Vector Databases · ArcadeData

arcadedb

ArcadeDB is a multi-model database that handles SQL, graphs, documents, key-value, time-series, and vector embeddings in a single system. Built in Java with an emphasis on performance and written by the founder of OrientDB, it supports Cypher, Gremlin, MongoDB, and Redis protocols alongside native SQL.

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Source: GitHub — github.com/ArcadeData/arcadedb

GitHub stars

119

Forks

Java

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	ArcadeData/arcadedb
Owner	ArcadeData
Primary language	Java
License	Apache-2.0 — OSI-approved
Stars	1k
Forks	119
Open issues	97
Latest release	26.7.1 (2026-07-01)
Last updated	2026-07-08
Source	https://github.com/ArcadeData/arcadedb

What arcadedb is

Multi-model DBMS written in Java 17+ using Low-Level Java for reduced GC pressure and mechanical sympathy. Supports ACID transactions, graph algorithms (70+), parallel query execution, materialized views, columnar time-series with Gorilla compression, geospatial indexing, and vector similarity search. Accessible via HTTP/JSON, Postgres Wire protocol, Redis protocol, MongoDB driver compatibility, and embedded JVM access.

Quickstart

Get the arcadedb source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/ArcadeData/arcadedb.git2cd arcadedb3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Knowledge Graphs & Recommendation Engines

Native graph engine with 70+ built-in algorithms (pathfinding, centrality, community detection), combined with vector embeddings and time-series for recommendations and citation/co-authorship network analysis.

Real-time IoT & Time-Series Analytics

Columnar time-series with Gorilla compression, InfluxDB/Prometheus ingest protocols, PromQL support, and Grafana integration for monitoring and analytics on high-volume sensor or event data.

Polyglot Query Systems (Graph RAG, Fraud Detection)

Single database supporting graph traversal (Cypher/Gremlin), document queries (MongoDB compatibility), full-text search, and vector similarity in one transaction context—ideal for fraud detection and retrieval-augmented generation pipelines.

Implementation considerations

Java 17+ runtime required; verify JVM memory tuning for your dataset size and query complexity (especially graph traversals and vector searches).
Multi-model schema design requires upfront thought: decide which models (graph vs. document vs. time-series) serve each entity and how they interrelate within transactions.
Protocol/driver compatibility (MongoDB, Redis, Postgres) is partial; review specific operations you need against documented support to avoid surprises.
Vector embeddings and similarity search integrate but assume external embedding generation (not built-in); integrate with your ML pipeline.
Materialized views and pre-computed graph algorithms reduce query latency but require maintenance planning as data changes.

When to avoid it — and what to weigh

Exclusively RDBMS Workloads — If you need only traditional relational tables with complex joins and no graph/document/vector requirements, purpose-built RDBMS (PostgreSQL, MySQL) may offer better tooling and narrower operational surface.
Non-JVM Embedded Deployments — Core engine is Java-only; while Python bindings exist, production embedding outside JVM is not a first-class concern. Non-JVM stacks require network access.
Minimal Resource Constraints Without Testing — Marketing claims 'Raspberry Pi to cloud,' but actual resource requirements for your workload (especially with graphs/vectors) must be validated. No public benchmarks provided in DATA.
Strict Commercial Compliance Without Review — Apache 2.0 is permissive, but implications for closed-source derivatives and enterprise indemnification should be reviewed with legal counsel before production commitment.

License & commercial use

Apache License 2.0 (Apache-2.0): permissive open-source license. Allows commercial use, modification, and distribution with minimal restrictions (must include license copy and state changes). No royalties or permission required.

Apache 2.0 explicitly permits commercial use, modification, and closed-source derivatives. No additional license fees or proprietary restrictions. However, consult legal counsel regarding your indemnification, warranty, and support obligations when deploying in production; the project offers no formal SLA or commercial support tier in the provided DATA.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Clear
Deployment complexity	Moderate
DEV.co fit	Good
Assessment confidence	Medium

Security considerations

Built-in ACID transactions and user management implied ('advanced security' mentioned). Requires review: authentication/authorization scopes, encryption at rest/in-transit, audit logging, and vulnerability patching cadence. Meterian security badge present but score not quantified. No penetration test or security audit report in DATA. Production deployment should include security review and dependency scanning.

Alternatives to consider

Neo4j

Purpose-built graph database with Cypher, mature HA/cluster support, and enterprise editions. Lacks multi-model (document, time-series, vector) in core; better if graphs are primary concern and you accept single-model lock-in.

PostgreSQL + PostGIS + pgvector

MongoDB (+ separate vector DB)

Industry-standard document database with Sharding, Atlas cloud offering, and ecosystem maturity. Lacks native graph engine and time-series optimization; vector search is newer; favored if document model dominates and you can accept polyglot architecture.

Software development agency

Build on arcadedb with DEV.co software developers

Start with the arcadedb-usecases repository for runnable Docker examples, then validate performance on your workload. Engage the community on Discord or StackOverflow, and consult legal on Apache 2.0 commercial implications before production deployment.

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arcadedb FAQ

Can I use ArcadeDB for a production recommendation system?

Yes, it has graph algorithms, vector embeddings, and time-series—all in one transaction. Validate performance (no published benchmarks in DATA) and confirm vector embedding generation is externalized. Test HA setup if high availability is required.

Is there commercial support?

Not stated in provided DATA. Project is open-source (Apache 2.0); support channels (Discord, StackOverflow) are community-based. Contact ArcadeData directly to inquire about consulting or enterprise support options.

Does it run on non-JVM languages?

Core engine is Java-only. Python bindings exist (arcadedb-embedded-python) for embedded use. Remote access via HTTP/JSON, Postgres Wire, MongoDB, or Redis protocols supports any language. Choose embedded for performance, remote for language flexibility.

How does it compare to specialized time-series databases like InfluxDB?

ArcadeDB supports InfluxDB ingest and PromQL but is a generalist multi-model system. InfluxDB is purpose-built for time-series and likely more optimized for high-cardinality metrics. Choose ArcadeDB if you need graphs + documents + time-series in one; else specialized tools may have better ops maturity.

Work with a software development agency

Adopting arcadedb is usually one piece of a larger software development effort. As a software development agency, DEV.co provides software development services and web development expertise — pairing senior software developers and web developers with your team to design, build, and operate vector databases software in production.

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