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DirectoryData & AnalyticsDatabase PlatformsApache AGE

Apache AGE

Open Source

About Apache AGE

Apache AGE aims to provide graph data processing and analytics capability to relational databases. It gives PostgreSQL users access to graph query modeling within the existing relational database. Users can read and write graph data as nodes and edges. Users can apply algorithms such as variable length and edge traversal when analyzing data. It is compatible with PostgreSQL's distributed assets and uses graph data structures to analyze relationships and patterns in data.

Used for

  • Analyzing relationships and patterns in data
  • Reading and writing graph data in nodes and edges
  • Traversing edges and variable length paths when analyzing data
  • Querying graph databases using SQL extensions similar to Cypher
  • Transforming a PostgreSQL database into a graph database

Who it is for

  • PostgreSQL users seeking graph query modeling within their existing relational database.

From Apache AGE's own website, read 2026-10-23. How we build listings.

How to evaluate Database Platforms

This is how the CIOPages Research Team evaluates this category. It is not an assessment of Apache AGE. It comes from our Enterprise Database Platforms buyer guide.

25%
Data Model & Query Fit
Match of the engine to the workload: relational integrity and joins, document/JSON flexibility, key-value latency; transaction isolation levels and ACID guarantees; SQL standard coverage and dialect quirks; secondary indexes, full-text and vector (pgvector / Atlas Search) support; foreign keys and referential integrity where you need them
20%
Consistency, Scale & HA
Single-primary vs. multi-writer; synchronous vs. async replication and resulting RPO; read-replica and sharding story; horizontal write scale and global/multi-region behavior (strong vs. eventual consistency); failover time, achievable RTO, and how partitions are handled under load
20%
Operability & Managed-Service Experience
Quality of the managed offering (RDS/Aurora, Azure SQL, Cloud SQL/AlloyDB) vs. self-managed burden; in-place and major-version upgrade path; backup/PITR, observability, and connection pooling; autoscaling and serverless options; day-2 toil — vacuum/compaction, index bloat, rebalancing — and the depth of the DBA skills it demands
15%
Licensing & Portability
License class and its real constraints: OSI open source (PostgreSQL, MySQL, Valkey, Redis 8 AGPL) vs. source-available (MongoDB SSPL, CockroachDB enterprise/BSL) vs. proprietary (Oracle, SQL Server); cloud lock-in of proprietary engines (Aurora, Spanner, DynamoDB); wire-protocol and SQL-dialect compatibility that preserves an exit; audit exposure on proprietary metrics
12%
Security & Compliance
Encryption at rest and in transit, TLS enforcement, and key management (BYOK/HSM); row- and column-level security and fine-grained RBAC; field-level / queryable encryption for sensitive data; audit logging; data-residency controls and certifications (SOC 2, ISO 27001, HIPAA, PCI, FedRAMP) on the managed service
8%
Ecosystem & Talent
Driver and ORM maturity across your languages; migration and CDC tooling (logical replication, Debezium, DMS); breadth of extensions and integrations; size and hireability of the talent pool; quality of docs and the support path — first-party vendor vs. third-party (EDB, Percona, Crunchy) for open engines

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Quick Facts

age.apache.org
CategoryData & Analytics
SubcategoryDatabase Platforms

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