Loops vs YABANIN
A side-by-side comparison built from both listings. Blank cells mean the company has not given that detail; we never guess.
Loops compared with YABANIN
| Detail | 
Loops | 
YABANIN |
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| What it does | Python SDK for LoRA fine-tuning with supervised fine-tuning and reinforcement learning
| Self-hosted AI infrastructure with measurable returns
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| Stage | Not given
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| Founded | Not given
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| Based in | Not given
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| Pricing model | Not given
| Contact for pricing
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| Pricing | Not given
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| Key features | - 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
| - Model provider support through a single common gateway
- Usage attribution to teams, tasks, and agents
- Agent workflows in Scala for repositories and code
- Apache-2.0 open-source core with enterprise deployment option
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| What makes it different | - 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
| - Full visibility into AI costs attributed to specific teams, tasks and agents
- Self-hosted deployment keeps data and models under company control
- Model agnostic with support for multiple providers through one gateway
- Structured four-step implementation path with clear ownership and baselines
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| Community votes | 0 | 0 |
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| DR (Domain Rating by Ahrefs) | 77 ● no change since last check | Not checked yet ● no change since last check |
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| Trust Flow (Majestic) | 13 | 0 |
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| Citation Flow (Majestic) | 43 | 0 |
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| Referring domains (Majestic) | 550 | 0 |
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3 details filled in for both companies.
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