailored for AI experimentation and academic research teams. Accessible pricing and a focus on the research community make it a practical option for universities and independent researchers who need GPU compute for LLM experimentation and model training without enterprise overhead. A strong choice for academic AI teams that want affordable, flexible GPU access.
- Research-friendly
- Flexible pricing
- Academic focus
- Very small provider — limited scale
- No enterprise SLAs
- Limited documentation and support resources
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Key differences
Canopy Wave
Merging with SAIHEAT — H200, H100, serverless inference with DeepSeek V4 Pro, Kimi K3
Lyceum
GPU cloud for researchers