Scale vs Loops
A side-by-side comparison built from both listings. Blank cells mean the company has not given that detail; we never guess.
Scale compared with Loops
| Detail |
Scale |
Loops |
| What it does | Reliable AI systems for critical decisions
| Python SDK for LoRA fine-tuning with supervised fine-tuning and reinforcement learning
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| Stage | Established
| Not given
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| Founded | Not given
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| Based in | US
| Not given
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| Pricing model | Contact for pricing
| Not given
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| Pricing | Not given
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| Key features | - High-quality training data sourced from contributors with 25% having advanced degrees
- Model evaluations and red-teaming
- RLHF (Reinforcement Learning from Human Feedback)
- Private benchmarks and leaderboards for AI companies
- Full-stack AI systems deployment
- Hands-on AI application building for specific use cases
| - forward_backward() operation for computing loss and accumulating gradients
- optim_step() for updating the adapter
- sample() for generating from current or saved policy
- save_state() to save and resume training state
- Tinker API compatibility
- Separate trainer and sampler resources
- Synchronous and asynchronous RL patterns
- Frozen base model weights with LoRA adapter updates
- Support for open models from Qwen, Google, DeepSeek, Moonshot AI, NVIDIA, and Z.ai
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| What makes it different | - Powers 90% of the world's leading generative AI model builders
- Humans stay in the loop throughout the AI stack
- 10 years of proven experience in AI breakthroughs
| - Managed training and sampling infrastructure, no setup required
- Tinker-compatible API for easy migration
- Ready-made SFT and RL recipes through Loops cookbook
- Support for vision-language models
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| Community votes | 0 | 0 |
| DR (Domain Rating by Ahrefs) | 80 ● no change since last check | 77 ● no change since last check |
| Trust Flow (Majestic) | 23 | 13 |
| Citation Flow (Majestic) | 53 | 43 |
| Referring domains (Majestic) | 8300 | 550 |
3 details filled in for both companies.
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