Over the past week, our Discord server has been growing fast—we saw almost 100 developers joining every single day, crossing the 1,000 member milestone in under a week. The conversations we are having in that community have illuminated a stark divide in the AI ecosystem.
We have observed that founders at the very start of their journey don't yet realize the wall they are going to hit. They start building AI applications using traditional graph databases because it seems like the right tool for modeling complex relationships. But the founders who are already building at scale? They've told us exactly how critical our mission is.
The $70,000 Wall
When you deploy autonomous agents at scale, the number of queries explodes. Agents constantly traverse relationships to gather context, reason, and act. With a traditional graph database, this translates directly to compute overhead.
We spoke with a team recently whose graph database bill had ballooned to almost $70,000 a month. When your infrastructure costs scale exponentially with agent reasoning, trying to earn that back from end-user subscriptions is almost impossible. The unit economics simply do not work.
Stripping Away the Bloat
Our ongoing conversations with engineers building AI applications and autonomous agents at scale have crystallized a core thesis: for AI workloads, you can strip away almost all of the bloated features of a traditional graph database. Agents don't need sprawling query languages or heavy transactional abstractions—they need raw, real-time structural context.
The evolution here mirrors the history of computer graphics. Traditional graph databases are like offline CGI rendering—incredibly feature-rich, capable of modeling anything, but fundamentally too slow for real-time interaction. What AI agents actually need is an Unreal Engine. They need a system designed from the ground up for a real-time hot loop, stripping away everything that doesn't serve the immediate traversal.
pgGraph applies that exact mindset to Postgres data. By discarding the heavy abstractions of relational emulation and compiling edges into a bare-metal CSR (Compressed Sparse Row) array, we achieve graph traversals at speeds that standard Postgres query planners physically cannot match.
A Fundamental Change
That is what drove us to build Evokoa. We realized that for AI agents to actually be deployed at scale, the unit economics of graph infrastructure must fundamentally change.
You shouldn't have to choose between deep reasoning capabilities and sustainable infrastructure costs. By rethinking the problem from the bare metal up, we're building the real-time graph engine that makes scalable agents economically viable.