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.
- 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 — no currency conversion risk for Japanese customers
- JPY pricing excluded from USD cross-provider comparisons
- Smaller catalog vs global inference hubs
- Newer provider — limited third-party benchmark transparency
HAI by HCloud Models
| Model | Modality | Tier | Price | Cached /1M | TTFT | Throughput | Context | Intel. | Caps | |
|---|---|---|---|---|---|---|---|---|---|---|
Gemma 4 31B Gemma 4 | Chat | efficient | ¥55.00/ ¥125.00 in / out per 1M · Priced in JPY (per 10k tokens native) EST Oct 2026 | — | — | — | 131K | — | ||
DeepSeek V4.1 Flash 1017 DeepSeek | Chat | efficient | ¥60.00/ ¥240.00 in / out per 1M · Priced in JPY (per 10k tokens native) EST Oct 2026 | $5.00 | — | — | 131K | — | ||
GLM-5.3 GLM-5.3 | Chat | advanced | ¥280.00/ ¥880.00 in / out per 1M · Priced in JPY (per 10k tokens native) EST Oct 2026 | $55.00 | — | — | 131K | — | ||
Kimi K3 (TEE) Kimi | Chat | advanced | ¥585.00/ ¥2910.00 in / out per 1M · Priced in JPY (per 10k tokens native) EST Oct 2026 | $85.00 | — | — | 131K | — |
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HAI by HCloud is an inference API provider that hosts open-weight and third-party large language models. 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. Common use cases include Privacy-sensitive workloads, APAC / Japan deployments, Confidential inference. Headquartered in Japan, founded 2024. All models are accessible via a REST API compatible with standard OpenAI-style request formats, enabling drop-in integration with most LLM frameworks and orchestration tools.
HAI by HCloud vs other LLM providers
HAI by HCloud competes with OpenAI, Anthropic, Google, Groq, Together AI, Mistral, Cohere, and other inference API providers across dimensions of price, throughput, latency, context window, and model intelligence. Japan-based inference with zero data retention and a TEE-secured Kimi K3 variant for confidential workloads. Use the LLM provider comparison tool to see HAI by HCloud token pricing, latency, and throughput side-by-side with any other provider. The full LLM pricing table shows all providers ranked by input token cost, output token cost, and throughput in a single sortable view.
Why choose HAI by HCloud?
HAI by HCloud stands out because: Japan-based inference with zero data retention and a TEE-secured Kimi K3 variant for confidential workloads.. The API currently offers 4 models, spanning 2 advanced, 2 efficient tiers. For teams running high-volume inference workloads, prompt caching and batch API endpoints can reduce effective input token costs by 50–90% — check the context window cost guide for a full breakdown of caching economics.
Understanding HAI by HCloud token pricing
HAI by HCloud charges separately for input (prompt) and output (completion) tokens, priced per 1M tokens in USD. Output tokens are typically 3–5× more expensive than input tokens due to the compute cost of autoregressive generation. Prices shown are sourced from HAI by HCloud's public pricing page and updated every 15 minutes. Need help estimating your spend? Read the LLM API cost calculator guide — it covers tokens, context windows, prompt caching, and batch discounts with worked examples for RAG, chat history, and document processing workloads.
HAI by HCloud context window and model capabilities
Context window size directly affects both capability and cost — every token in the context window is charged as an input token. HAI by HCloud's largest context window is 131K tokens. For RAG and document processing workloads, longer context windows enable richer retrieval but increase per-call costs proportionally. Prompt caching — where supported — stores the KV state of repeated prefixes and charges 75–90% less for cache hits, making it the most impactful cost optimization for applications with consistent system prompts or retrieved documents. See the LLM context window cost guide for a full analysis of how context length affects your API bill.
Self-hosted vs managed inference: when HAI by HCloud makes sense
Managed inference APIs like HAI by HCloud eliminate infrastructure overhead — no GPU provisioning, driver management, or model serving stack to maintain. The trade-off is cost at scale: a single H100 at ~$2.50/hr can serve ~500K tokens/min of Llama 3.3 70B, which at HAI by HCloud API rates would cost significantly more per token. The break-even point depends on your request volume, latency requirements, and engineering capacity. For teams processing fewer than ~10M tokens/day, managed APIs are almost always cheaper when total cost of ownership is considered. Above that threshold, self-hosted inference on rented GPU compute typically wins on unit economics. Read the cheapest GPU cloud guide for a full break-even analysis. Historical HAI by HCloud token price data is available in the LLM price history charts.
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