
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
I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...
Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...
For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...
Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...
Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...
For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...
New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...
Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...
In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ...
In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...
