Neo4j, where the graph is the query.
In short: Neo4j is the leading native graph database, storing data as nodes, relationships, and properties rather than tables. Its index-free adjacency makes multi-hop relationship traversals fast, queried with the Cypher language. It powers fraud detection, recommendation engines, knowledge graphs, and identity graphs, with the Graph Data Science library for in-database algorithms.
Native graph storage with index-free adjacency, Cypher query language designed for traversal, Graph Data Science library for in-database algorithms, and AuraDB managed-cloud across AWS, Azure, GCP.
Neo4j 5 · causal cluster
3 Core (Raft) + 2 Read Replicas
0.00k
1ms
0M
99.0%
Query Throughput
0.00k QPS[OK] cypher: query plan uses NodeIndexSeek, 4ms
[INF] index: population on :User(email) 73% complete
[OK] cluster: Raft quorum healthy, 3 core servers
[INF] page-cache: warmup 91%, 84M nodes resident
Representative fleet view · illustrative metrics
0+
Neo4j Clusters Managed
0.99%
Uptime SLA
0×
Traversal Speedup vs SQL JOINs
0%
Avg Cost Savings
Building a graph workload?
- ▸ Fraud detection / identity graph needs multi-hop traversal at production scale — Postgres recursive CTEs are slow, and the team is debating Neo4j Community vs Enterprise plus GDS.
- ▸ Knowledge graph for RAG — retrieval needs to traverse semantic relationships, not just nearest-neighbour vectors, and the architecture call hasn't been made between Neo4j + vectors and a pure vector DB.
- ▸ Cypher learning curve — the team is fluent in SQL and uncertain whether Cypher's graph-first model justifies the ramp time for the workload at hand.
JusDB Neo4j specialists design, deploy, and operate graph workloads. See Neo4j consulting →
Neo4j service paths
Neo4j Consulting
Decide your graph model and AuraDB-vs-self-managed strategy, then pick the right GDS and Cypher approach — written advisory deliverables on the Neo4j Consulting page.
Talk to a Neo4j Engineer
Scoping call for graph design, migration from RDBMS, GDS pipeline design, or production Neo4j operations on AuraDB or self-managed K8s.
What we do
What we build with Neo4j
From graph model design to production GDS pipelines — end-to-end Neo4j expertise.
Native Graph Storage
Index-free adjacency means traversals are O(1) per hop — no JOIN cost growth with depth. The right architecture when relationship paths are the query.
Cypher Query Language
Purpose-built for graph traversal — ASCII-art syntax representing nodes and edges. Five-hop queries that are concise where SQL would be verbose and slow.
Graph Data Science (GDS)
In-database algorithms — PageRank, community detection, pathfinding, similarity, embeddings, ML pipelines — without exporting graph data to external tools.
Knowledge Graphs
RAG architecture where retrieval is graph-traversal — combine Neo4j with vector search for hybrid retrieval, semantic relationships preserved through queries.
Fraud & Identity Graphs
Transaction-account-device graphs for fraud detection, identity-resolution patterns, multi-hop suspicious-pattern queries that relational stores struggle with.
AuraDB Cloud Operations
Managed Neo4j on AWS, Azure, GCP — Professional, Business Critical, and Virtual Dedicated tiers with HA, automated backup, and per-region placement.
Traversal performance
Deep traversals, constant cost per hop
Index-free adjacency keeps each hop O(1) — so five-hop fraud and recommendation queries stay fast where recursive SQL CTEs fall off a cliff. We tune the graph model, indexes, and Cypher to match the workload.
Cypher Performance
After tuning50×
Traversal speedup
50%
Cost reduction
Real cases
Queries we've transformed
3,000ms
4ms
Full label scan over 84M :User nodes
The fix
CREATE INDEX FOR (u:User) ON (u.email)
9,400ms
27ms
Unbounded variable-length path explosion
The fix
Bounded path: MATCH (a)-[:KNOWS*1..3]-(b)
12,100ms
19ms
Query had no anchor, scanned all nodes
The fix
Anchored traversal on an indexed start node
0.00%
Cluster Uptime
<0s
Leader Re-election
0ms
Replica Lag
High availability
Always on. Cluster-engineered.
Neo4j Autonomous Clustering replicates the graph across primary servers with Raft consensus, plus secondary servers for read scale-out. A lost primary fails over in seconds — real availability, on AuraDB or self-managed.
Incident response
A runaway-traversal P1, handled in under 15 minutes.
When an unbounded variable-length Cypher path saturates a primary server, a named Neo4j engineer responds — not a ticket queue. We diagnose via query logs, bound the traversal online, and add the missing index.
Recommendation query p99 > 3s — graph degrading
Named engineer in under 15 min, not a ticket queue
Missing index on :User(email) — full label scan
CREATE INDEX + anchored traversal on indexed node
Lookup 3s → 4ms, p99 cleared — total 14 min
Pre-Migration Assessment
RDBMS / RDF → Neo4j 5
Estimated cutover window: < 10 minutes
Migration
Move to Neo4j without the downtime
Relational → Neo4j, or self-managed → AuraDB. We model the graph from the relational schema, bulk-import with neo4j-admin / LOAD CSV, validate traversals against the source, then cut over with confidence.
FAQ
Neo4j — common questions
Ready to evaluate Neo4j?
Book a 30-minute scoping call. We'll discuss your workload, the graph-vs-relational tradeoff, and the shape of the right engagement before any statement of work.
Explore Our Neo4j Services
Explore more ways our Neo4j experts can help with your database infrastructure.