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---
title: Replace 5 databases with 1 universal engine
description: NodeDB natively combines relational, vector, graph, document, columnar, key-value, full-text, and scientific array data in one Rust binary. Your existing PostgreSQL client just works.
layout: landing
---

<Head>
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<meta property="og:site_name" content="NodeDB" />
<meta property="og:title" content="NodeDB — Replace 5 databases with 1 universal engine" />
<meta property="og:description" content="Relational, vector, graph, document, columnar, and scientific array data — in one Rust binary. Your existing Postgres client just works." />
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  "applicationCategory": "DatabaseApplication",
  "operatingSystem": "Linux, macOS, Windows, iOS, Android, Web",
  "description": "Distributed hybrid database with eight peer engines plus Graph and Full-Text Search overlays, in a single Rust binary.",
  "url": "https://nodedb.dev",
  "offers": { "@type": "Offer", "price": "0", "priceCurrency": "USD" }
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<Banner id="ph-launch" persist="session">
🚀 **Featured on Product Hunt.** [See the launch thread →](https://www.producthunt.com/products/nodedb)
</Banner>

<LandingNav logo="/assets/logo.svg" github="https://github.com/NodeDB-Lab/nodedb" discord="https://discord.gg/s54gDMVc7B" />

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  ph-kicker="Featured on"
  producthunt="https://www.producthunt.com/products/nodedb"
  github="https://github.com/NodeDB-Lab/nodedb"
  discord="https://discord.gg/s54gDMVc7B"
  title="Replace 5 databases with"
  accent="1 universal engine."
  sub="Stop wrestling with fragmented data silos. NodeDB natively combines relational, vector (AI), graph, document, columnar, and scientific array data into a hyper-efficient Rust architecture. Your existing PostgreSQL client just works."
/>

<Section class="ndb-hero-code">
<CodeWindow snippet="graphrag-fusion" title="GraphRAG · vector search + graph expansion · one query" lang="sql" />

Vector DB + graph DB + ranker, fused in one query. No pipelines. No Python glue. This is GraphRAG at the database layer.
</Section>

<Section class="ndb-before-after">

## The stack you were going to build

Most AI product teams ship with Postgres + a vector DB + a graph DB + a cache + a search engine. That's five systems to deploy, five bills, five failure modes, and a mountain of glue code. NodeDB is one.

<FeatureGrid class="ndb-ba-grid">
<Feature title="Before">
5 services, your ops team, 3am pages

- **Postgres** for users, orders, billing
- **Pinecone / Weaviate** for embeddings
- **Neo4j** for social graph & recommendations
- **Redis** for sessions & cache
- **Elasticsearch** for product search
- **TileDB / Zarr** for scientific arrays (genomics, climate, earth obs)
- ⚠️ Glue code, dual writes, sync drift, cross-service joins in your app layer
</Feature>
<Feature title="After">
One connection string

- **NodeDB** — relational tables
- **NodeDB** — vector index with HNSW + PQ
- **NodeDB** — property graph with 13 algorithms
- **NodeDB** — key-value with O(1) hash lookups
- **NodeDB** — full-text search with BM25
- **NodeDB** — ND sparse arrays for genomics, climate, earth obs
- **Bitemporal** on the engines that need it — audit, time-travel, GDPR-safe erasure
- **RLS + RBAC + audit log + tenant isolation** — multi-tenant SaaS without writing row filters
- ✨ One SQL planner joins them natively. Ship the app, not the plumbing.
</Feature>
</FeatureGrid>
</Section>

<Section bg="muted">

## What NodeDB Replaces

The combination of PostgreSQL + pgvector + Redis + Neo4j + ClickHouse + Elasticsearch — unified into one binary with shared storage and zero network hops between engines.

| Engine           | Purpose                        | Replaces            |
| ---------------- | ------------------------------ | ------------------- |
| Vector           | Semantic search, RAG, recs     | pgvector, Qdrant    |
| Graph            | Relationships, GraphRAG        | Neo4j               |
| Document         | Flexible or strict schemas     | MongoDB, PostgreSQL |
| Columnar         | Analytics, timeseries, spatial | ClickHouse, DuckDB  |
| Key-Value        | Sessions, counters, caches     | Redis, DragonflyDB  |
| Full-Text Search | BM25, fuzzy, hybrid fusion     | Elasticsearch       |
| Array            | ND sparse scientific data      | TileDB, SciDB, Zarr |
| CRDT             | Offline sync, edge devices     | Custom sync layers  |

</Section>

<Section class="ndb-sql-showcase">

## Cross-Engine Queries in Standard SQL

```sql
-- Vector search with metadata pre-filtering
SELECT title, vector_distance(embedding, $query_vec) AS score
FROM articles WHERE category = 'ml'
  AND id IN (SEARCH articles USING VECTOR(embedding, $query_vec, 10));

-- Hybrid BM25 + vector, fused with Reciprocal Rank Fusion
SELECT title, rrf_score(
    vector_distance(embedding, $query_vec),
    bm25_score(body, 'distributed database')
) AS score FROM articles LIMIT 10;

-- Spatial + vector in one statement
SELECT name FROM restaurants
WHERE ST_DWithin(location, ST_Point(-73.99, 40.75), 2000)
  AND embedding <-> $query_vec LIMIT 10;
```

</Section>

<Section class="ndb-faq">

## The questions you're about to ask

<Faq />
</Section>

<Section id="get-started" class="ndb-final">
<CTA title="Ship AI products, not plumbing." description="NodeDB is in public preview and already running in real projects. Pull the image, point any Postgres client at it, and start building." />

<CodeWindow snippet="quickstart" title="run it in 30 seconds" lang="bash" />

Pre-1.0 and moving fast — follow the [road to 1.0](https://github.com/orgs/NodeDB-Lab/projects/7).

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  discord="https://discord.gg/s54gDMVc7B"
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/>
</Section>