Kyndryl and Elastic announced an expanded global partnership to provide customers full-stack observability, enabling them to accelerate their ability to search, analyze and act on machine data (IT data and business data) stored across hybrid cloud, multi-cloud and edge computing environments.
Under the partnership, Kyndryl and Elastic will collaborate on creating joint solutions and delivery capabilities designed to provide deep, frictionless observability at all levels of applications, services, and infrastructure to address customer data, analytics and IT operations management challenges.
The companies will focus on delivering large-scale IT operations and AIOps capabilities to joint customers by leveraging Kyndryl’s data framework and toolkits and Elastic’s Enterprise Search, Observability, and Security solutions, enabling streamlined migrations, modernized infrastructure and tenant management, and AI development for efficient and proactive IT management.
As part of the partnership, Kyndryl and Elastic plan to collaborate to support customer needs and requirements via joint offerings and solutions across the following areas:
- IT Data Modernization – Helping organizations manage exponential storage growth and giving them the capability to search for data wherever it resides.
- IT Data Management Services for Elastic – Providing flexibility to users of Elastic by letting Kyndryl manage the entire stack infrastructure and analytics workloads for IT operations.
- Intelligent IT Analytics – Enabling actionable observability through AI/ML capabilities that deliver unified insights for proactive and efficient IT operations with technology domain-specific insights.
- Data Migration Services for Elastic – Delivering the capability to streamline migrations and deploy self-managed Elastic workloads to the hyperscalers of a customer’s choice.
Kyndryl’s global team of data management experts will also participate in the global Elastic certification program to expand their expertise in advising, implementing and managing Elastic solutions across critical IT projects and environments.
“Customers in all industries are seeking to improve their capacity to search and analyze the data stored in the cloud and on edge computing environments,” said Nicolas Sekkaki, Applications, Data & AI global practice leader for Kyndryl. “We are happy to partner with Elastic to create and bring forward a unified approach that will help customers overcome hurdles and improve their ability to access and gain insights at scale from their business data.”
“Enabling customers to gain actionable insights from their data is a key enabler of data-driven digital transformation,” said Scott Musson, VP, Worldwide Channel and Alliances at Elastic. “The combination of Kyndryl’s global expertise in managing mission-critical information systems and the proven scale and flexibility of the Elastic Search Platform provides the critical foundation to help organizations drive speed, scale, and productivity, and address their observability needs across hybrid cloud, multi-cloud and edge computing environments.”
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 ...