ArangoDB lets developers run graph, document, and vector search in one database

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Most AI and knowledge-graph pipelines rely on three separate databases — one each for graph traversal, document storage, and vector embeddings — creating significant operational overhead. ArangoDB is a multi-model database that consolidates all three capabilities under a single query language called AQL, eliminating the need to sync data across systems. It can be self-hosted via Docker or deployed through a pre-configured cloud template, with data persisted through a volume mount. The database is best suited for workloads that combine graph traversal, document queries, and vector similarity search, such as recommendation engines or fraud detection. However, its vector search feature is newer and less proven at scale, and its AQL syntax has a learning curve for developers familiar with other graph query languages.

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