Rakuten Launches Japanese LLM for E-Commerce

Rakuten, Japan's largest e-commerce platform and a global tech conglomerate valued at over $12 billion, has officially launched a new large language model (LLM) specifically optimized for the Japanese language and tailored to power e-commerce applications across its ecosystem. The move positions Rakuten as one of the first major retail platforms in Asia to develop and deploy a proprietary LLM designed from the ground up for commercial use.

The announcement signals a broader trend of non-Western tech giants building sovereign AI models that cater to languages and markets underserved by dominant English-centric models like OpenAI's GPT-4, Google's Gemini, and Anthropic's Claude.

Key Facts at a Glance

• Rakuten has developed a proprietary LLM optimized specifically for the Japanese language
• The model is purpose-built for e-commerce applications, including product recommendations, customer service, and merchant tools
• Rakuten's ecosystem spans over 1.7 billion registered users globally across 70+ services
• The LLM leverages Rakuten's massive proprietary dataset of consumer behavior and transaction history
• Japan's AI market is projected to reach $22.4 billion by 2027, according to IDC estimates
• The model reportedly outperforms general-purpose LLMs on Japanese-language retail benchmarks

Why Rakuten Built Its Own Model Instead of Using GPT-4

Rakuten's decision to build a proprietary model rather than rely on third-party APIs from OpenAI or Google reflects a strategic calculation that many enterprise leaders are now making. General-purpose models like GPT-4 and Claude 3.5 excel at broad tasks but often struggle with domain-specific nuances — especially in non-English languages.

Japanese presents unique challenges for LLMs. The language uses 3 distinct writing systems — hiragana, katakana, and kanji — and relies heavily on context for meaning. Most leading Western models are trained predominantly on English-language data, with Japanese comprising a relatively small fraction of their training corpora.

Rakuten's model addresses this gap by training on a massive corpus of Japanese-language text, including product descriptions, customer reviews, merchant communications, and transactional data drawn from its own platform. This gives the model a significant advantage in understanding the specific vocabulary and patterns of Japanese online commerce.

E-Commerce Applications Drive the Model's Architecture

Unlike general-purpose chatbots, Rakuten's LLM is architected with specific e-commerce use cases in mind. The company has outlined several initial deployment scenarios that highlight how the model integrates directly into its retail infrastructure.

Key applications include:

• Personalized product recommendations that understand nuanced customer preferences expressed in natural Japanese
• Automated customer service agents capable of handling complex inquiries about orders, returns, and shipping
• Merchant support tools that help sellers optimize product listings, generate descriptions, and analyze market trends
• Search enhancement that interprets ambiguous or conversational queries and returns more relevant results
• Review summarization that condenses thousands of customer reviews into actionable insights for shoppers

These applications tap directly into Rakuten's core revenue engine. The company processes billions of dollars in gross merchandise value (GMV) annually, and even marginal improvements in conversion rates or customer satisfaction could translate to hundreds of millions in additional revenue.

How Rakuten's Model Compares to Competitors

Rakuten is not operating in a vacuum. Several other Japanese tech companies have launched or announced their own LLM initiatives. NEC Corporation unveiled a Japanese-language LLM with 13 billion parameters in 2023. Preferred Networks, a Tokyo-based AI startup, has also developed models targeting the Japanese market. Meanwhile, SoftBank has invested heavily in AI infrastructure and has signaled plans for its own foundational models.

What distinguishes Rakuten's approach is its tight integration with a live commercial platform. While competitors are building general-purpose Japanese LLMs, Rakuten is building a model that sits at the intersection of language understanding and commercial intent. This is a critical distinction.

Compared to Western counterparts, the strategy mirrors what Amazon has done with its own AI initiatives — leveraging proprietary retail data to build models that no competitor can easily replicate. Amazon's Rufus shopping assistant, launched in early 2024, similarly uses the company's vast product catalog and customer data to power conversational commerce. Rakuten appears to be following a parallel playbook for the Japanese market.

Japan's AI Landscape Heats Up Amid Government Support

Rakuten's LLM launch comes at a pivotal moment for Japan's AI industry. The Japanese government has made artificial intelligence a national priority, allocating over $13 billion in AI-related investments as part of its broader digital transformation agenda.

Prime Minister Fumio Kishida has repeatedly emphasized the importance of developing domestic AI capabilities, particularly in light of growing concerns about data sovereignty and reliance on American tech platforms. Japan's Ministry of Economy, Trade and Industry (METI) has established guidelines encouraging the development of Japanese-language AI models and providing subsidies for companies investing in AI research.

This government backing creates a favorable environment for companies like Rakuten. Access to subsidies, regulatory clarity, and national infrastructure investments in GPU computing clusters all reduce the cost and risk of building proprietary models.

The broader context is significant for Western observers. As countries around the world — from France to the UAE to South Korea — invest in sovereign AI capabilities, the era of a few Silicon Valley companies dominating the global LLM landscape may be drawing to a close.

What This Means for Global E-Commerce and AI Strategy

Rakuten's move carries implications that extend well beyond the Japanese market. For global e-commerce companies, it demonstrates that domain-specific, language-optimized LLMs can deliver superior performance compared to general-purpose alternatives.

This has several practical implications:

• Retailers in non-English markets may increasingly build or commission custom LLMs rather than relying on translations of English-centric models
• Data moats become even more valuable — companies with large proprietary datasets of customer behavior hold a strategic advantage in training effective commercial AI
• API dependency risks are driving enterprises toward owning their AI stack, reducing reliance on OpenAI, Google, or Anthropic
• Localization moves beyond simple translation to encompass cultural context, shopping behavior patterns, and market-specific regulations

For Western companies operating in Asian markets, Rakuten's approach serves as both a competitive challenge and a potential blueprint. Firms like Shopify, eBay, and Walmart may need to consider similar strategies for markets where English-centric models underperform.

Technical Considerations and Open Questions

Several technical details about Rakuten's model remain undisclosed. The company has not publicly revealed the model's parameter count, its training infrastructure, or whether it plans to open-source any components. These details matter significantly for assessing the model's capabilities relative to competitors.

Training a high-quality LLM requires substantial compute resources. Companies like Meta spent an estimated $100 million or more training Llama 3, and even smaller models require thousands of GPU hours. Rakuten's investment in AI infrastructure — including partnerships with cloud providers and potential use of NVIDIA's latest H100 or B200 GPUs — will be a key factor in the model's long-term competitiveness.

There are also questions about hallucination rates and accuracy in commercial contexts. In e-commerce, incorrect product information or misleading recommendations can erode customer trust and create liability issues. Rakuten will need to demonstrate that its model achieves high factual accuracy, particularly when generating product descriptions or answering customer queries about policies and pricing.

Looking Ahead: Rakuten's AI Roadmap and Industry Impact

Rakuten has indicated that the current LLM launch is just the beginning of a broader AI strategy. The company is expected to expand the model's capabilities to cover additional services within its ecosystem, including Rakuten Mobile, Rakuten Travel, and Rakuten Securities.

The timeline for these expansions has not been specified, but industry analysts expect Rakuten to roll out enhanced AI features across its platform throughout 2025. The company's Rakuten Viber messaging platform, which serves over 800 million users globally, could also become a distribution channel for AI-powered services.

For the broader AI industry, Rakuten's launch reinforces several emerging trends: the rise of domain-specific models, the growing importance of non-English language AI, and the strategic value of proprietary training data. As more companies follow this path, the competitive landscape for LLMs will become increasingly fragmented — and increasingly interesting.

The era of one-size-fits-all AI is giving way to a world of specialized, localized, and deeply integrated models. Rakuten's bet is that in e-commerce, knowing your customer's language — in every sense of the word — is the ultimate competitive advantage.