The future of Dgraph is open, serverless, and AI-ready

At Hypermode, we believe that models will fundamentally change how apps are built over the next decade. Whether it’s the latest developments with generative AI or traditional machine learning approaches, incorporating models into an app allows for unmatched personalization, discovery, and productivity.

Building a model-native app includes, but can be much more than prompting a language model and returning the results directly to the user. It often involves enriching the prompt or results with an organization’s proprietary data. Or, it could be bringing natural language search indexes backed by embedding models directly into the app’s database.

No matter what way you slice it, harnessing and integrating data is critical for model-native apps. We were excited to acquire Dgraph Labs last year, in part because of the increased role we see knowledge graphs playing in incorporating specific, timely knowledge with AI models.

Beyond the release of native vector support in Dgraph v24, we’ve been working with a number of organizations adopting Dgraph as the knowledge graph foundation for their AI strategy. We’ve also seen teams discovering how graph databases can efficiently power app features like leaderboards, activity feeds, and network maps. While there’s energy around new AI-powered possibilities, the upcoming improved performance optimizations and query expressiveness benefit all types of use cases.

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