Elastic
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Elastic has 2 source-linked AI deployments documented in AIUseCaseHub, across 1 industry and 2 countries.
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Hyperscaler mix
See whether Elastic's cases are powered by Microsoft, AWS, GCP, or multiple providers.
How Elastic builds AI
Build / Buy / Compose across this company's documented cases
2 of 2 cases classified (100%) · Compare all use-case types
Use case portfolio
Use case types at Elastic
RAG infrastructure leads with 1 of 2 documented cases; 2 distinct types appear across the visible portfolio.
Reported outcomes
1 case reports measurable results
−20%
Cost savings
median · 1 metric
−25%
Sustainability & resources
median · 1 metric
Medians of results published in Elastic cases, normalized for comparability. See all benchmarks →
Technology snapshot
What Elastic uses across visible cases
AI Agents appears in 1 of 2 indexed cases; 9 named technologies are mentioned, led by .NET 6.0.
All Use Cases (2)
Elastic re-architects Elasticsearch offering on Google Cloud with Gemini Enterprise Agent Platform integration
Elastic fundamentally re-architected its Elastic Cloud offering to Elastic Cloud Serverless on Google Cloud, transforming its ability to provide lightning-fast search and AI capabilities at scale while eliminating operational complexity for customers and significantly improving its own software delivery performance through DORA practices.The solution uses Google Compute Engine, Google Kubernetes Engine, and Cloud Storage, and integrates Elastic vector search with Gemini Enterprise Agent Platform UI and SDK for prompt grounding.
Elastic Streamlines Enterprise AI Agent Development with Plug-and-Play Architecture
Elastic, a global search company, partnered with Microsoft to launch the Semantic Kernel Elasticsearch Vector Store Connector, enabling seamless integration between Microsoft Semantic Kernel, Azure OpenAI, and Elasticsearch for building enterprise AI agents.The collaboration specifically targets simplification of Retrieval-Augmented Generation (RAG) applications where contextual responses from LLMs are driven by data in a vector store.A demo use case is provided involving hotel data; questions about hotels submitted by users are processed by generating embeddings, querying the Elasticsearch vector store, and forming context-rich prompts for Azure OpenAI.The connector is optimized for .NET developers, allowing them to leverage high-level abstractions for vector storage, search, and prompt design with little code overhead.A detailed technical workflow includes adding NuGet packages, defining a domain model schema, initializing Semantic Kernel, registering AI services and vector storage, and ingesting demo data.The Semantic Kernel engine orchestrates embeddings generation, Elasticsearch search, and prompt injection, all in a modular setup which could easily enable swap-in of alternative AI or data storage services for production scenarios.The RAG pattern illustrated ensures relevant grounding for generative answers, with data and technology choices clearly articulated.The solution addresses pain points in scaling AI-powered enterprise workflows, promoting flexibility and reduced complexity for real-world deployments.
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