Elastic, widely recognized as the Search AI Company, has announced the availability of its Elastic Inference Service (EIS) through Cloud Connect for self-managed Elasticsearch deployments. With this latest advancement, organizations can now tap into powerful, cloud-hosted inference capabilities on demand, without the burden of building or maintaining costly GPU infrastructure. At the same time, businesses can continue to keep their core systems and sensitive data securely on-premises.
This launch comes as modern enterprises increasingly rely on semantic search to deliver smarter, more accurate results. Semantic search depends heavily on vector embeddings, which help search engines understand the meaning behind queries rather than simply matching keywords. However, generating embeddings and running reranking models typically requires significant GPU resources, making it challenging for self-managed customers to scale efficiently.
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Now, with Elastic Inference Service available in Elasticsearch 9.3, Elastic is making it far easier for self-managed users to access GPU-based embedding and reranking models. Instead of investing in complex infrastructure, teams can seamlessly offload embedding generation and search inference to Elastic Cloud’s managed GPU fleet. As a result, organizations can implement advanced semantic search faster while avoiding operational overhead.
In addition, users gain immediate access to models developed by Jina.ai, an Elastic company known for its leadership in open-source multilingual and multimodal embeddings, rerankers, and small language models. This integration ensures customers can take advantage of cutting-edge AI models to enhance search quality across diverse languages and content formats.
Importantly, Elastic designed this service to fit smoothly into existing architectures. Self-managed clusters can remain unchanged, allowing organizations to maintain control over their infrastructure and data location. Meanwhile, Elastic Cloud handles the heavy lifting of AI-powered inference, delivering a secure and efficient hybrid approach.
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“With Elastic Inference Service via Cloud Connect, we’re making it easier for self-managed customers to adopt semantic search without taking on the complexity of GPU infrastructure,” said Steve Kearns, general manager, Search at Elastic. “With a single setup, self-managed customers can access a range of cloud services from automated diagnostics to fast AI inference, all while keeping their data on-premises.”
Ultimately, this move strengthens Elastic’s position in the rapidly evolving Search AI landscape. By bridging the gap between on-premises deployments and cloud-based AI acceleration, Elastic is enabling more organizations to modernize their search experiences with minimal disruption. As semantic search continues to become essential for digital businesses, Elastic’s new inference service offers a practical path toward faster innovation, improved relevance, and scalable AI-driven search performance.
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