OpenSearch, Hybrid Vectors, and AI

Hey everybody, we're back in Amsterdam at the KubeCon + CloudNativeCon Europe Conference, and we're having a chat with my friend Bianca, who runs the OpenSearch Foundation. How are you doing? I'm good.

Great to be here, Mike, and great to see you again. Great to see you again as well. There's been a lot of movement and a lot of momentum around the whole foundation and the whole...

I think there's a lot more interest and maybe even newfound respect for search engines in the age of AI. But I know you have some new members that joined recently, so walk us through and give us a little update on what's going on with the foundation. Great starting point.

So let's start at the top, where the foundation's really happy that we've just announced a bunch of new members that include companies like Resolve Technologies and BigData Boutique, and Open Source Connections, which are super important. There's more that's going to be announced, so stay tuned, everyone. It's exciting times that we're living in.

Since the last time that we spoke, obviously there's been a lot of growth within the foundation, which I guess is the proof that OpenSearch as a part of your AI infrastructure layer is definitely heading in the right direction. We're on a wave of innovation now. But in terms of announcements per announcements, you will see, and I encourage all of the viewers to look it up for themselves, we just released a great case study from Atlassian, which is large scale use of OpenSearch across multiple different use cases, which plays into the narrative of a foundational layer of infrastructure that you can build different use cases, whether that be search, observability, security, FinOps, and you can build that all out in the same data layer.

That's a really interesting case study. Another one just released is Changi Airport, which, if I'm not mistaken, is the largest airport in the world. It's based in Singapore.

Yeah. And they're using OpenSearch for super interesting use cases. From search use cases, they're using it for their retail, where they want 1,000 retail outlets.

And what they're doing is they're using it to not only geolocate, but also as a recommendation engine. So I buy a pair of shoes, and it's going to recommend a pair of socks to go with the shoes, which is geolocated behind me. There we go.

I think that there is a perception that says search engines are for indexing and I'm going to surface up a bunch of text, but it's gone beyond that as a technology, and it seems it's really about surfacing the right data at the right place at the right time, and that's a big challenge, especially in the AI era, given how much data there is. So are people starting to understand exactly how to use search in that context, or what are you hearing and seeing? So let's break that question, which is a really good question, into two different parts.

Let's concentrate on the search and how we need to use that in the world of AI and what that means in the broader context, the second part of that question. In search itself, we are modernizing the search. As we spoke about last year, 2025, I think, was all about vectors.

Mm-hmm. Everything is a vector database. Now that's kind of parity.

Table stakes. Yeah. Exactly.

So you said that much more eloquently than I did. But what it is now, it's all about how do we make agentic search efficient and safe? And that modernizing on the search engine, OpenSearch is doing really interesting things.

As examples, we're creating a very powerful hybrid search. We all know it's running on GPUs, it's expensive infrastructure, it's a bottleneck, and we've got to be really efficient about our search results. So before you go and search it on vectors, and we search for brown dogs, and I get brown cats, and I get white dogs, and I get all the adjacent vectors, we combine that with the lexical search within OpenSearch, which will give the vector search the context that it needs to provide more accurate results, less hallucination, and provide a more efficient and reliable layer of RAG.

Mm-hmm. And so that, to me, sounds like the foundation for what people are calling context engineering. Yeah.

Because I have to pull that data together in a way that I expose it to the AI- Yeah ... so that it knows what to go do. Otherwise, it just might go do something in an infinite loop, right?

There we go. Essentially, what we need to be doing now, because we're scaling at not one query a second, but Bianca's right in, we are scaling at hundreds of thousands of queries a second. So what we need to do is we need to build agentic AI.

We need to let AI monitor AI and control AI at that sort of a scale. Mm-hmm. And that actually goes to the second part of your question, that now, with that infrastructure layer, we can't think about it as search being isolated in the bucket of the search project.

We have to think about search and observability and security and costs and business decisions as a whole, all as this part of the same thing. Mm-hmm. And giving each of those different necessities the right contextTo automate.

Do you think we're going to see a more convergence of different data management functions as we go along to achieve that goal? Because historically, things were always kind of treated in isolation. And if I wanted to be really honest, a lot of enterprises, well, let's just say they wouldn't get a good housekeeping seal of approval for the way they manage data.

No. You're so right. I can't tell you how many companies I've spoken to who are great on modernizing the AI infrastructure up until the point where they're realizing my data's not ready for it.

However, yeah, I think that's a critical section, is that the whole data infrastructure layer has to be re-looked at now so that it can support what's going to happen with all of the scale in AI automation now. Without that, it's kind of like, I would say, having a supercar with no safety mechanisms and no road to drive it on, but try and drive it at your own peril. Now, last time I looked, there wasn't a huge pool of data engineers out there to help me sort all this stuff out.

So will we use AI to kind of manage the data a little bit more to scale those people so we can basically use AI to make the data ready for AI? Yeah. And I think we're still in that kind of paradigm, right?

Can we use AI to do functions for the skills of people that we don't have? Which we haven't quite got to that point, at least in the senior part of the organization. And rather, what we've seen now is the skill set we do have, instead of looking to supplement the people, we're looking to taking the people that we've already got in the limited numbers and to making them superpowered to solve the problems and to supplement their abilities.

And the paradigm shift there is not to do it on an ad hoc individual basis, but much more on the organizational level, so it's coordinated. So we've heard this metaphor a million times where people say data's the new oil. But truth be told, if you've ever actually seen a barrel of oil, it's pretty useless on its own, standing there doing anything.

You have to refine it into something to make it into something that we can monetize. Is that kind of the same conversation we're having in the age of AI, where now we got to figure out how to refine the data and process it in a way that really drives some value out of it? Yes, and really interesting to think about it in terms of looking as AI is the oil.

It's necessary to run things, but by itself, it does nothing. So you're absolutely right. What's interesting, though, is the machinery using the oil will now be generating more oil because the AI is going to generate more data that has had to be used to manage the AI.

This is true. So it becomes a virtuous loop at the end of the day. There we go.

So the days of having my oil and then having my engines to run the oil and then having the driver for the engines, that linear building block is past. And what we instead have is we have a virtuous loop, a continuous loop of development, and that's going to accelerate the pace of development now. " How much time do we have today, Mike?

Okay. I'm just going to throw out a couple of examples of what keeps me up at night. There you go.

One great example is in the age of AI, people have seen this coming, and there's a whole lot of compliance regulations like CRA coming in Europe and things like this. And what does that mean in terms of data sovereignty? Nobody really knows.

And where the scales and how it's going to be implemented is going to be a challenge. Now, what does that mean? In the world of AI, data is my everything.

It's my oil. It's my assets. The world is not going to go around without my data.

So instead of putting my data into different services and having it all over the show, I have to have data sovereignty over my own data so I've got the flexibility to comply with things that can be changing very quickly. So I want to now bring my services towards my data instead of throwing my data into those services. I think the paradigm's shifting, and I see a lot of companies I'm talking to struggling with just simple decisions like that.

How quickly, where, how? And we're actually focusing very much to make OpenSearch part of that solution to give you data sovereignty then. Oddly enough, have we come full circle?

Because at least I'm old enough to remember back in the day- Yeah ... we told everybody we should bring the compute to the data. And then we spent 10 years or so pushing data into the cloud.

Into the compute, yeah. And now we're back full circle trying to bring the compute to the data again. Yeah.

The more things change, the more we realize that things stay the same. I don't think AI's going to be much different in the long run. We'll become much more productive.

The scale's going to be different, of course. There are going to be new challenges. But I think we'll always work cyclically.

There's a reason why the 1960s fashion comes back every 30 years, right? There you go. How do folks get involved with the foundation?

What's the entry point for folks? " Yeah. So thank you for asking, and it gives me the chance to showcase the OpenSearch Software Foundation, which I'm excited to do.

You can join in multiple different ways. You can do things very simply. We've got community Slacks.

You can contribute code to one of our 141 repositories. You can link to me on LinkedIn, and I will help you out personally. We've got a team just willing to talk to you.

And we've got a huge number of announcements coming in April at OpenSearchCon EU about benefits which are going to really make your life easier as an enterprise customer of OpenSearch. Stay tuned, be in touch, and I'll help you to get on board in the foundation. Sounds good.

Folks, you heard it here. If you want to join the foundation, just find Bianca on LinkedIn. It's that simple.

And then they go from there, and everything else will be easy. There's also an event coming up for you guys, right? Right.

In April 16 in Prague, OpenSearchCon EU. Yeah. And like I said, we've got so many exciting and new announcements that I'm dying to tell everybody now at KubeCon because I think that this is an exciting event.

But we have to have patience and announce everything at the right time. So see you all in Prague. And if you've never been to Prague, this is the best excuse for going.

Hey, thanks for coming by. Mike, thank you so much. Always a pleasure.

Thank you. And we'll be back in a minute.