Edo Liberty, Founder and Chief Scientist at Pinecone – Interview Series

Edo Liberty, Founder and Chief Scientist at Pinecone, is a leading expert in large-scale data systems and machine learning. Before launching Pinecone—the vector database built for performance and scale—he was Director of Research at AWS and Head of Amazon AI Labs, where his team developed core technologies behind SageMaker, OpenSearch, Kinesis, and more. He previously led Yahoo’s New York research lab, advancing machine learning platforms and applications across search, advertising, and security. His work centers on algorithms and mathematical foundations for handling massive datasets, spanning dimensionality reduction, clustering, streaming, and large-scale linear algebra.

Pinecone is a fully managed vector database built to power scalable and efficient search across large, dynamic datasets. It supports both dense and sparse embeddings, real-time indexing, and metadata-based filtering while integrating seamlessly with leading clouds, models, and frameworks. With serverless auto-scaling, enterprise-grade security, and compliance standards like SOC 2, ISO 27001, GDPR, and HIPAA, Pinecone provides a reliable foundation for deploying large-scale AI applications.

You founded Pinecone in 2019 after leading research at Amazon AI Labs and building systems like SageMaker. What inspired you to launch Pinecone and focus specifically on vector databases?

At AWS, our ML team built amazing systems, but when it came to memory, there was no way to semantically  search through vast amounts of unstructured data. It required extremely specialized engineers who knew how to build complex vector search based solutions. I knew that if there was a way for people to easily access the semantic richness that vectors capture and combine it with sophisticated models, then anyone could accelerate the value of AI for themselves. So I left AWS with the goal of truly transforming how to make it as simple as possible to get the most value out of proprietary unstructured data using the power of AI.

Pinecone has grown into the defining company in the vector database space. Looking back, what were the biggest technical or market hurdles you had to overcome to establish this new category?

The biggest challenge? Nobody knew what a vector database was! We had to educate the market on what we were building and why it was important. We asked our customers what they called it themselves and they told us a vector database.

Once others started to catch on, people would ask why they can’t just use open source? And we’d have to explain all the limitations of open source and scale-performance tradeoffs you’d get – and, after all that, you would still need experienced engineers to build your infrastructure. That’s why we have always been a managed service and focus on user experience. Our system is extremely complicated under the hood because you need this specialized infrastructure for billion-scale similarity search. But we make it accessible with an API call that any developer can use.

This meant abstracting away all the complexity of approximate nearest neighbor algorithms, index management, and distributed systems. Developers don’t want to think about HNSW parameters, they just want it to work.

You’ve recently transitioned from CEO to Chief Scientist, bringing in Ash Ashutosh to lead the company. What motivated this decision, and how do you see your role evolving in Pinecone’s next chapter?

Pinecone, as a company, is an AI company and research-centered company. We got to where we are today because we have redefined search, systems, and algorithms. And, we have been very academically active, publishing technical reports and papers, giving talks and educating the market, even writing text books and giving university courses on AI and Memory. As the company grows, we need to formalize these efforts under a research lab which is separated from the rest of the business. Think about DeepMind and Google as an example. Going forward, I will focus my energy on leading our research, making AI knowledgeable, and building the next set of contextual products from Pinecone.

At the same time, Ash will be a fantastic CEO and leader for Pinecone. He founded and scaled multiple infrastructure companies and knows how to operate a company like Pinecone very effectively. He is deeply knowledgeable and creative about our technology and our market. And, he is intensely customer obsessed. Ash and I will partner deeply on growing the company and our business.

In your blog post you wrote about focusing on “making AI knowledgeable.” Can you unpack what that means in practice and how Pinecone’s technology is uniquely positioned to enable it?

AI without memory is like a brilliant person with amnesia: lots of intelligence, but no context. “Making AI knowledgeable” means giving AI systems the ability to access, understand, and reason over vast amounts of information in real time.

We’re enabling AI-powered applications to provide relevant insights in real time, drawn from semantically understood and organized data and ensuring that AI systems are not just guessing, but are able to retrieve and synthesize knowledge on demand. This results in more accurate, well-informed, and up-to-date outputs for end users.

We do this by providing all the components and capabilities for high-quality, accurate end-to-end retrieval in a single place, along with industry-leading performance for large-scale retrieval.

Having taught “Long Term Memory in AI” at Princeton, how do you see the relationship between vector databases and the future of AI models? Do you believe memory and context are the missing ingredients for today’s large models?

Absolutely. LLMs are pattern matching machines – brilliant ones, but still fundamentally limited by their training data cutoff and context windows. The course I taught with Matthijs Douze from Meta focused on the algorithms that make vector search possible over massive amounts of data. The future isn’t bigger models, it’s smarter retrieval over more data, in real time.

Many enterprises struggle with moving from AI pilots to production-scale deployments. How does Pinecone help bridge this gap, and what best practices have you observed from successful customers?

The gap usually comes down to three things: performance (and cost) at scale, security, and complexity. A demo that works with 10 million documents falls apart at 10 billion. Running something for an hour is different from running it 24/7 with no tolerance to down-time. That’s why we’ve obsessed over our serverless architecture, enterprise-grade features, and ease of use.

The most important thing to do is to just get started. Our customers are often surprised at how much they can do without even talking to us. We’ve designed it that way on purpose, but are always there when our customers need us too.

You’ve spent your career moving between academia, big tech research (Yahoo, AWS), and entrepreneurship. How have those different environments shaped your approach to building Pinecone?

Academia taught me to think from first principles. How to abstract and design great solutions.  At Yahoo and AWS, I learned how to build simple data platforms that engineers love building on.

Entrepreneurship is where you learn that the best technology only wins if it solves real problems in a way people can actually use.

This mix is crucial for what we’re building. We’re not just writing research papers or building technology for technology’s sake. Every innovation has to make developers’ lives easier and enterprises’ applications more powerful.

The convergence of search and AI feels like one of the biggest shifts in computing. Where do you think this is headed in the next five years, and how will Pinecone’s work help shape that future?

Search is becoming conversational and contextual. In five years, you won’t “search” – you’ll have dialogues with AI systems that understand not just your query but your intent, your context, your history. Every interaction will be informed by vast knowledge bases which are updated in real-time.

We’re building the infrastructure for this. Our vector database is just the beginning. I see a future where every application has embedded context, where AI doesn’t hallucinate because it’s grounded in data, where the boundary between searching and knowing disappears.

As Chief Scientist, you’ll be more hands-on with data, models, and prototyping. What areas of research are you personally most excited to dig into right now?

Oh man, where do I even start? For the short-medium term, I’m diving deeper into efficiency and ease-of-use. Can we make vector search 10x faster while making it 10x cheaper? Can we make our APIs even simpler than they are today? These are hard problems.

For the longer term, I’m obsessed with the intersection of retrieval and reasoning. How do you build systems that don’t just find relevant facts but understand the relationships between them? And then use that context to create knowledgeable AI and more powerful agents.

Finally, on a personal level: stepping back from the CEO role, what excites you most about this transition, and what kind of breakthroughs do you hope to unlock in Pinecone’s next stage?

I’m happiest when I’m deep in the code, working with our research team, having those “wow, this actually works!” moments at 2 AM.

My dream? To make retrieval so good that it becomes invisible. Where developers can build contextual applications without thinking about embeddings or indexes or any of that and it just works.

We’re at this incredible moment where AI and data are converging. The potential is limitless and I get to spend my days making that future happen now.

Thank you for the great interview, readers who wish to learn more should visit Pinecone.