Stop writing 15-line JOINs.
Query relationships instead.
AI Graph OLAP connects to your Snowflake, BigQuery, or Starburst warehouse and lets you run multi-hop graph queries 120,000x faster than SQL. AI-powered query generation. Zero idle cost. Deploys in your VPC.
Trusted by data engineers at companies running
The core problem
SQL handles: “Show me all transactions over £10,000”
SQL breaks at: “Find accounts sharing a phone number with someone who shares an address with someone who made a suspicious transfer”
That's 3 hops through relationships— graphs handle this in one line. SQL needs 15+ self-joins.
SELECT DISTINCT a3.account_id
FROM accounts a1
JOIN addresses addr1
ON a1.address_id = addr1.id
JOIN accounts a2
ON a2.address_id = addr1.id
JOIN transfers t
ON t.from_account = a2.account_id
JOIN beneficiaries b
ON b.id = t.beneficiary_id
JOIN accounts a3
ON a3.beneficiary_id = b.id
WHERE a1.flagged = true
AND t.amount > 10000;MATCH (a:Account {flagged: true})
-[:SHARES_ADDRESS]->()
<-[:SHARES_ADDRESS]-(b:Account)
-[:TRANSFERRED_TO]->(c:Beneficiary)
<-[:LINKED_TO]-(d:Account)
WHERE a.amount > 10000
RETURN DISTINCT d.account_idSame fraud-detection query. Graph OLAP uses Cypher — a purpose-built language for relationships.
SQL complexity explodes exponentially
Every additional hop through your data doubles the SQL you need to write. Cypher stays flat — always one line per relationship.
Built for production graph analytics
Enterprise-grade infrastructure that plugs into your existing data stack. No migration needed.
120,000x Faster Queries
In-memory graph engine processes multi-hop traversals in milliseconds. What takes SQL minutes takes Cypher milliseconds.
< 12ms for 2.4M nodes
One Graph Per Analyst
Each analyst gets their own isolated graph pod. No noisy neighbors, no shared state, full data isolation by default.
Zero cross-tenant leakage
20+ Built-in Algorithms
PageRank, betweenness centrality, community detection, shortest path and more. Run advanced graph analytics out of the box.
PageRank on 2M nodes in 12ms
Connects to Your Warehouse
First-class connectors for Snowflake, BigQuery, Starburst, and Databricks. Your data never leaves your infrastructure.
50+ data source integrations
Kubernetes Native
Helm charts, auto-scaling with KEDA, pod-per-user architecture. Runs on any K8s cluster, any cloud, or on-prem.
Scale to zero, scale to thousands
Fully Open Source
Apache 2.0 licensed. No vendor lock-in, no phone-home telemetry, no usage limits. Deploy in your VPC, your rules.
Apache 2.0 — no strings attached
Analyse relationships without writing code
Smart schema mapping, visual exploration, and 20+ graph algorithms make graph analytics accessible to every analyst.
Smart Schema Mapping
AI auto-detects which files are nodes vs. edges, suggests primary keys, and infers relationships from column names. Upload data and the graph builds itself.
Visual Graph Explorer
Click-to-expand node exploration with hover tooltips, community detection, and 4 layout algorithms. No Cypher needed — business users can investigate directly.
Path Highlighting
Click any two nodes to instantly see the shortest path between them. Trace fraud rings, supply chain dependencies, or permission chains visually.
20+ Built-in Algorithms
PageRank, betweenness centrality, Louvain community detection, shortest path, and more. Run complex graph algorithms with a single Cypher call.
Anomaly Detection
Identify unusual patterns, outlier nodes, and hidden clusters using graph metrics. Spot fraud rings and suspicious connections that tabular analytics miss.
Private & Secure
Everything runs inside your infrastructure. No data sent to external APIs. Per-analyst isolation ensures zero cross-tenant data leakage.
From warehouse to graph in 4 steps
No ETL pipelines. No data duplication. Connect, map, launch, explore.
Connect Source
Point Graph OLAP at your Snowflake, BigQuery, Starburst or Databricks warehouse. Credentials stay in your VPC.
Map Your Data
Choose which tables become nodes and which become edges. Use the visual mapper or YAML config.
Launch Graph
One click exports data from your warehouse and spins up an isolated in-memory graph instance on Kubernetes.
Explore & Analyze
Run Cypher queries, execute graph algorithms, and explore results visually. All in the browser.
Built for teams that need answers fast
Real queries, real results. See how graph analytics solves problems across industries.
Fraud Detection
Banking & Financial ServicesTrace fraud rings through shared accounts, addresses, and beneficiaries. Detect circular transfers and shell company networks that SQL queries miss entirely.
MATCH (a:Account)-[:SHARES_ADDRESS]->(addr)<-[:SHARES_ADDRESS]-(b:Account)-[:TRANSFERRED_TO]->(c) WHERE a.flagged = true RETURN b, cFound 847 suspicious accounts linked through 12 shared addresses in 23ms
Supply Chain Tracing
Manufacturing & LogisticsWhen a supplier fails, trace the full impact chain across your production lines. Identify alternate suppliers and predict delivery delays across 6+ hops.
MATCH path = (s:Supplier {status:'failed'})-[:SUPPLIES*1..6]->(p:Product) RETURN pathMapped 2,341 affected products across 6 tiers in 45ms
Network Analysis
IT Security & InfrastructureMap permission chains across your entire infrastructure. Answer "who can access what" through any chain of roles, groups, and service accounts.
MATCH (u:User)-[:MEMBER_OF*1..4]->(g:Group)-[:HAS_ACCESS]->(r:Resource {type:'prod'}) RETURN u, rIdentified 23 users with unintended production access in 8ms
Customer 360
Retail & E-commerceBuild a unified view of customer relationships across purchases, support tickets, social connections, and product interactions. Power recommendations and churn prediction.
MATCH (c:Customer)-[:PURCHASED]->(p:Product)<-[:PURCHASED]-(other:Customer)-[:PURCHASED]->(rec:Product) WHERE NOT (c)-[:PURCHASED]->(rec) RETURN recGenerated 15,000 personalised recommendations across 2M customers in 120ms
How Graph OLAP compares
The only solution that combines warehouse-native connectivity, real in-memory performance, and zero idle cost.
| Capability | Graph OLAP Open source | PuppyGraph SaaS only | Neo4j Separate DB | Raw SQL JOINs |
|---|---|---|---|---|
| Warehouse-native | ||||
| Multi-hop performance | 120,000x vs SQL | Slow (virtualised) | Fast (separate DB) | Exponential JOINs |
| Zero idle cost | ||||
| Per-analyst isolation | ||||
| Built-in graph algorithms | ||||
| Open source | Apache 2.0 | AGPL / Commercial | ||
| Self-hosted / VPC deploy | ||||
| No data duplication | ||||
| Kubernetes native |
Swipe to see all columns
Start free. Scale when ready.
The core engine is free and open source. Enterprise features and managed cloud are available for teams that need them.
Community
Self-hosted in your VPC. Full graph engine with zero restrictions.
- Full graph engine (FalkorDB + RyuGraph)
- 20+ built-in algorithms
- Visual graph explorer
- Warehouse connectors (Snowflake, BigQuery, Databricks)
- Kubernetes Helm charts
- Per-analyst isolated workspaces
- Zero idle cost (auto-suspend)
- Community support (GitHub)
- Apache 2.0 licensed
Enterprise
Self-hosted with SSO, RBAC, audit logs, and priority support.
- Everything in Community, plus:
- Single Sign-On (SAML / OIDC)
- Role-Based Access Control (RBAC)
- Audit logging & compliance reporting
- Priority support (SLA-backed)
- Custom connector development
- Deployment assistance
- SOC 2 Type II (in progress)
Managed Cloud
Fully managed. No Kubernetes. One-click deployment with usage-based pricing.
- Everything in Enterprise, plus:
- Fully managed — no Kubernetes to maintain
- One-click deployment
- Automatic scaling & upgrades
- 99.9% uptime SLA
- Usage-based pricing
Ready to see graph analytics in action?
Try the live demo with sample data, or deploy in your own infrastructure. Graph OLAP runs entirely inside your VPC — we never touch your data.
No account required. Sample data included. Deploy guide in the docs.