By Running separate tools for search, observability, APM, and business intelligence at AI scale creates compounding data tax, multiplied security exposure, and economics that break down fast. As AI workloads move from pilot to production, the fragmented stack is no longer a manageable trade-off — it is an operational liability.
In this interview on TFiR, Bianca Lewis, Executive Director at OpenSearch Software Foundation, walks through what the 2026 Linux Foundation Open Data Infrastructure Report found, why OpenSearch repositioned as an AI data infrastructure layer, and what the doubling of production adoption signals for enterprise teams evaluating platforms now.
Guest: Bianca Lewis, Executive Director at OpenSearch Software Foundation
Show: TFiR
Here is what every platform engineer and AI infrastructure team needs to know.
Technical Deep Dive
Q: Why is OpenSearch adoption surging in 2026?
Bianca Lewis, Executive Director at OpenSearch Software Foundation, points directly to AI workload adoption as the primary driver. The OpenSearch Software Foundation repositioned the project as an AI data infrastructure layer rather than a collection of separate platform use cases — search, observability, APM, and business intelligence — and that shift in messaging aligned with exactly what enterprises began demanding as AI workloads scaled. The 2026 Linux Foundation Open Data Infrastructure Report found that 83% of surveyed organizations are either running or plan to run AI workloads, a figure that rises above 90% for large enterprises.
“The growth rate is really fueled by the adoption of AI workloads.”
Bianca Lewis, Executive Director, OpenSearch Software Foundation
Q: What is the cost of running siloed data tools at AI scale?
Lewis describes operating separate tools for each workload type at AI scale as a data tax, a security risk, and an economics problem that becomes unmanageable. Executing hundreds of thousands of queries per second across siloed platforms compounds latency, widens the attack surface, and drives up total cost of ownership in ways that are difficult to justify as workload volume grows. Consolidating onto a single platform eliminates those failure modes.
“At AI scale, executing hundreds of thousands of queries a second and working in those silos is a massive data tax, security risk, and unmanageable on scale and economics.”
Bianca Lewis, Executive Director, OpenSearch Software Foundation
Q: How do enterprises evaluate AI data infrastructure platforms today?
The 2026 Linux Foundation Open Data Infrastructure Report found that 78% of organizations prioritize flexibility — specifically, the ability of one tool to handle more than one workload type — as their top evaluation criterion. Total cost of ownership and the absence of vendor lock-in follow closely, with nearly 80% treating cloud-provider independence as a strategic priority rather than a nice-to-have. Lewis notes this is a deliberate decision, not a preference.
“It was really important to not have any vendor lock-in or independence from every cloud provider, and this was driven as a strategic decision and priority, not just a nice-to-have.”
Bianca Lewis, Executive Director, OpenSearch Software Foundation
Q: What does the jump from 19% to 36% production adoption actually mean?
Lewis highlights that production adoption of OpenSearch nearly doubled, moving from approximately 19% to 36% of workloads running in production. What makes the figure more significant is that the number of OpenSearch clusters currently in production is matched by an equal number still in proof-of-concept, testing, or non-production phases. That parity did not exist a few years ago, and it indicates the next production wave is already queued.
“The pipeline of growth coming seems to be just as big.”
Bianca Lewis, Executive Director, OpenSearch Software Foundation
Q: How large is the untapped OpenSearch market among organizations not yet using it?
The report found that 81% of respondents currently not using OpenSearch said they are willing to evaluate it for their AI data infrastructure needs. Lewis frames this as confirmation that momentum within the current user base is significant, but the addressable market for new adoption is comparably large. The combination of a strong existing pipeline and a willing non-user segment positions OpenSearch for continued compounding growth.
“As much momentum as OpenSearch has got, I think the real innovation and Golden Age is still ahead of us.”
Bianca Lewis, Executive Director, OpenSearch Software Foundation
Resources & Documentation
- OpenSearch Software Foundation, official project site, documentation, and community resources for the OpenSearch platform
- Linux Foundation 2026 Open Data Infrastructure Report, industry research covering AI workload adoption and data infrastructure evaluation criteria (opensearch.org or linuxfoundation.org)
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👇 Click to Read Full Raw Transcript
Swapnil Bhartiya: I talk about plans, I just want to talk about these numbers. As you rightly mentioned, it’s almost like 140% in a year. And the contributions, you know, contributors also grew almost 31% as you rightly mentioned, of course, neutral umbrella of Linux foundation backing by some of the biggest players that do give some confidence. But what is fueling that momentum? Because also OpenSearch came at the right time with the whole suddenly the explosion of AI as well. So it was in the right place at the right time, under the right umbrella as well. So talk about what is fueling this momentum swap.
Bianca Lewis: That’s an interesting question which was I think largely answered by the research project that we just mentioned now is that I think that the growth rate is really fueled by the adoption of AI workloads. Now, about a year ago we started to speak in different terms. We started to speak not in terms of platform use cases, not an observability use case and a search use case and a use case and a business intelligence use case or an APM use case. We started to talk about at AI scale, we executing hundreds of thousands of queries a second working in those silos is a massive data tax security risk and unmanageable on scale and economics. And we consolidated the position of OpenSearch as, as an AI data infrastructure layer. And I think that that message directly impacted this wave of growth. Now, because what the research found was that 83% of all organizations that were surveyed, and that was almost 300 different organizations are either running or plan to run AI workloads. That figure goes to more than 90% if you go to large enterprises. Now that by definition is going to require that data infrastructure layer which is feeding into exactly what OpenSearch does when companies making decisions of what platform can we unify our AI workloads on. It was really interesting to hear that 78% of organizations were evaluating data infrastructure tools based on flexibility, where one tool can do more than one thing, so they can unify these use cases together, as the messaging goes. But not far behind that there were the usual suspects, right? Total cost of ownership, again, the open platform, which is driving that, the almost 80%. It was really important to not have any vendor lock in or independent every cloud provider. And this was driven as a strategic decision and priority, not just a one to have. So all of this is really fueling into the growth of OpenSearch today, which positions us nicely, but it also positions OpenSearch with great responsibility that we make the project innovative and sustainable for the community. To use and enjoy.
Swapnil Bhartiya: And since you quoted of course the research, you know of course UN Linux foundation research just released the 2026 Open Data Info report as well. Talk a bit about what was the most interesting trends that you noticed in that report that even you were like wow this is exciting.
Bianca Lewis: What I found really exciting and we spoke about this that the awareness of OpenSearch is close to 90% today. However, production adoption has increased from around 19% to 36% of workloads into production it’s nearly doubled. But the amazing figure about that swapno is that the amount of open search clusters in production are matched by the amount of open source clusters who are still in POC or testing phases or non production phases, which was not the case just a couple of years ago. So the growth has been massive, but the pipeline of growth coming seems to be just as big. So I think the future is extremely exciting and extremely bright. And in the survey it actually highlighted that 81% of respondents who are today not using OpenSearch are really happy and willing and want to evaluate OpenSearch for the AI data infrastructure needs. So as much momentum as OpenSearch has got, I think that the real innovation in Golden Age is still ahead of us.
