TensorDock vs Together AI: Pricing, Specs, Strengths & Weaknesses
Side-by-side comparison of GPU compute pricing, regions, billing models, and strengths for TensorDock and Together AI. Rates sourced from public APIs — verify current availability and billing terms before purchasing.
Overview
Strengths & Best For
TensorDock is a GPU cloud marketplace connecting users with independent hosts across 100+ global locations. 45+ GPU models from consumer RTX cards to HGX H100 SXM5, including H100 SXM5, A100 SXM4, and RTX 4090. Pay-per-second billing with no minimum commitments, no waitlists, and no quotas. Hosts compete on price, keeping rates among the lowest available. KVM virtualization with root access and dedicated GPUs. Windows 10 support. Voltage Park (merged with Lightning AI) is a TensorDock partner. Customers include Florida State University, airgpu, ELBO, and Creavite.
- H100 SXM — competitive H100 pricing
- A100 SXM and RTX 4090 at competitive marketplace rates
- Pay-per-second billing with no minimum commitment
- 45+ GPU models from consumer to H100
- 100+ global locations across 20+ countries
- No waitlists or quotas — low minimum deposit to start
- Marketplace model — hardware quality varies by host
- No enterprise SLAs or uptime guarantees
- Limited support for complex distributed training
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Together AI is a full-stack AI platform combining a high-throughput inference API (200+ open-source models), dedicated GPU clusters (H100 SXM, A100), and a managed fine-tuning service. Together MoE optimization cuts costs on large mixture-of-experts models. Function calling and JSON mode across all major models. Fine-tuning: LoRA and full fine-tune for Llama, Mistral, and Qwen. GPU cloud: H100 SXM and A100 clusters for large-scale training. Check api.together.ai/models for current per-token rates.
- 200+ open-source models — wide inference API catalog
- Llama 4 Scout — competitive frontier-open pricing
- Together MoE optimization reduces cost on large MoE models
- Managed fine-tuning: LoRA and full fine-tune for Llama, Mistral, Qwen
- Function calling and JSON mode across all major models
- GPU cloud + inference API from one vendor
- GPU cloud side less polished than pure-play providers like CoreWeave
- Inference API latency trails Fireworks and Cerebras on speed benchmarks
- Enterprise SLAs require custom contracts — not self-serve
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Key differences
TensorDock
H100 SXM, A100 SXM, RTX 4090 — 45 GPU types, 100+ locations
Together AI
Full-stack AI platform — 200+ models, GPU cloud, fine-tuning, and Together MoE optimization