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Confidential AI vs Loops

A side-by-side comparison built from both listings. Blank cells mean the company has not given that detail; we never guess.

Confidential AI compared with Loops
DetailConfidential AI logo Confidential AILoops logo Loops
What it doesPrivate AI inference and confidential GPU computing infrastructure Python SDK for LoRA fine-tuning with supervised fine-tuning and reinforcement learning
StageNot given Not given
FoundedNot given Not given
Based inUS Not given
Pricing modelPaid Not given
PricingConfidential Inference: DeepSeek V4.1 Flash $0.30 input and $1.20 output per 1M tokens; Kimi K3 $3.00 input and $15.00 output per 1M tokens; GLM 5.3 $1.40 input and $4.40 output per 1M tokens; Qwen3.8 27B $0.30 input and $1.80 output per 1M tokens. Confidential VMs: B300 at $7.50 per GPU-hour, B200 Not given
Key features
  • OpenAI-compatible endpoint for private LLM inference
  • Hardware-encrypted VMs on Intel TDX, AMD SEV-SNP and NVIDIA GPUs
  • Client attestation verification before sending requests
  • Support for multiple open-weight models including DeepSeek, Kimi, GLM, Qwen
  • Confidential Kubernetes for data center scale deployments
  • Confidential training and fine-tuning on licensed stack
  • Root access over SSH to confidential VMs
  • Encrypted GPU communications with NVLink encryption on B200 and B300
  • Confidential agents runtime support
  • Prompt caching with cached input billing
  • 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
What makes it different
  • Prompts and model weights verified by clients with cryptographic attestation before any data is sent
  • Operator-blind inference: data remains unreadable to the company and hosting provider
  • Flexible deployment: choice of hosted cloud, rented VMs, or licensed stack on own infrastructure
  • Open-weight models only, no closed-source frontier models
  • Full cryptographic verification stack including open-source tools
  • 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
Community votes00
DR (Domain Rating by Ahrefs)14 +2 (up) since last check77 no change since last check
Trust Flow (Majestic)713
Citation Flow (Majestic)2643
Referring domains (Majestic)36550

3 details filled in for both companies.

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