GPU cloud comparison · 2026
Salad vs Together AI
Together AI wins on 3 of 5 key metrics — but the right choice depends on your workload.
Salad
Distributed inference cloud — RTX 3090/4090 from $0.03/h
from $0.03/h
★★★★☆ 3.9 / 5 (423 reviews)
Try Salad →VS
Overall Winner
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
Salad
Together AI
Starting Price Lower hourly rate
from $0.03/h
from $1.49/h
Overall Rating User rating
3.9 / 5
4.4 / 5
GPU Types Variety
4 types
4 types
Max VRAM Largest available
24 GB
141 GB
Locations Regions covered
Global (distributed)
US, EU
Wins out of 5
2
3
GPU Availability
Salad
RTX 3090RTX 4090RTX 3080RTX 3070
VRAM: 8–24 GB · Locations: Global (distributed)
Together AI
H100H200A100 80GBL40S
VRAM: 48–141 GB · Locations: US, EU
Pros & Cons
Salad
Pros
- Absurdly cheap — RTX 3090 from $0.03/h
- Massive horizontal scale (1000+ nodes)
- Auto-fleet management for inference
- No data-egress charges
Cons
- Distributed = no persistent storage
- Not suitable for training
- Latency varies by node geography
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?
Choose Salad if…
- You need GPU compute for Stateless inference
- You need GPU compute for Stable Diffusion bulk generation
- You need GPU compute for Embedding generation
- You need GPU compute for Cost-sensitive batch jobs
- Lower price is your top priority (from $0.03/h vs from $1.49/h)
- You want more GPU variety (4 vs 4 types)
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 3.9)