Features
A data expert ranks the nine best multi-model databases for 2025, comparing features like global distribution, query flexibility, pricing, and developer tooling to help teams pick the right engine.
The best multi-model databases in 2025 are Azure Cosmos DB, ArangoDB, and Couchbase Capella. Azure Cosmos DB excels at low-latency global distribution; ArangoDB offers unified document–graph queries; Couchbase Capella is ideal for edge-ready deployments with SQL++ access.
Multi-model databases store and query more than one data model—such as document, graph, key-value, relational, or time-series—in a single engine.
This versatility lets teams consolidate infrastructure and build hybrid workloads without ETL, driving faster development and lower operational cost.
We evaluated engines released or materially updated in 2025 using seven weighted criteria: feature breadth (20%), performance and reliability (20%), developer experience (15%), integration ecosystem (15%), pricing and TCO (10%), support and community (10%), and strategic momentum (10%).
Scores came from vendor benchmarks, independent tests, official docs, and verified user reviews.
The top multi-model databases this year are Azure Cosmos DB, ArangoDB, Couchbase Capella, MarkLogic 11, Amazon DynamoDB, OrientDB 4, Redis Enterprise 2025, Fauna 2025, and DataStax Astra DB.
Each shines for specific workloads and budgets.
Azure Cosmos DB leads with turnkey global distribution, <40 ms read latency, automatic sharding, and native APIs for document, graph, key-value, table, and relational data. A new 2025 “Burst IOPS” tier slashes cost for spiky traffic.
Downsides are vendor lock-in to Azure and RU-based billing complexity.
ArangoDB 3.12 adds OneShard clusters that hit 1 M ops/s while AQL queries traverse documents and graphs in one statement. Free Community edition suits startups, while ArangoGraph SaaS offers elastic billing. Limited enterprise support hours may slow mission-critical teams.
Couchbase Capella 2025 ships “Capella App Services” for offline-first sync between devices and the cloud.
SQL++ unifies JSON, KV, and search queries, and 99.99% uptime SLA rivals hyperscalers. Memory footprints remain high, impacting small clusters.
MarkLogic 11’s multi-model index blends documents, triples, and relational views, now with built-in vector search for GenAI use cases.
Government-grade security and ACID transactions stand out, but proprietary licensing keeps costs premium.
DynamoDB added open JSON-schema validation and graph adjacency lists in 2025, widening its key-value/document focus. On-demand capacity removes planning headaches, yet advanced queries need extra AWS services like Athena.
OrientDB 4 rebuilds the storage engine for twice the throughput and delivers ANSI SQL-2025 coverage across document and graph models.
Community momentum has waned, so enterprises often self-support.
Redis Enterprise bundles RedisJSON, RedisGraph, and RedisSearch with automatic tiered memory, enabling sub-millisecond reads across models. It shines for real-time analytics but lacks built-in ACID multi-document transactions.
Fauna’s FQL 5.0 merges document and relational patterns with full ACID consistency and a generous free tier.
The serverless pay-per-query model is budget-friendly, though cold starts still add single-digit-millisecond overhead.
Astra DB overlays Cassandra with JSON columns, CQL, REST, GraphQL, and now vectors. Transparent multi-region replication and open-source roots reduce lock-in, but eventual consistency may deter strict-ACID workloads.
By collapsing disparate stores, multi-model engines cut cross-service latency, simplify consistency models, and slash license fees.
They let engineers join graph relationships and JSON attributes in one query, shaving weeks off development.
Match data patterns to dominant models, test latency under realistic traffic, model cloud egress fees, and validate enterprise features like role-based access.
Pilot two finalists side-by-side for 30 days to measure DevEx and ops load.
Galaxy’s modern SQL editor and AI copilot connect to the multi-model engines above, letting engineers craft complex queries faster, share vetted snippets, and keep documentation up to date. Collections and endorsements ensure team-wide alignment on cross-model SQL.
No. Modern multi-model stores like Azure Cosmos DB and ArangoDB use model-specific indexes, keeping performance on par with single-model peers while adding flexibility.
Export collections as JSON, load them into the target multi-model engine, then run native migration tools to generate graph edges. Validate with dual-writes before cut-over.
Yes. Galaxy connects to every database on this list via standard drivers, offering a desktop IDE, context-aware AI copilot, and shared query collections—streamlining multi-model SQL development.
Fauna 2025 and ArangoDB Community are popular with startups because they provide generous free tiers and pay-per-query or pay-per-node pricing that scales linearly with usage.
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