Compare graph database services (Neptune, Cosmos DB Gremlin, Spanner Graph, OCI Graph Studio).
Output will appear here...The comparison table is a static, hand-maintained dataset of feature rows grouped by category (overview, query, storage, compute, availability, pricing) with free-text search across all fields; it's a reference snapshot, not a live specs feed, verify current storage limits, query language support, and pricing directly with the provider before a database selection decision.
A comparison of graph database options across Amazon Neptune (a purpose-built property graph and RDF engine), Azure Cosmos DB's Gremlin API (a multi-model database with graph as one of several API surfaces), GCP's Spanner Graph or managed Neo4j AuraDB, and OCI's Property Graph feature built into Autonomous Database, and the fundamental architectural difference matters for query language portability: Neptune and Cosmos DB both speak Apache TinkerPop Gremlin, making cross-provider Gremlin query portability realistic between those two, while GCP's options split between GQL/SQL-PGQ (Spanner Graph) and Cypher (Neo4j Aura) and OCI uses PGQL, meaning a graph query written for one of these three often needs a genuine rewrite, not just a syntax tweak, to run on another.
Query language compatibility, not raw feature parity, is usually the deciding factor in a graph database migration, check whether your existing Gremlin/Cypher/PGQL queries need a full rewrite before comparing providers on storage limits or pricing alone.
Neptune's SPARQL and openCypher support alongside Gremlin gives it broader query-language flexibility than Cosmos DB's Gremlin-only API, worth weighing if a team anticipates needing RDF/SPARQL-style semantic graph queries down the line, not just property-graph traversals.
A workload evaluated purely as 'we need a graph database' sometimes turns out better served by a relational database with recursive CTEs for shallow traversal depth, before committing to any of these four, confirm the actual traversal depth and pattern complexity genuinely needs a dedicated graph engine.
Largely yes for standard TinkerPop Gremlin traversals, both implement the Gremlin query language, though provider-specific extensions, performance characteristics, and certain advanced features (like Neptune's openCypher and SPARQL support, which Cosmos DB's Gremlin API doesn't offer) mean full portability isn't guaranteed for anything beyond fairly standard traversal patterns. Test the specific query set against both before assuming a clean migration.
Conceptually similar (both support property-graph modeling and traversal), but architecturally different, Spanner Graph is graph capability built into Spanner's distributed relational engine (accessed via GQL or ISO SQL-PGQ), while Neptune is a purpose-built graph engine from the ground up. For teams already invested in Spanner's global consistency model, Spanner Graph avoids standing up a separate database; for graph-workload-first use cases, a purpose-built engine like Neptune may have more mature graph-specific tooling.
It's a feature within Oracle Autonomous Database, not a standalone service, so an organization already running Autonomous Database can add graph capability without provisioning a new separate database product, which is a meaningfully different adoption path than standing up Neptune, Cosmos DB, or Spanner Graph as dedicated new services.
Was this tool helpful?
Disclaimer: This tool runs entirely in your browser. No data is sent to our servers. Always verify outputs before using them in production. AWS, Azure, and GCP are trademarks of their respective owners.