Independent comparison Updated April 2026 20 GPU providers tested Real hourly pricing

GPU cloud comparison · 2026

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TensorDock vs Together AI

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TensorDock wins on 3 of 5 key metrics — but the right choice depends on your workload.

Overall Winner
T
TensorDock
Marketplace GPU cloud — RTX 4090 from $0.21/h, H100 from $1.99/h
from $0.21/h
★★★★☆ 4.2 / 5 (167 reviews)
Try TensorDock →
VS
T
Together AI
Inference-first GPU cloud — H100/H200 with optimized serving stacks
from $1.49/h
★★★★☆ 4.4 / 5 (521 reviews)
Try Together AI →

Head-to-Head Comparison

T TensorDock
T Together AI
Starting Price Lower hourly rate
from $0.21/h
from $1.49/h
Overall Rating User rating
4.2 / 5
4.4 / 5
GPU Types Variety
5 types
4 types
Max VRAM Largest available
80 GB
141 GB
Locations Regions covered
US, EU, Global
US, EU
Wins out of 5
3
2

GPU Availability

T TensorDock
RTX 4090RTX 3090A100 80GBH100L40S

VRAM: 24–80 GB · Locations: US, EU, Global

T Together AI
H100H200A100 80GBL40S

VRAM: 48–141 GB · Locations: US, EU

Pros & Cons

T TensorDock
Pros
  • Among the cheapest H100 access in 2026
  • Wide host network = better availability
  • Per-second billing for short jobs
  • Free egress saves on data-heavy workloads
Cons
  • Reliability varies by host
  • No managed cluster orchestration
  • Support is community-led
T Together AI
Pros
  • Best-in-class inference performance
  • Excellent open-source model coverage
  • Strong fine-tuning workflow
  • Token-based pricing for variable load
Cons
  • Less GPU variety than RunPod
  • Focus is inference, not raw training
  • Custom interconnects not exposed

Which Should You Choose?

T Choose TensorDock if…
  • You need GPU compute for Budget GPU rentals
  • You need GPU compute for Stable Diffusion fine-tuning
  • You need GPU compute for Short-burst training
  • You need GPU compute for Indie ML developers
  • Lower price is your top priority (from $0.21/h vs from $1.49/h)
  • You want more GPU variety (5 vs 4 types)
T Choose Together AI if…
  • You need GPU compute for High-throughput inference
  • You need GPU compute for Open-source LLM serving
  • You need GPU compute for Llama / Mistral fine-tuning
  • You need GPU compute for Production AI APIs
  • Higher user satisfaction matters (4.4 vs 4.2)