HAI by HCloud vs Hugging Face: Token Pricing, Speed & Intelligence
Full comparison of HAI by HCloud and Hugging Face — live token pricing, latency, throughput, context window, strengths, weaknesses, and best use cases. Rates sourced from public APIs — verify before use.
HAI by HCloud
Japan-based inference — zero data retention, JPY pricing, TEE-secured Kimi K3
HAI is HCloud's LLM inference platform based in Japan, offering DeepSeek, GLM-5.3, Kimi K3, and Gemma 4 models with a zero data retention policy. Prices are in JPY per 1M tokens, tax included. The Kimi K3 (TEE) variant runs in a Trusted Execution Environment for confidential inference. Suited for Japanese enterprises and APAC workloads requiring data residency.
Hugging Face
Serverless Inference API — 150K+ open models including Llama, Qwen, and FLUX
Hugging Face is the central hub for open-source AI models, hosting 150K+ models across text, image, audio, and video. Its Serverless Inference API provides pay-per-request access to popular open-weight models including Llama 3.3 70B, Qwen 2.5 72B, and FLUX.1-dev — without managing infrastructure. Ideal for developers who want open-weights models without self-hosting.
Key metric comparison
—
—
—
—
—
—
—
—
—
—
—
—
Live token pricing
Strengths and weaknesses
HAI by HCloud
Hugging Face
Key differentiators
Japan-based inference with zero data retention and a TEE-secured Kimi K3 variant for confidential workloads.
The world's largest open-model hub — Serverless Inference API gives instant pay-per-request access to Llama, Qwen, FLUX, and 150K+ other open-weight models without any infrastructure setup.
Frequently asked questions
HAI by HCloud FAQs
What is HAI by HCloud?
HAI is HCloud's LLM inference API based in Japan. It offers DeepSeek V4.1 Flash, GLM-5.3, Kimi K3 (TEE), and Gemma 4 31B with a zero data retention policy and JPY pricing. See hai.hcloud.ltd/pricing for current rates.
What is the Kimi K3 TEE variant?
The Kimi K3 (TEE) variant runs inside a Trusted Execution Environment, providing hardware-level isolation for confidential inference. This is suited for workloads where prompt and response data must not be accessible to the infrastructure operator.
Does HAI retain my data?
No. HAI operates a zero data retention policy — prompts and responses are not stored after the request completes.
Hugging Face FAQs
What is the Hugging Face Inference API?
The Hugging Face Serverless Inference API provides pay-per-request access to popular open-weight models including Llama 3.3 70B, Qwen 2.5 72B, and FLUX.1-dev. No infrastructure management required — just an API key.
How much does the Hugging Face API cost?
Serverless Inference API pricing varies by model. Llama 3.3 70B and Qwen 2.5 72B are $0.59/$0.79 per 1M input/output tokens. FLUX.1-dev is $0.025 per image. A free tier is available for low-volume use. See huggingface.co/pricing for current rates.
Can I use my own fine-tuned model on Hugging Face?
Yes. You can deploy your own fine-tuned model on Hugging Face Inference Endpoints (dedicated) or upload it to the Hub for Serverless Inference if it meets the supported architecture requirements.
Provider resources
HAI by HCloud — Japan-based inference — zero data retention, JPY pricing, TEE-secured Kimi K3
HAI is HCloud's LLM inference platform based in Japan, offering DeepSeek, GLM-5.3, Kimi K3, and Gemma 4 models with a zero data retention policy. Prices are in JPY per 1M tokens, tax included. The Kimi K3 (TEE) variant runs in a Trusted Execution Environment for confidential inference. Suited for Japanese enterprises and APAC workloads requiring data residency.
Japan-based inference with zero data retention and a TEE-secured Kimi K3 variant for confidential workloads.
Hugging Face — Serverless Inference API — 150K+ open models including Llama, Qwen, and FLUX
Hugging Face is the central hub for open-source AI models, hosting 150K+ models across text, image, audio, and video. Its Serverless Inference API provides pay-per-request access to popular open-weight models including Llama 3.3 70B, Qwen 2.5 72B, and FLUX.1-dev — without managing infrastructure. Ideal for developers who want open-weights models without self-hosting.
The world's largest open-model hub — Serverless Inference API gives instant pay-per-request access to Llama, Qwen, FLUX, and 150K+ other open-weight models without any infrastructure setup.
Key strengths compared
HAI by HCloud
- Zero data retention policy — suitable for privacy-sensitive workloads
- Japan-based infrastructure — low latency for APAC users
- TEE-secured Kimi K3 variant for confidential inference
- JPY pricing excluded from USD cross-provider comparisons
- Smaller catalog vs global inference hubs
Hugging Face
- Largest open-model hub — 150K+ models across all modalities
- Serverless Inference API requires no infrastructure management
- Access to Llama, Qwen, Mistral, FLUX, and hundreds of other open-weight models
- Serverless API has cold-start latency for less popular models
- No proprietary frontier models — all hosted models are open-weight
Key weaknesses compared
HAI by HCloud
- JPY pricing excluded from USD cross-provider comparisons
- Smaller catalog vs global inference hubs
- Newer provider — limited third-party benchmark transparency
Hugging Face
- Serverless API has cold-start latency for less popular models
- No proprietary frontier models — all hosted models are open-weight
- Rate limits on free tier; dedicated endpoints required for production SLAs
- Pricing varies by model and can be less predictable than fixed-rate APIs
Provider category context
HAI by HCloud is a inference api, founded in 2024. Hugging Face is a open source host, founded in 2016. The category difference means these providers serve partially overlapping use cases — compare the model lists and pricing tables above to find the best fit for your specific workload.
How to choose between them
Choose HAI by HCloud if you need Privacy-sensitive workloads. Choose Hugging Face if you need Open-model inference. For high-volume production workloads, run a cost comparison using the token pricing table above with your actual prompt/completion token ratio — the cheapest provider depends heavily on your input-to-output token ratio.