Meet FalkorDB, Winner of Startups of The Year 2024 in Tel-Aviv/Graph Database for GenAI

The special Startups of The Year 2024 Winners Interview series is a celebration of all this year’s Tech Champions. You’ve earned it! The HackerNoon community can’t wait to learn more about your journey!

Tell us about you.

FalkorDB delivers an accurate, multi-tenant RAG solution powered by a low-latency, scalable graph database technology.

Our solution is purpose-built for development teams working with complex, interconnected data—whether structured or unstructured—in real-time or interactive user environments

Our mission is clear: to empower organizations with tools that make their data actionable, accurate, and seamlessly integrated into advanced AI workflows. With FalkorDB's GraphRAG capabilities, businesses can confidently deploy LLM-based applications that meet their needs for precision, speed, and scalability.

Tell us how your startup is changing the world.

FalkorDB is an open-source, ultra-low latency graph database designed for high-throughput, real-time workloads. Our mission is straightforward: make graph-native infrastructure efficient enough to handle today’s AI, cybersecurity, and complex data systems without compromise. We see a future where connected data isn’t a bottleneck—it’s the backbone of intelligent systems.

FalkorDB was founded to address a critical challenge in deploying large language model (LLM)-based applications: the lack of trust and reliability in existing solutions. Enterprises often struggle with these issues, as even leading vector and search database technologies fail to deliver the high accuracy required for enterprise-grade applications. For instance, Microsoft's "hybrid+Semantic Ranker" achieved only 75% accuracy as of September 2023—a level insufficient for many business use cases. Our approach is grounded in cutting-edge research and practical innovation.

What sets you apart from the competition?

FalkorDB⁠ is the first queryable Property Graph⁠ database to use sparse matrices⁠ to represent the adjacency matrix⁠ in graphs and linear algebra⁠ to query the graph. It leverages AVX (Advanced Vector Extensions) to accelerate performance and eliminate the need for complex batch processing jobs.

We designed FalkorDB from first principles—using sparse matrices and linear algebra instead of traditional pointer-based storage. That gives us 496x faster query performance, 11x higher throughput, and 6x lower memory usage compared to incumbents like Neo4j. But beyond benchmarks, it's about simplicity—eliminating the need for complex tuning or batch processing to achieve real-time performance.

What does it mean for you to win this title?

The entire team celebrated this win, it made us feel so great. It validates that there's a demand for rethinking graph databases. This recognition isn’t just about FalkorDB—it signals that developers and architects are ready for infrastructure that doesn’t force trade-offs between performance and flexibility.

What do you love about your team, and why are you the ones to set out for this mission?

We’re a team of system-level thinkers—engineers who care less about trends and more about solving core computational problems. Everyone here understands that performance isn’t a feature; it’s architecture. That mindset drives how we build, review, and iterate. It’s why we’ve been able to outperform much larger players.

Looking back, what milestone was the biggest turning point for your startup?

When enterprise AI teams started replacing their vector databases and traditional RAG stacks with FalkorDB for GraphRAG implementations. That shift proved that graph-native retrieval wasn’t theoretical—it delivered measurable improvements in latency, accuracy, and cost. Product-market fit came when customers stopped asking "why graphs?" and started asking "how fast can we migrate?"

What’s one valuable lesson you learned this year that you’d pass on to other startups?

Speed—both in product and decision-making—wins. But speed without focus leads to noise. In 2024, we learned to say no to feature requests that didn’t align with our core vision. Staying opinionated helped us deliver value faster.

How do you envision your industry evolving in the coming years, and how will your startup stay ahead?

Graph databases will move from being backend tools to critical components in AI pipelines, cybersecurity frameworks, and real-time analytics. But performance ceilings will expose legacy designs. We’ll stay ahead by continuing to optimize for compute efficiency and by integrating graph-native retrieval into emerging AI and security architectures.

How do you or your company intend to embrace the responsibility of this title in 2025?

We’ll use the visibility to push for better standards around graph performance transparency. Too many vendors hide behind benchmarks that don’t reflect production realities. We aim to lead open discussions on what modern graph workloads actually require—and how to meet them.

What goals are you looking forward to accomplishing in 2025?

• Expand FalkorDB’s adoption in AI-driven enterprises replacing vector-based retrieval.
• Release advanced clustering for multi-tenant, distributed graph deployments.
• Grow our open-source community with contributions focused on real-world performance optimizations.

2024 has been crazy, with all the new techs, and all the geopolitics fluctuations. What are the impacts of these to your startup, and to your industry as a whole? Be as brief or as detailed as you like.

AI hype inflated expectations around infrastructure, while cost pressures forced companies to rethink architectures. That worked in our favor—teams began looking for leaner, faster alternatives to legacy databases. Geopolitical shifts highlighted the need for secure, efficient, self-hosted solutions. We adapted by focusing on performance, transparency, and giving enterprises full control over their data.