Why we’re continuing to invest in Dgraph

When evaluating technology investments, we need to look beyond the current market position – we look at fundamental technology value and future potential. Our investment in Dgraph stems from a deep conviction in both the technology and the growing importance of knowledge graphs in AI infrastructure.

A quick look back

Since its inception in 2016, Dgraph has maintained something remarkable: a robust, technically superior graph database system with a highly engaged open source community. The core technology continues to solve real problems for real users. Its distributed architecture ensures that it can scale with data and user needs. And it is clear that the technology can be applied to the real-world. With thousands of deployments globally, Dgraph is behind some of the world’s most important applications. From building rockets and self-driving cars, to beloved personal computers, to producing clean energy—Dgraph is a trusted part of their applications.

Why Dgraph matters now more than ever

We're at a fascinating inflection point in AI development. While Large Language Models (LLM) have transformed what's possible with AI, they've also exposed critical limitations in how machines understand and relate information. Knowledge graphs aren't just a nice-to-have anymore – they are becoming a critical infrastructure for next-gen AI systems that require:

• Structured representation of complex relationships
• Verifiable and auditable information paths
• Dynamic updating of knowledge bases
• Semantic understanding and reasoning capabilities

Dgraph's architecture is particularly well-suited with AI-native primitives. Its vector indexing, search, and storage allow you to store multiple embeddings on any given node or relationship. Uniquely able to store multiple vector embeddings, Dgraph allows you to compare and combine embedding models and runtime to get the best results for your similarity search. You can combine HNSW vector similarity, keyword-search, geospatial polygons and graph traversals to power your multi-modal search. Further, Dgraph’s namespace capabilities enable your multi-customer AI apps to have logically separated knowledge graphs. We are in a fortunate position to continue building upon a solid technical foundation.

The timing of our investment in Dgraph aligns with the growing enterprise need for a knowledge graph engine and tools. As forward-leaning, Series A startups such as software automation tool Resolve AI, omnichannel marketing platform Tofu, and productivity tool Tana leverage knowledge graphs as a core differentiation in their product offerings, we are working with companies such as Kori Labs and Sonoma who were founded this year and building with Dgraph in their foundational architecture.

Our vision forward

Our investment in Dgraph isn’t just about maintaining status quo – it's about growth and evolution. Here's what we're committed to:

1. Technical Stability: Increasing resources for core development, bug fixes, and performance optimizations
2. Enterprise Readiness: Enhancing features that large-scale deployments need: better monitoring, security controls, and compliance capabilities
3. AI Integration: Developing native capabilities for AI applications, including improved vector search, semantic query capabilities, and LLM integration
4. Community Trust: Rebuilding and strengthening relationships with both open source users and enterprise customers

Furthermore, we are making Dgraph v25 fully open-source in the upcoming months, releasing previously commercial enterprise features for free. This change and the serverless expression of Dgraph to allow for a pay-as-you-use cloud offering are both designed to make graph database implementations more accessible and cost-effective for developers building AI-powered apps.

Moving forward together

We recognize that trust is earned through actions, not just words. That's why we're committed to being transparent about our plans and progress. We're setting up new channels for community feedback and will be sharing our technical roadmap in the coming weeks.

To our existing users: thank you for your continued trust and patience. To those who may have stepped back: we invite you to take another look at what we're building.

The future of AI needs robust, scalable knowledge graph solutions. With this investment, we're not just betting on Dgraph's technology – we're investing in the future of agentic services and the infrastructure they'll need to succeed. We're excited about this new chapter, and we invite you to be part of it. Your feedback and input will be crucial in shaping these plans.

Have thoughts or questions about our direction? We'd love to hear from you. Drop us a note on our Discord or GitHub discussions.