Elastic announced a new five-year strategic collaboration agreement (SCA) with Amazon Web Services (AWS).
The agreement reflects a shared commitment to help accelerate organizations’ transition into AI-native enterprises through joint product integrations and go-to-market (GTM) initiatives that help customers build generative AI-based applications faster while reducing complexity.
Building on a foundation of strong collaboration, Elastic and AWS will continue to invest in technical integrations designed to help customers drive their AI innovation. The integrations of Elastic’s Search AI Platform and AWS services will empower customers to build intelligent, scalable, and secure applications with unprecedented speed and flexibility, and will enable them to,
- Leverage Generative AI features across Elastic solutions using high-performing foundation models through Amazon Bedrock.
- Unlock support for migrating Elasticsearch workloads from on-premise data centers to Elastic Cloud on AWS.
- Benefit from ongoing cost efficiencies when using Elastic Cloud Serverless on AWS.
- Accelerate agentic AI as a result of the Elastic and AWS collaboration on Model Context Protocol (MCP) and agent-to-agent interoperability.
- Build solutions that protect data across all layers for customers in highly regulated industries, including the Public Sector and Financial Services
“As the speed of generative AI adoption accelerates, search has become increasingly relevant,” said Ash Kulkarni, chief executive officer at Elastic. “Our collaboration with AWS and integration with Amazon Bedrock brings the power of search directly to generative AI for a host of use cases, including cybersecurity and observability. Together, we’re enabling developers to build intelligent, context-aware applications that leverage their own data securely and at scale.”
"Together with Elastic, we're helping customers transform how they leverage data and AI to drive innovation," said Ruba Borno, vice president, Specialists and Partners at AWS. "This strategic collaboration delivers particular value for highly regulated industries requiring robust data protection, while our shared commitment to standards like Model Context Protocols enables seamless agent-to-agent interactions. Available through AWS Marketplace, customers will be able to quickly deploy solutions that combine Elastic's powerful search capabilities with Amazon Bedrock on the secure, global AWS infrastructure, helping them build compliant, intelligent applications that accelerate their AI journey."
The SCA will build on the long-standing collaboration between AWS and Elastic. For example, Elastic AI Assistant, Attack Discovery, Automatic Import, Automatic Migration, Automatic Troubleshoot, and AI Playground integrate with Large Language Models through Amazon Bedrock. These integrations help customers accelerate root cause analysis, synthesize complex signals into actionable insights, automate data onboarding in minutes, and simplify migration. With natural language and RAG-powered workflows, teams can interact with data more intuitively and drive faster, smarter decisions.
Customers, including Generis and BigID, benefit from Elastic’s work with AWS:
"The strength of the Elastic and AWS partnership has been fundamental to Generis’s mission of delivering secure, compliant, and intelligent solutions for clients in highly regulated industries," said Mariusz Pala, CTO at Generis. "By deploying Elastic on AWS, we’ve reduced average search times by 1000% and cut the time to produce complex, compliance-driven documents from two weeks to just two days, providing our clients real-time insights while upholding the highest standards of data integrity and control.”
"Leveraging Elastic Cloud on AWS has been transformative for BigID. We've achieved a 120x acceleration in query performance, enabling real-time data insights that were previously unattainable," said Avior Malkukian, Head of DevOps at BigID. "The scalability and flexibility of Elastic Cloud on AWS allow us to efficiently manage vast and complex data landscapes, ensuring our customers can swiftly discover and protect their sensitive information. Elastic Cloud on AWS is a powerful combination that allows us to deliver innovative features, reduce operational costs, and maintain our leadership in data security and compliance."
This SCA comes on the heels of Elastic’s recent recognition within the AWS Partner Network. In December 2024, Elastic was named the AWS Global Generative AI Infrastructure and Data Partner of the Year. It was among the first 15 AWS software partners recognized at the launch of the AWS Generative AI Competency. In recent months, Elastic was also recognized by AWS for its work in the public sector, receiving AWS competency designations for both the Government (February) and Education (May) sectors.
The Latest
In MEAN TIME TO INSIGHT Episode 14, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud network observability...
While companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending ...
Telecommunications is expanding at an unprecedented pace ... But progress brings complexity. As WanAware's 2025 Telecom Observability Benchmark Report reveals, many operators are discovering that modernization requires more than physical build outs and CapEx — it also demands the tools and insights to manage, secure, and optimize this fast-growing infrastructure in real time ...
As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...
Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...
AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...
Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...
A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...
IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...
A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...