SaladCloud vs Chutes
Agent-native side-by-side. Scores are curated estimates on a 0–10 scale. Lock-in: higher = worse. Prefer JTBD picks via /api/decide when choosing for a job.
| Dimension | SaladCloud | Chutes |
|---|---|---|
| Overall | 6.7 | 7.3 |
| Capability fit | 6.5 | 7.4 |
| Price / performance | 9.5 | 9.2 |
| Latency / reliability | 5.0 | 6.8 |
| Developer & agent DX | 7.0 | 7.6 |
| Privacy / data retention | 4.5 | 5.8 |
| Lock-in risk (higher = worse) | 3.5 | 4.0 |
| Maturity / track record | 6.8 | 6.2 |
| Pricing | As of 2026-10-24 (salad.com/pricing, fetched 2026-10-24): GPU classes 'from $0.015 per GPU-hour at Lowest priority'; 'Billing is per second while an instance is in the running state.' 'Allocation, image download, and con… | As of 2026-10-27 (chutes.ai/pricing, fetched twice 2026-10-27): Public TEE inference PAYG per 1M tokens, input/output (the page says per-model rates are 'pulled live from our pricing API'): gemma-4-31B-turbo $0.12/$0.37;… |
| Tagline | Promoted high on official /pricing floor ($0.015/GPU-hr). Depot interruptible ultra-cheap GPU pick… | Budget open-model overflow with a now-pinned public TEE matrix. Depot open-model production pick stays… |
SaladCloud
Promoted high on official /pricing floor ($0.015/GPU-hr). Depot interruptible ultra-cheap GPU pick; Vast/TensorDock remain marketplace peers. Prefer Nebius/RunPod/Modal when you need sticky sessions or predictable TTFT. Strengths: Extremely cheap GPUs; Huge distributed capacity; Container-native.
Strengths
- Extremely cheap GPUs
- Huge distributed capacity
- Container-native
Weaknesses
- Interruptible/priority variance
- Consumer GPU mix
- Not enterprise SLA
Chutes
Budget open-model overflow with a now-pinned public TEE matrix. Depot open-model production pick stays Fireworks; ultra-budget volume DeepInfra/Novita; use Chutes as capacity valve and re-check live rates. Strengths: Aggressive open-model inference pricing; Serverless chute deploys for custom models; Useful overflow ca
Strengths
- Aggressive open-model inference pricing
- Serverless chute deploys for custom models
- Useful overflow capacity for batch agents
Weaknesses
- Live token matrix still volatile vs Fireworks/Together
- Support/SLA thinner for enterprise
- Decentralized TEE marketplace operational risk
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