HAI by HCloud vs Voyage AI: Token Pricing, Speed & Intelligence
Full comparison of HAI by HCloud and Voyage AI — 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.
Voyage AI
Voyage 3.5 — strong MTEB retrieval quality for RAG and semantic search
Voyage AI specialises in embedding and reranking models for retrieval-augmented generation (RAG) and semantic search. Voyage 3.5 and its variants rank highly on the MTEB leaderboard for retrieval quality. The 2026 lineup adds Voyage 3.5 Lite (cost-efficient), Voyage Code 3 (code-optimised embeddings), and Voyage Multimodal 3 (image + text embeddings). Voyage AI is the default embedding provider for Anthropic's Claude documentation and many production RAG systems. Check voyageai.com for current rates.
Key metric comparison
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Live token pricing
Strengths and weaknesses
HAI by HCloud
Voyage AI
Key differentiators
Japan-based inference with zero data retention and a TEE-secured Kimi K3 variant for confidential workloads.
Voyage 3.5 Lite delivers strong MTEB retrieval quality at cost-efficient pricing — with specialised variants for code and multimodal content.
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.
Voyage AI FAQs
What is Voyage AI used for?
Voyage AI provides embedding and reranking models for RAG pipelines, semantic search, and document retrieval. It does not offer chat or text generation models — it is purpose-built for the retrieval layer of AI applications.
How does Voyage AI compare to OpenAI embeddings?
Voyage 3.5 consistently scores well on MTEB benchmarks relative to OpenAI text-embedding-3-large, often at lower per-token cost. Check voyageai.com and openai.com/api/pricing for current rates to compare.
What is Voyage Code 3?
Voyage Code 3 is a code-optimised embedding model designed for code search and retrieval. It outperforms general-purpose embeddings on code-related retrieval tasks, making it the preferred choice for code search applications.
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.
Voyage AI — Voyage 3.5 — strong MTEB retrieval quality for RAG and semantic search
Voyage AI specialises in embedding and reranking models for retrieval-augmented generation (RAG) and semantic search. Voyage 3.5 and its variants rank highly on the MTEB leaderboard for retrieval quality. The 2026 lineup adds Voyage 3.5 Lite (cost-efficient), Voyage Code 3 (code-optimised embeddings), and Voyage Multimodal 3 (image + text embeddings). Voyage AI is the default embedding provider for Anthropic's Claude documentation and many production RAG systems. Check voyageai.com for current rates.
Voyage 3.5 Lite delivers strong MTEB retrieval quality at cost-efficient pricing — with specialised variants for code and multimodal content.
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
Voyage AI
- Voyage 3.5 ranks highly on MTEB retrieval benchmarks
- Voyage 3.5 Lite: cost-efficient embedding option
- Voyage Code 3 for code-optimised embeddings
- Embeddings and reranking only — no chat or generation models
- Smaller ecosystem than OpenAI
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
Voyage AI
- Embeddings and reranking only — no chat or generation models
- Smaller ecosystem than OpenAI
- No open-weight models
Provider category context
HAI by HCloud is a inference api, founded in 2024. Voyage AI is a inference api, founded in 2023. Both are inference api providers — the comparison is primarily about pricing, model selection, and feature differentiation within the same tier.
How to choose between them
Both HAI by HCloud and Voyage AI host open-weight models. The key differentiators are latency, throughput, and which specific model versions each provider offers. Check the speed metrics above — inference API providers often differ significantly on tokens-per-second for the same model. Pricing is typically competitive between them; availability of specific model versions (e.g., Llama 3.1 405B, DeepSeek V3) may be the deciding factor.