AllegroGraph (Franz) vs Neo4j
AllegroGraph (Franz) vs Neo4j: side-by-side comparison of two knowledge-graph platforms systems — architecture, taxonomy, license, pricing, MCP/A2A support, and direct edges.
Cost & capability
| AllegroGraph (Franz) | Neo4j | |
|---|---|---|
| Cost tier | free | free |
| $/Mtok input | 0 | 0 |
| $/Mtok output | 0 | 0 |
Where they differ (10)
Rows where both sides have data and the values disagree — the shortlist of dimensions that actually distinguish these two systems.
| AllegroGraph (Franz) | Neo4j | |
|---|---|---|
| Type | RDF triple/quad store + reasoning | Property graph + vectors + Aura Agent |
| Created | 1984 | 2024-12 |
| Pricing | Free tier + commercial licenses (quote-based) | Free + paid |
| Funding | No external funding — privately held since 1984 | $325M total $2.0B val Series F · 2021-06 |
| Backend storage | custom (RDF triple store) | custom (native graph store) |
| Deployment | Both (on-prem + cloud) | Both |
| API surface | REST, SPARQL, SDK: Java, Python, Lisp | Bolt, REST, GraphQL, Cypher, SDK: many |
| Multi-tenancy | Repository-level access control (statement-level Security Filters per user/role); on-prem only for full isolation | AuraDB Virtual Dedicated Cloud / AuraDS Enterprise: dedicated AWS/Azure/GCP account/subscription/project per customer; PrivateLink supported |
| MCP | no first-party MCP adapter published as of 2026-05; community connectors may exist. | via official adapter — neo4j-mcp |
| OpenTelemetry | no first-party OpenTelemetry exporter documented; standard logs/metrics typically available. | first-class — Neo4j metrics + OTel |
At a glance
| AllegroGraph (Franz) | Neo4j | |
|---|---|---|
| Section | Knowledge-graph platforms | Knowledge-graph platforms |
| Tier | T1 | T1 |
| Type | RDF triple/quad store + reasoning | Property graph + vectors + Aura Agent |
| Created | 1984 | 2024-12 |
| Latest release | — | mcp-neo4j-cyphe… 2026-04-10 |
| License | — | MIT |
| GitHub | — | 944★ +15/mo Python |
| Pricing | Free tier + commercial licenses (quote-based) | Free + paid |
| Funding | No external funding — privately held since 1984 | $325M total $2.0B val Series F · 2021-06 |
| Backend storage | custom (RDF triple store) | custom (native graph store) |
| Deployment | Both (on-prem + cloud) | Both |
| API surface | REST, SPARQL, SDK: Java, Python, Lisp | Bolt, REST, GraphQL, Cypher, SDK: many |
| Embedding | BYO | BYO |
| Multi-tenancy | Repository-level access control (statement-level Security Filters per user/role); on-prem only for full isolation | AuraDB Virtual Dedicated Cloud / AuraDS Enterprise: dedicated AWS/Azure/GCP account/subscription/project per customer; PrivateLink supported |
| MCP | no first-party MCP adapter published as of 2026-05; community connectors may exist. | via official adapter — neo4j-mcp |
| A2A | no Google A2A (Agent2Agent) integration documented as of 2026-05. | no Google A2A (Agent2Agent) integration documented as of 2026-05. |
| OpenTelemetry | no first-party OpenTelemetry exporter documented; standard logs/metrics typically available. | first-class — Neo4j metrics + OTel |
| Optimised for | relationship modeling + reasoning + governance over pure vector | relationship modeling + reasoning + governance over pure vector |
| Anti-fit | not for purely-vector or simple-RAG use cases (graph adds setup cost) | not for purely-vector or simple-RAG use cases (graph adds setup cost) |
Taxonomy
| Axis | AllegroGraph (Franz) | Neo4j |
|---|---|---|
| storage | graph | graph |
| retrieval | graph-traversal | graph-traversal |
| persistence | long-term | long-term |
| update | overwrite | overwrite |
| unit | fact | fact |
| governance | auditable | inspectable |
| conflict | overwrite | overwrite |
Pros & cons
AllegroGraph (Franz)
Pros: FedShard sharding architecture handles trillion-triple scale; strong RDF + reasoning support for semantic-web workloads.
Cons: Niche product with a small community; tooling and docs lag mass-market alternatives.
Neo4j
Pros: Largest graph database community by orders of magnitude; Cypher is the de-facto graph query language; strong native AI/RAG integrations.
Cons: Property-graph model can be awkward for RDF/semantic-web workloads; clustering and scale-out can get expensive.