Canopy Wave vs Packet AI: Pricing, Specs, Strengths & Weaknesses
Side-by-side comparison of GPU compute pricing, regions, billing models, and strengths for Canopy Wave and Packet AI. 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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Packet AI offers dedicated single-tenant and dynamic scheduler-managed shared GPU instances for AI workloads. Both deployment options are available with on-demand hourly and monthly billing. Packet AI also provides Token Factory, a managed LLM inference API with an OpenAI-compatible endpoint for teams that want to serve models without operating their own inference stack.
- Dedicated and dynamic GPU instances
- Hourly and monthly billing
- Managed OpenAI-compatible LLM inference API
- Single-tenant deployment option
- Smaller ecosystem than hyperscale clouds
- Some GPU configurations are region-specific
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Live GPU Pricing
Region Coverage
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Provider pages
Key differences
Canopy Wave
Merging with SAIHEAT — H200, H100, serverless inference with DeepSeek V4 Pro, Kimi K3
Packet AI
Dedicated and dynamic GPU instances with managed LLM inference