We got tired of paying eye-watering Vector DB bills. So we built ZizkaDB (and just went open-source).
We got tired of paying eye-watering Vector DB bills. So we built ZizkaDB (and just went open-source).
Hey Indie Hackers,
Like many of you building in the AI space, we’ve spent the last year diving deep into autonomous agents and RAG pipelines. But as our agent workflows scaled, we ran into a massive, frustrating wall: the infrastructure bills.
Traditional vector databases and cloud-hosted state management tools were absolutely eating our margins. We realized we were paying premium tier pricing just to manage long-term agent memory, track complex state loops, and store embeddings.
Worse yet, standard vector DBs aren't actually built for agents. They are built for search. They struggle with fast-moving agentic state, dynamic memory pruning, and deterministic retrieval when an agent loops or fails.
We got fed up. So, we decided to build our own solution.
Meet ZizkaDB ($zizkadb$) — a lightweight, hyper-optimized database engineered specifically for AI agents, long-term memory, and research workflows.
And as of today, it is officially live and 100% open-source.
Why ZizkaDB is different:
Instead of trying to be a generic database for everything, we architected ZizkaDB around the literal pain points of agentic infrastructure:
Zero-Friction Agent Integration: It hooks natively into any custom agentic framework you are running, allowing you to manage agent state and memory pipelines without the overhead.
MCP Native: Fully compatible with the Model Context Protocol (MCP). You can expose ZizkaDB to your LLM architectures seamlessly right out of the box.
Built for Research & Synthesis: It excels at structured retrieval, making it perfect for complex, multi-step research agents that need to cross-reference massive datasets without throwing context-window errors or blowing up token costs.
Check it out & break it:
We wanted to build something that solves our own problem first, but now we need your brutal feedback to make it better.
Explore the site & docs: db.zizka.ai
