Enterprise Search Teams Gain Cloud AI Capabilities Without Infrastructure Overhead
Organizations running self-managed Elasticsearch clusters face a persistent challenge: implementing advanced semantic search requires GPU infrastructure for embedding generation and model inference, but procuring and maintaining that hardware demands significant capital investment, specialized expertise, and ongoing operational overhead. For teams committed to on-premises deployments due to data sovereignty, compliance requirements, or existing architectural investments, this creates a strategic bottleneck that delays AI adoption.
Elastic (NYSE: ESTC) has introduced a solution that addresses this infrastructure dilemma. The company announced availability of Elastic Inference Service (EIS) via Cloud Connect for self-managed Elasticsearch deployments, enabling organizations to access cloud-hosted GPU inference capabilities while keeping their core data and infrastructure on-premises.
Bridging On-Premises Architecture with Cloud-Scale AI Models
The new offering allows self-managed clusters to offload computationally intensive embedding generation and search inference tasks to Elastic Cloud’s managed GPU fleet. This architectural approach eliminates the need for organizations to purchase, deploy, or manage specialized hardware while maintaining their existing data residency posture.
Available in Elasticsearch 9.3, EIS on Cloud Connect provides immediate access to advanced models from Jina.ai, an Elastic company recognized for open-source multilingual and multimodal embeddings, rerankers, and small language models. These models power modern semantic search implementations that rely on vector embeddings to deliver contextually relevant results beyond traditional keyword matching.
The technical implementation follows a hybrid model: self-managed clusters retain their established architecture and data locality, while securely routing inference requests to Elastic’s cloud infrastructure. This design pattern allows development teams to implement sophisticated semantic search capabilities without architectural disruption or lengthy procurement cycles.
Operational Efficiency Meets Strategic Flexibility
According to Steve Kearns, general manager of Search at Elastic, the service removes complexity barriers that have historically slowed semantic search adoption among self-managed customers. “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,” Kearns stated.
The on-demand access model addresses both capital expenditure concerns and skills gaps. Organizations no longer need to forecast GPU capacity requirements, manage hardware refresh cycles, or build internal expertise in GPU cluster operations. Instead, they gain elastic access to inference capacity that scales with demand.
For enterprises navigating AI transformation while managing compliance frameworks, data governance policies, or legacy infrastructure commitments, this hybrid approach offers a pragmatic path forward. Teams can accelerate semantic search implementations without forcing binary choices between cloud migration and AI capability.
Immediate Availability for Enterprise Customers
EIS via Cloud Connect is now available for Elastic Enterprise customers running self-managed deployments on Elastic Stack 9.3. Organizations interested in evaluating the service can initiate trials through Elastic Cloud account creation.
The release represents Elastic’s broader strategy of meeting customers where they operate—acknowledging that enterprise infrastructure decisions reflect complex technical, regulatory, and organizational realities rather than simple technology preferences. By decoupling AI inference infrastructure from data storage and core cluster management, Elastic provides self-managed customers with cloud-scale capabilities without requiring wholesale architectural change.
About Elastic
Elastic (NYSE: ESTC), the Search AI Company, integrates its deep expertise in search technology with artificial intelligence to help everyone transform all of their data into answers, actions, and outcomes. Elastic’s Search AI Platform — the foundation for its search, observability, and security solutions — is used by thousands of companies, including more than 50% of the Fortune 500. Learn more at elastic.co.
Elastic and associated marks are trademarks or registered trademarks of elasticsearch BV and its subsidiaries. All other company and product names may be trademarks of their respective owners.
