Elastic has completed the acquisition of Jina AI, a pioneer in open source multimodal and multilingual embeddings, reranker, and small language models.
The acquisition deepens Elastic’s capabilities in vector search, retrieval-augmented generation (RAG), and context engineering, further strengthening the company’s position as the leading Search AI Platform for developers and enterprises. Elastic’s addition of Jina AI demonstrates its continued commitment to delivering open, accessible, and production-ready Search AI at scale.
“Search is the foundation of generative AI,” said Ash Kulkarni, CEO, Elastic. “Jina AI’s team and technology bring cutting-edge models into the Elastic ecosystem, making our platform even more powerful for context engineering. Together, we are expanding what developers and enterprises can achieve with search-powered AI, while staying true to our commitment to openness and accessibility.”
The acquisition of Jina AI broadens Elastic’s leadership in relevance for unstructured data by adding dense vector, multilingual and multimodal embeddings models that process both text and images, complementing Elastic’s ELSER model. It also adds advanced rerankers that strengthen retrieval quality for visual and long-context multilingual documents, along with specialized small language models for grounding (such as HTML-to-Markdown conversion). These capabilities deepen Elastic’s strength in relevance, enabling developers to build and deliver higher-quality context to generative AI systems. The acquisition also expands Elastic’s team of AI researchers to further accelerate the company’s model innovation.
“Our mission at Jina AI has been to build search foundation models that push the boundaries of retrieval relevance for AI,” said Han Xiao, former CEO of Jina AI and newly appointed VP of AI at Elastic. “Joining Elastic allows us to scale that mission globally, bringing advanced models directly into real-world applications.”
Elastic will continue Jina AI’s practice of releasing models on Hugging Face and publishing academic research. For enterprise use, these models will be available through the Elastic Inference Service (EIS) on Elastic Cloud, enabling customers to run embeddings and rerankers natively alongside Elastic’s vector database.
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