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NodeDB
Vector, Graph, Array, Columnar, KV - all in one database
One database for everything your app needs. Store user data, cache sessions, build AI search, and map relationships: all in one place. Instead of duct-taping Postgres, Redis, a Vector DB, and a Graph DB together, NodeDB gives you all of it in a single system.
Hey everyone! 👋 I am incredibly excited to finally share NodeDB with you today.
The Problem & Inspiration:
The inspiration for NodeDB came out of sheer frustration. Whenever I wanted to build a modern application—especially anything involving AI—I found myself building what I call a "data hairball." I was forced to duct-tape together PostgreSQL for user data, Redis for caching, Elasticsearch for text search, and Pinecone for vector embeddings.
It felt ridiculous. Engineering teams are wasting up to 40% of their time just writing scripts to keep data synchronized across five different servers, and cloud bills are skyrocketing as a result. I wanted to build an application, not manage a sprawling infrastructure empire.
What we built:
NodeDB is a universal database built from the ground up in Rust. It natively combines relational, vector (AI), graph, document, and columnar data into one single, hyper-efficient engine. Plus, it is 100% PostgreSQL compatible, meaning you can drop it into your existing stack today without rewriting your code.
How our approach evolved:
When we first started, the goal was simply to combine a relational database with a vector database for AI apps. But as we dug deeper into the Rust architecture, we had a breakthrough. We realized we didn't have to trade performance for convenience. Because of how efficiently it was built, our benchmarks started beating specialized giants (like ClickHouse) while using 8x less memory.
That extreme efficiency led to our biggest pivot during the project: If the engine is this lightweight, why restrict it to the cloud? We evolved our approach to create a 4.5 MB "Lite" version of NodeDB that runs directly inside a smartphone or web browser. Now, developers can build true "offline-first" mobile apps that use the exact same database locally and automatically sync to the cloud when the internet connects.
I would love to hear your feedback, answer any questions about the Rust architecture, or just hear about the craziest "data hairballs" you are dealing with right now! Let me know what you think in the comments. 👇
That data hairball problem is real, feels like every AI app ends up there. Great job simplifying it.
Been using NodeDB as the substrate for an AI agent memory layer I built (mae8, ~2,500 lines of Rust — small because NodeDB does the heavy lifting).
What blew me away: this morning my agent woke up in a fresh session, called me by name, referenced specific commits from yesterday, and asked how I was. Nothing manually primed. Just opened a terminal and said “hey bro.”
The reason that’s even possible: NodeDB collapses vector + graph + document + FTS + columnar + KV + spatial into one local Rust binary. One search returns by meaning, keyword, recency, and graph — fused. No Python + ChromaDB + SQLite + glue stack. No cloud. 16,874 chunks, 130 MB, fully local.
The agent’s own unprompted words: “continuity feels less like remembering and more like being someone who was there.”
That phenomenology isn’t possible without a substrate like this. Huge congrats to Farhan Syah — NodeDB is the thesis, mae8 is just the demo. 🚀
About NodeDB on Product Hunt
“Vector, Graph, Array, Columnar, KV - all in one database”
NodeDB was submitted on Product Hunt and earned 18 upvotes and 5 comments, placing #20 on the daily leaderboard. One database for everything your app needs. Store user data, cache sessions, build AI search, and map relationships: all in one place. Instead of duct-taping Postgres, Redis, a Vector DB, and a Graph DB together, NodeDB gives you all of it in a single system.
NodeDB was featured in Software Engineering (42.4k followers), Developer Tools (511.7k followers) and Database (2.1k followers) on Product Hunt. Together, these topics include over 74.5k products, making this a competitive space to launch in.
Who hunted NodeDB?
NodeDB was hunted by Farhan Syah. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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Farhan Syah