Elastic
3 documented AI deployments at Elastic, across 1 industry. Each one links to the public source it was taken from.
Public AI record
Elastic has 3 documented public AI cases on the hub, too few for a score. Customer service automation and ai development platform are common among peers but not yet documented at Elastic.
A record score appears once Elastic has 5 documented public cases. The peer comparison below already uses what is on record.
First documented case Dec 2024, latest Jul 2026. Cloud providers named: AWS, GCP, Microsoft.
Compared with peers
Against 14 other tech & comms companies on the hub.
What peers document
- Customer service automation8 of 14 peers57%
- AI development platform4 of 14 peers29%
- Workflow automation3 of 14 peers21%
- Workflow orchestration3 of 14 peers21%
Gaps against the industry
- Customer service automation: 57% of peers, not documented at Elastic yet
- AI development platform: 29% of peers, not documented at Elastic yet
Signature use cases
No peer on the hub documents rag infrastructure, search modernization or security operations.
Stack and approach vs peers
- Peer cases name Microsoft 69%, GCP 24%, AWS 13%.
- AI agents: 33% of Elastic's cases vs 56% for peers.
- Cases from the last 12 months: 67% vs 34% for peers.
Deployments
3 deployments at Elastic, newest first.
- 15 Jul 2026Tech & CommsUnited States
Elastic security operations assistant using Amazon Bedrock (Anthropic Claude 3)
Elastic · Security operations · AWS
Elastic built the Elastic AI Assistant to help security operations teams with alert triage and investigation, summarize environments, convert queries into Elastic syntax, and answer questions over large bodies of security log data using retrieval-augmented generation with Elasticsearch Relevance Engine on AWS. Users can select Anthropic Claude 3 models via Amazon Bedrock, and Elastic emphasizes privacy, security, scalability, and private connectivity while helping customers reduce incident detection and mitigation time.
Exploring
- 17 Jun 2026Tech & CommsUnited States
Elastic re-architects Elasticsearch offering on Google Cloud with Gemini Enterprise Agent Platform integration
Elastic · Search modernization · GCP
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.
Scaled Production
−20%operational expenses - 6 Dec 2024Tech & CommsGlobal
Elastic Streamlines Enterprise AI Agent Development with Plug-and-Play Architecture
Elastic · RAG infrastructure · Microsoft
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.
Agentic
What it builds
RAG infrastructure is the most common pattern in these deployments: 1.
Cloud providers named
AWS is named in 1 of these deployments.
- AWS33% of these1
- GCP33% of these1
- Microsoft33% of these1