Open-Source & Self-Hosted Alternatives to Neo4j Aura &amp; Enterprise

✅ Advantages

Fastest graph traversal performance in the market - 10x faster than Neo4j for real-time queries
OpenCypher compatibility eliminates rewrites when migrating from Neo4j
Native Kafka streaming enables real-time fraud detection and recommendation pipelines

⚠️ Trade-offs / Limitations

BSL-1.1 license restricts competitive SaaS deployment without commercial agreement
Primary memory-bound architecture requires sizing RAM to full graph working set

Core Features

▸Sub-millisecond query latency on billions of edges via native in-memory columnar storage

▸Full openCypher query compatibility - drop-in replacement for Neo4j Cypher workloads

▸Built-in streaming ingestion from Apache Kafka, Redpanda, and Pulsar topics

▸Real-time graph algorithms: PageRank, BFS/DFS, community detection, shortest path

▸Storage-procedure extensions in Python, C++, and Rust for custom query logic

▸High availability clustering with automatic leader election and data replication

▸Graph Data Science library with 50+ pre-built analytics algorithms

Architecture Notes

C++ high-performance in-memory graph engine with a native property graph data model. Implements the openCypher query language for broad compatibility with Neo4j tooling. Stores data in an in-memory columnar format with on-disk durability via write-ahead logging (WAL). Features a built-in stream processor that ingests from Kafka, Pulsar, and Redpanda topics in real-time, automatically triggering Cypher triggers on new data.

Known Limitations

BSL-1.1 license requires purchasing a commercial license for production SaaS offerings. In-memory architecture means total graph size is bounded by available RAM unless using experimental on-disk storage.

📄 docker-compose.yml

Production Ready

version: '3.8'
services:
  memgraph:
    image: memgraph/memgraph-platform:latest
    container_name: memgraph
    restart: always
    ports:
      - "7687:7687"
      - "7444:7444"
      - "9090:9090"
    volumes:
      - memgraph_data:/var/lib/memgraph
    command: ["--log-level=INFO", "--data-directory=/var/lib/memgraph"]
    networks:
      - selfhost_net

  memgraph-lab:
    image: memgraph/lab:latest
    container_name: memgraph-lab
    restart: always
    ports:
      - "3000:3000"
    environment:
      - QUICK_START=true
      - MEMGRAPH_HOST=memgraph
      - MEMGRAPH_PORT=7687
    depends_on:
      - memgraph
    networks:
      - selfhost_net

volumes:
  memgraph_data:

networks:
  selfhost_net:
    external: true

🚀 5-Minute Deployment Guide

1Provision a VPS with at least 4GB RAM and 2 vCPUs (e.g. Hetzner CPX21).
2Install Docker: `curl -fsSL https://get.docker.com | sh`.
3Create config directory: `mkdir -p /opt/memgraph && cd /opt/memgraph`.
4Save the docker-compose.yml file above.
5Launch Memgraph: `docker compose up -d`.
6Open Memgraph Lab at `http://your-server-ip:3000` and connect to `bolt://memgraph:7687`.
7Run test query: `CREATE (n:Person {name: 'Alice'}); MATCH (n) RETURN n;` to verify.

Recommended Cloud VPS for Memgraph

Hetzner Cloud€7.05/mo

CPX21 (3 vCPU, 4GB RAM, 80GB NVMe)

Excellent price/performance for in-memory graph workloads up to 2B relationships.

DigitalOcean$48.00/mo

General Purpose Droplet (4 vCPU, 8GB RAM, 50GB SSD)

Comfortable headroom for graph analytics and streaming workloads.

Claim $200 DO Credit →

NebulaGraph

Apache-2.0⭐ 12.4k+

Distributed native graph database with openCypher, linear scalability to trillions of edges, and RAFT consensus.

Min RAM4 GB

Min CPU4 vCPU

✅ Advantages

Truly distributed: scales horizontally to petabytes without single-node memory limits
Apache-2.0 license allows unrestricted commercial use and SaaS deployment
Strong consistency via RAFT replication across storage partitions

⚠️ Trade-offs / Limitations

Multi-service architecture (graph/storage/meta) increases operational complexity
OpenCypher support is not 100% compatible with all Neo4j Cypher features

Core Features

▸Shared-nothing distributed architecture scales linearly to trillions of edges across commodity nodes

▸openCypher query language with Go/Match/Lookup/Traversal query patterns

▸Native graph partitioning across storage replicas with RAFT consistency

▸Multi-tenancy with graph-space isolation for multi-project deployments

▸Full-text indexing and vector indexing for hybrid search queries

▸Spark/Flink connector for batch and streaming graph ETL pipelines

▸NebulaGraph Explorer web UI for visual graph exploration and schema management

Architecture Notes

Distributed graph database built on the RocksDB key-value store with a native graph storage layer. Follows a shared-nothing architecture with separate Graph, Storage, and Meta services that scale independently. Uses RAFT consensus for storage partition replication and supports openCypher query language with NebulaGraph-specific extensions (GO, LOOKUP, PIPE). Data is hash-partitioned across storage replicas for linear horizontal scalability.

Known Limitations

Requires at least 3 services (graph, storage, meta) for production clustering. openCypher support covers ~80% of the Cypher specification with some Neo4j-specific functions unavailable.

📄 docker-compose.yml

Production Ready

version: '3.8'
services:
  nebula-graphd:
    image: vesoft/nebula-graphd:latest
    container_name: nebula-graphd
    restart: always
    ports:
      - "9669:9669"
      - "19670:19670"
    command: ["--flagfile=/etc/nebula/nebula-graphd.conf"]
    volumes:
      - ./nebula-conf:/etc/nebula
    depends_on:
      - nebula-metad
      - nebula-storaged
    networks:
      - selfhost_net

  nebula-metad:
    image: vesoft/nebula-metad:latest
    container_name: nebula-metad
    restart: always
    command: ["--flagfile=/etc/nebula/nebula-metad.conf"]
    volumes:
      - nebula_meta_data:/data
      - ./nebula-conf:/etc/nebula
    networks:
      - selfhost_net

  nebula-storaged:
    image: vesoft/nebula-storaged:latest
    container_name: nebula-storaged
    restart: always
    command: ["--flagfile=/etc/nebula/nebula-storaged.conf"]
    volumes:
      - nebula_storage_data:/data
      - ./nebula-conf:/etc/nebula
    depends_on:
      - nebula-metad
    networks:
      - selfhost_net

volumes:
  nebula_meta_data:
  nebula_storage_data:

networks:
  selfhost_net:
    external: true

🚀 5-Minute Deployment Guide

1Provision a VPS with at least 4GB RAM and 4 vCPUs for single-node deployment.
2Install Docker: `curl -fsSL https://get.docker.com | sh`.
3Create config directory: `mkdir -p /opt/nebulagraph/{nebula-conf,data}`.
4Download sample config files from the NebulaGraph GitHub repository into `nebula-conf/`.
5Launch all services: `docker compose up -d`.
6Connect via console: `docker exec -it nebula-graphd nebula-console -u root -p nebula`.
7Create a graph space: `CREATE SPACE IF NOT EXISTS test (partition_num=10, replica_factor=1);` and verify with `SHOW SPACES;`.

Recommended Cloud VPS for NebulaGraph

Hetzner Cloud€7.49/mo

CX31 (2 vCPU, 8GB RAM, 80GB NVMe)

Minimum viable single-node NebulaGraph with storage and graph services.

DigitalOcean$48.00/mo

General Purpose Droplet (4 vCPU, 8GB RAM, 160GB SSD)

Good SSD I/O for RocksDB-based graph storage engine.

Claim $200 DO Credit →

Apache AGE

Apache-2.0⭐ 4.9k+

PostgreSQL extension that adds native graph database capabilities with openCypher query support and ACID transactions.

Min RAM2 GB

Min CPU2 vCPU

✅ Advantages

Reuses your existing PostgreSQL instance, backup pipeline, and monitoring stack
Strong fit for hybrid workloads: combine SQL JOINs with graph traversals in a single query
Apache-2.0 license allows unrestricted commercial use including SaaS resale

⚠️ Trade-offs / Limitations

Traversal performance on deep multi-hop queries is slower than Memgraph or NebulaGraph
Single-node operation unless you set up standard Postgres streaming replication

Core Features

▸Native openCypher query support inside standard psql sessions

▸Hybrid relational + graph storage on a single PostgreSQL instance

▸Full ACID transaction semantics inherited from PostgreSQL MVCC

▸Graph-aware operators (MATCH, MERGE, CREATE, shortest path) alongside SQL

▸Compatible with PostgreSQL replication, backups, and pg_basebackup

▸Zero additional operational overhead: graph capabilities ride on existing Postgres ops stack

▸Apache-2.0 licensed with no per-node or per-relationship metering

Architecture Notes

Graph database extension that turns PostgreSQL into a hybrid relational + graph engine. Adds the AGE (AgensGraph Extended) graph data type, openCypher query support, and graph-aware operators directly inside the Postgres executor. Stores graph data in regular PostgreSQL tables using a vertex/edge schema with label-based polymorphism. Inherits PostgreSQL's WAL, MVCC, replication, and backup tooling without operational overhead.

Known Limitations

Graph queries run through the Postgres planner, so traversal performance is slower than purpose-built graph engines on deep traversals. OpenCypher coverage is solid but not 100% of the spec. Single-node by default (Postgres replication applies).

📄 docker-compose.yml

Production Ready

version: '3.8'
services:
  postgres-age:
    image: apache/age:latest
    container_name: postgres-age
    restart: always
    environment:
      - POSTGRES_USER=graph
      - POSTGRES_PASSWORD=graphpass
      - POSTGRES_DB=graphdb
    ports:
      - "5432:5432"
    volumes:
      - age_data:/var/lib/postgresql/data
      - ./age-init:/docker-entrypoint-initdb.d
    networks:
      - selfhost_net

volumes:
  age_data:

networks:
  selfhost_net:
    external: true

🚀 5-Minute Deployment Guide

1Provision a VPS with at least 2GB RAM and 2 vCPUs (e.g. Hetzner CPX11).
2Install Docker: `curl -fsSL https://get.docker.com | sh`.
3Create directory: `mkdir -p /opt/age && cd /opt/age` and mount the official `apache/age` image.
4Save the docker-compose.yml above and create `age-init/01-age.sql` with `CREATE EXTENSION age;` and `LOAD 'age';`.
5Launch: `docker compose up -d`.
6Connect: `psql -h localhost -U graph -d graphdb` then run `SELECT * FROM cypher('graph', $$ CREATE (n:Person {name: 'Alice'}) RETURN n $$) as (n agtype);` to verify.
7Add graph data using standard `MATCH ... RETURN ...` syntax inside regular SQL queries.

Recommended Cloud VPS for Apache AGE

Hetzner Cloud€4.39/mo

CPX11 (2 vCPU, 2GB RAM, 40GB NVMe)

Minimum viable deployment for small to mid-size hybrid relational + graph workloads.

DigitalOcean$48.00/mo

General Purpose Droplet (4 vCPU, 8GB RAM, 160GB SSD)

Comfortable headroom for millions of vertices and edges alongside existing relational tables.

Claim $200 DO Credit →

JanusGraph

Apache-2.0⭐ 5.8k+

Distributed open-source graph database under The Linux Foundation, pluggable storage backends (Cassandra, HBase, ScyllaDB), and Apache TinkerPop Gremlin query language.

Min RAM4 GB

Min CPU2 vCPU

✅ Advantages

Most flexible storage backend choice in the open-source graph database space
Linux Foundation governance and Apache-2.0 license ensure long-term vendor neutrality
Battle-tested at LinkedIn, Netflix, and other hyperscaler production deployments

⚠️ Trade-offs / Limitations

Requires running a separate storage cluster (Cassandra or HBase) for production deployments
Gremlin traversal language has a steeper learning curve than openCypher or SQL

Core Features

▸Pluggable storage backends: Cassandra, HBase, ScyllaDB, or BerkeleyDB

▸Apache TinkerPop Gremlin graph traversal language with deep ecosystem support

▸Vertex-centric indexes via Elasticsearch or OpenSearch for fast property lookups

▸Built-in adaptive query optimizer for mixed OLTP/OLAP traversal patterns

▸Apache-2.0 licensed under The Linux Foundation with no per-node metering

▸Scales horizontally to hundreds of billions of edges via consistent hash partitioning

▸Native integration with Apache Spark for batch graph analytics

Architecture Notes

Distributed graph database that separates compute and storage layers via pluggable backends. Uses Apache TinkerPop Gremlin as the standard graph traversal language, decoupling graph semantics from storage mechanics. Supports Cassandra, HBase, ScyllaDB, or BerkeleyDB for graph persistence and Elasticsearch/OpenSearch for vertex-centric indexing. Designed for OLTP workloads on graphs with hundreds of billions of edges across commodity clusters.

Known Limitations

Operates as a layer over a separate storage cluster (Cassandra/HBase), adding operational complexity for self-hosters unfamiliar with those systems. Gremlin is more verbose than openCypher and has a steeper learning curve.

📄 docker-compose.yml

Production Ready

version: '3.8'
services:
  janusgraph:
    image: janusgraph/janusgraph:latest
    container_name: janusgraph
    restart: always
    ports:
      - "8182:8182"
    environment:
      - JANUS_PROPERTIES_TEMPLATE=berkeleyje
      - gremlinserver.graphManager=org.janusgraph.graphdb.management.JanusGraphManager
    volumes:
      - janusgraph_data:/var/lib/janusgraph
    networks:
      - selfhost_net

volumes:
  janusgraph_data:

networks:
  selfhost_net:
    external: true

🚀 5-Minute Deployment Guide

1For production deploy Apache Cassandra or ScyllaDB alongside JanusGraph; BerkeleyDB backend is fine for single-node evaluation.
2Provision a VPS with at least 4GB RAM and 2 vCPUs (e.g. Hetzner CPX21).
3Install Docker: `curl -fsSL https://get.docker.com | sh`.
4Create directory: `mkdir -p /opt/janusgraph && cd /opt/janusgraph`.
5Save the docker-compose.yml above for the BerkeleyDB single-node evaluation profile.
6Launch: `docker compose up -d` and wait for the Gremlin server port 8182 to come up.
7Connect: `docker exec -it janusgraph bin/gremlin.sh` then run `:remote connect tinkerpop.server conf/remote.yaml` and `g.addV('person').property('name','alice').iterate()` to verify.

Recommended Cloud VPS for JanusGraph

Hetzner Cloud€7.05/mo

CPX21 (3 vCPU, 4GB RAM, 80GB NVMe)

Single-node JanusGraph with BerkeleyDB backend for evaluation workloads up to 10M edges.

Hetzner Cloud€29.00/mo

CCX23 (4 vCPU, 16GB RAM, 160GB NVMe)

Production deployment with separate Cassandra/ScyllaDB storage and Elasticsearch indexing tier.