Nodedb
NodeDB fuses vector search, graph, document, columnar, key-value, full-text, sparse array, and CRDT into one universal database engine
If your RAG pipeline currently stitches together pgvector, a graph database, a cache, and Elasticsearch, NodeDB is the most direct consolidation pitch on the market and the SQL-first design means you keep your existing Postgres client. The hybrid search story is the real draw: rrf_score() fuses BM25 and vector in one query with no re-ranking service in between, and the same goes for pre-filtered vector search. Just go in with eyes open about the pre-1.0 label, which is fine for a GraphRAG
Verified 38m ago · liveness 62/100 · cite: rightaichoice.com/tools/nodedb
- AI product teams building hybrid RAG or GraphRAG that need vector plus graph plus full-text in one SQL query
- SaaS developers who want RLS, RBAC, tenant isolation, and audit logging inside the same database as their embeddings
- Data engineers consolidating pgvector, Redis, Neo4j, ClickHouse, and Elasticsearch into a single deployable engine
- Edge and offline-first apps needing CRDT sync with configurable conflict policies
- Production systems with contractual uptime requirements or published SLA expectations
- Teams that need an extensive managed-service ecosystem and a deep bench of third-party integrations
- Simple CRUD apps with no multi-model or hybrid retrieval needs
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Skip NodeDB if you need a production database with a contractual SLA, a managed cloud offering, and a wide third-party integration ecosystem today — it's pre-1.0 and self-hosted.
NodeDB is self-hosted with contact-based pricing, so your real cost is the engineering time to run multi-Raft clusters, WAL tiering, backup, and corruption quarantine yourself.
NodeDB's pricing is contact-based, with no published tiers, so cost is negotiated. That fits funded startups and platform teams consolidating a five-database bill where the combined Postgres, Pinecone/Qdrant, Neo4j, Redis, and Elasticsearch spend justifies a single engine. For indie developers or small teams, self-hosting a pre-1.0 multi-Raft cluster is harder to justify versus a managed Postgres with pgvector at predictable monthly rates.
In short
Nodedb — NodeDB fuses vector search, graph, document, columnar, key-value, full-text, sparse array, and CRDT into one universal database engine. Best for AI product teams building hybrid RAG or GraphRAG that need vector plus graph plus full-text in one SQL query, SaaS developers who want RLS, RBAC, tenant isolation, and audit logging inside the same database as their embeddings, Data engineers consolidating pgvector, Redis, Neo4j, ClickHouse, and Elasticsearch into a single deployable engine. Contact Sales pricing.
What's new in Nodedb
Checked todayAcross the latest 3 updates: 2 feature updates and 1 launch.
Public Preview Launch on Product Hunt
NodeDB announced public preview availability and invited developers to pull the Docker image and start building.
Added CRDT offline sync support
Enabled built-in CRDT-based synchronization for edge devices, allowing conflict-free offline merging with configurable conflict policies.
Introduced hybrid search with Reciprocal Rank Fusion
Added a built-in RRF function to fuse BM25 and vector scores in a single query, enabling hybrid search without extra pipelines.
What people actually say about Nodedb — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
34 mentions across 4 sources (Hacker News, YouTube, Product Hunt, GitHub) · researched Aug 29, 2026.
Average across the 4 sources that answered — each source counts once, not each post.
- +Unifies five engines into one binary, simplifying AI data stacks.
- +Standard SQL across engines enables hybrid vector-relational queries.
- +CRDT offline sync lets edge devices merge changes seamlessly.
- +PostgreSQL wire protocol means existing Postgres clients work immediately.
- +Built-in row-level security and audit logging suit multi-tenant SaaS.
- −High-severity bugs: silent wrong reads and data loss in CRDT sync.
- −CRDT documents can become unopenable and spin CPU at 100%.
- −Trust-mode sync can leave catalogs corrupt and data dirs unbootable.
- −Very early stage: only 193 stars and 19 open issues.
- −Limited ecosystem and integrations compared to mature databases.
- • No transparent pricing; likely self-hosted infrastructure costs.
- • Integration and migration costs from existing databases could be high.
Viability Score
How well maintained and how widely used is Nodedb? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Unified SQL planner that joins vector, graph, document, columnar, key-value, and full-text engines natively
- Vector search with HNSW index and product quantization
- Hybrid search with built-in Reciprocal Rank Fusion via rrf_score()
- Full-text search with BM25 scoring and fuzzy matching
- Property graph with 13 built-in algorithms and CSR indexing
- ND sparse array storage for genomics, climate, and earth observation data
- Spatial queries with ST_DWithin and R-tree geometry index
- Bitemporal queries for audit, time-travel, and GDPR-safe erasure
- Built-in CRDT offline sync with configurable conflict policies
- Multi-Raft cluster replication with vshards
- Cross-shard transactions
- Row-Level Security, Role-Based Access Control, and tenant isolation for multi-tenant SaaS
- OIDC/SSO authentication and TLS
- PostgreSQL wire protocol (pgwire) compatibility for any Postgres client
- Change streams, consumer groups, webhooks, and cron scheduler
About Nodedb
NodeDB is a universal database engine that collapses the typical five-service AI stack into one binary. Vector search with HNSW and product quantization, property graph with 13 algorithms, document, columnar, key-value, full-text BM25, ND sparse arrays for genomics and climate data, and CRDT offline sync all sit on shared storage with zero network hops between engines. It replaces the PostgreSQL + pgvector + Redis + Neo4j + ClickHouse + Elasticsearch combination rather than sitting alongside it. The interface is plain SQL over the Postgres wire protocol. Point any pgwire-compatible client at one connection string and the planner joins across engines natively. You can pre-filter vector search with a relational WHERE clause, fuse BM25 and vector scores through the built-in rrf_score() Reciprocal Rank Fusion function, or mix ST_DWithin spatial filters with embedding similarity in a single statement. Audit, time-travel, and GDPR-safe erasure come from bitemporal queries on the engines that need them. For multi-tenant SaaS, NodeDB ships RLS, RBAC, OIDC/SSO, audit logging, tenant isolation, and tenant quotas, so row filters and permission plumbing stay in the database instead of your application layer. Multi-Raft cluster replication with vshards and cross-shard transactions handle scale-out, while change streams, consumer groups, webhooks, and a cron scheduler cover event-driven work. CRDT sync with configurable conflict policies covers edge and offline-first deployments. NodeDB entered public preview on Product Hunt in March 2026 and the team reports it is already running in real projects. It targets AI product teams building hybrid retrieval and GraphRAG workloads, SaaS developers consolidating data services, and data engineers who are tired of glue code, dual writes, and cross-service joins. The honest framing: this is pre-1.0 and moving fast, so compare it against Supabase with pgvector plus a dedicated search store for anything revenue-critical.
Behind the Verdict
We'd reach for NodeDB the moment a project needs vector plus graph plus full-text in one query. That combination is where five-database stacks hurt most: you either move embeddings across a network boundary or write application code to merge rankings. NodeDB's rrf_score() and native cross-engine joins delete both problems. Hybrid search shipped in the January 2026 changelog and CRDT offline sync followed in February, so the feature surface is still actively growing rather than frozen at launch. The second reason to pick it is operational. One binary, one connection string, one bill. For a small team shipping a GraphRAG product, that is months of platform work you don't do. Multi-tenant SaaS gets RLS, RBAC, tenant isolation, and quotas out of the box, which is genuinely rare to find alongside vector search in the same engine. Where it bites is maturity. This is a pre-1.0 public preview with no published SLAs and no independent benchmark results we can point to. Multi-Raft replication and cross-shard transactions are the features most likely to surprise you under real failure conditions, and those are exactly the ones you can't evaluate from a feature list. Run your own fault-injection tests before trusting them. Pricing is another unknown. We don't have verified tiers to report here, so budget conversations should start with the vendor directly until published plans appear. That matters more than usual for a system replacing five paid services. The closest alternative is the opposite approach: Supabase with pgvector plus Neo4j or Elasticsearch alongside it. That stack is boring, well understood, and hireable for. You pay in glue code and cross-service joins. NodeDB pays you back in that currency but charges in maturity risk. For greenfield GraphRAG prototypes,
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Real-world workflow fit
Concrete scenarios for the personas Nodedb actually fits — and what changes day-one when you adopt it.
Pull the NodeDB Docker image, point an existing Postgres client at it via pgwire, load documents and embeddings, then write one query that traverses the property graph, runs HNSW vector search, and fuses both with rrf_score.
Outcome: Hybrid retrieval ships without a Python fusion layer or a second database, and cross-service joins disappear from the application code.
Model tenants as collections with RLS policies and RBAC roles, move sessions and counters off Redis into the key-value engine, move product search off Elasticsearch into the built-in BM25 index, and enable audit logging for compliance.
Outcome: One connection string, one storage layer, and tenant isolation without writing custom row filters in every service.
Enable CRDT-based offline sync on devices, define conflict policies, and route unresolvable conflicts to the dead-letter queue; device data merges with cloud-side relational and vector data in the same engine.
Outcome: Offline-capable devices sync without a custom sync layer, and the cloud side queries edge data alongside its other workloads.
Use Cases
- Build a real-time GraphRAG application that joins property graph traversal and vector search in a single SQL query
- Replace a stack of PostgreSQL + Redis + Neo4j + Elasticsearch + TileDB with one engine for a multi-tenant SaaS platform
- Implement bitemporal data management for financial audit trails with GDPR-compliant erasure
- Serve hybrid search combining BM25 and vector scores via rrf_score for an e-commerce product catalog with no glue code
- Sync data from edge IoT devices to the cloud using built-in CRDT with configurable conflict policies
- Run spatial queries combined with vector similarity for location-aware recommendations
- Store and query ND sparse arrays for genomics, climate, or earth observation datasets
- Keep session and counter data in the same engine as relational and vector workloads
Limitations
- NodeDB is in public preview and pre-1.0, so expect rough edges and a fast-moving surface.
- There is no published SLA and no production benchmarks, which makes it hard to justify for revenue-critical systems.
- The integration ecosystem is thin: the docs list client libraries and wire protocols but no third-party connectors, managed cloud offering, or marketplace.
- Documentation covers architecture, indexing, and operations in depth but does not state explicit usage limits or production performance tuning guidance.
- You deploy and operate it yourself, which means cluster topology, replication, backup, and corruption quarantine are your responsibility.
as of 2026-09-14
Verification history
We have re-verified Nodedb 8 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
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Showing the 6 most recent of 8 verification passes.
Free to cite with attribution — this page re-verifies continuously.
Where the pricing makes sense
The company stage and team size where Nodedb's pricing actually pencils out — and where peers do it cheaper.
NodeDB's pricing is contact-based, with no published tiers, so cost is negotiated. That fits funded startups and platform teams consolidating a five-database bill where the combined Postgres, Pinecone/Qdrant, Neo4j, Redis, and Elasticsearch spend justifies a single engine. For indie developers or small teams, self-hosting a pre-1.0 multi-Raft cluster is harder to justify versus a managed Postgres with pgvector at predictable monthly rates.
Setup time & first value
How long it actually takes to get something useful out of Nodedb — broken out by persona, not the marketing-page minute.
For an engineer comfortable with Postgres, first value comes fast: pull the Docker image, point any pgwire client at it, and run a basic query in an afternoon. Standing up a production-shaped multi-Raft cluster with vshards, WAL tiering, backups, and RLS policies is a multi-day project. AI teams evaluating GraphRAG can prototype inside a day.
Switching to or from Nodedb
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From PostgreSQL: use the pgwire-compatible interface, then move vector columns into the HNSW vector engine and graph relationships into the property graph.
- →From pgvector: recreate embeddings in NodeDB's vector engine and replace application-layer fusion with rrf_score for hybrid queries.
- →From Redis: move session and counter workloads to the key-value engine with O(1) hash lookups.
- →From Elasticsearch: reindex documents into the BM25 full-text engine and rebuild fuzzy matching there.
- →From Neo4j: load nodes and edges into the property graph and rebuild traversals using the 13 built-in graph algorithms.
- ↗To PostgreSQL with pgvector: export relational tables via pgwire and rebuild vector indexes in pgvector.
- ↗To a managed vector database such as Qdrant: export embeddings and metadata and reindex.
- ↗To Neo4j: export graph nodes and edges and rebuild traversals in Cypher.
- ↗To Elasticsearch or OpenSearch: export full-text indexes and reindex for BM25 scoring.
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Nodedb”, and we withheld 6: 6 could not be judged, because “Nodedb” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Nodedb.
Official links
Tools that pair well with Nodedb
Common stack mates teams adopt alongside Nodedb, with the specific reason each pairing earns its keep.
Doris
Apache Doris: an open-source real-time SQL database that unifies OLAP analytics, full-text search, and vector search in one engine.
RAGFlow
Open-source RAG engine that turns messy documents into a trustworthy context layer for AI agents, with ETL, hybrid search and agentic retrieval.
Vespa
Vespa is an open-source distributed search and AI platform that unifies vector, text, and structured retrieval with machine-learned ranking in one engine.
Featured Head-to-Head Comparisons
Nodedb vs Spider Cloud
If you need fast, reliable web scraping for AI agents or RAG pipelines, Spider Cloud is the clear winner—its Rust engine, AI extraction upgrades, and 1,000+ scraper catalog deliver immediate value for ~$0.03/1k pages. NodeDB is an ambitious universal database, but it's early-stage and lacks pricing transparency; it's only worth considering if you're ready to consolidate multiple databases and can tolerate the risk of a less mature product.
Nodedb vs Voyage Ai
Choose Voyage AI if your priority is high-accuracy retrieval of domain-specific documents (finance, legal, code) using specialized embedding models and rerankers. Choose NodeDB if you need a single database that unifies vector, graph, document, and search capabilities to replace multiple databases, especially for hybrid RAG and multi-tenant SaaS. They serve different layers: Voyage is pure AI models, NodeDB is a data platform.
Nodedb vs Temporal Ai
Choose Temporal AI if you need reliable orchestration for AI agents or long-running workflows that survive failures — it's battle-tested with a clear pricing path. Choose Nodedb only if you absolutely must consolidate vector, graph, and document storage into one database and are willing to risk early-stage maturity. For most teams, Temporal is the safer bet today.
Arize Phoenix vs Nodedb
NodeDB is for teams consolidating multiple datastores into one multi-model engine, ideal for vector+graph hybrid RAG and offline sync. Arize Phoenix is for teams needing deep observability into LLM agent behavior, with tracing, evaluation, and experiment tracking. Choose NodeDB if your pain is database sprawl; choose Phoenix if your pain is untraceable agent failures.
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Doris
Apache Doris: an open-source real-time SQL database that unifies OLAP analytics, full-text search, and vector search in one engine.
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