# Which products combine graph and document database capabilities for a team that needs both relationship traversal and flexible schema storage in one system?

<p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">Which products combine graph and document database capabilities for a team that needs both relationship traversal and flexible schema storage in one system? Most teams that arrive at this question have already tried using a dedicated graph database alongside a document store and discovered what that actually costs: two query languages, two operational surfaces, two sets of connection logic, and data that has to stay in sync across both. A multi-model system that handles graph traversal and document storage in one engine is a different architectural bet, and the question is whether the products that offer it actually deliver on both sides rather than doing one well and the other adequately.</p><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">Looking specifically at the <a class="a a--md" elv="true" href="https://www.g2.com/categories/document-databases">document databases</a> category with this lens, here is what teams building graph-plus-document workloads seem to need:</p><ul>
<li>A single query language that handles both relationship traversal and document retrieval without switching contexts</li>
<li>Graph performance that does not degrade into full collection scans when the traversal depth increases</li>
<li>Enough document model flexibility to store semi-structured data alongside the graph relationships</li>
</ul><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">What the reviews show:</p><ul>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/arango/reviews"><strong>Arango</strong></a><strong>:</strong> This is what the product is specifically built for. Reviewers describe using a single query language, AQL, to handle both document retrieval and graph traversal in a single query, which is the core of what the multi-model promise means in practice. One team in a telco context described using it to build and maintain a dynamic model of their entire network topology as a unified landscape, which required both relationship traversal and flexible state storage in one place. Another team is building a clinical knowledge graph described using Arango to handle ontology searches, transactional data, and data science source data with a single data store and a lean team.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/mongodb-atlas/reviews"><strong>MongoDB Atlas</strong></a><strong>: </strong>Handles the document side well, but relationship traversal is handled through the $lookup operator rather than native graph capabilities. Reviewers who need complex multi-hop traversals describe pushing that logic into application code rather than the database, which means the graph layer is not truly native.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/azure-cosmos-db/reviews"><strong>Azure Cosmos DB</strong></a><strong>: </strong>Supports multiple APIs, including a Gremlin API for graph queries alongside the document API. Reviewers in Azure-heavy stacks describe using different API endpoints for different access patterns, though maintaining that across a single dataset adds operational complexity.</li>
</ul><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">For teams that evaluated multi-model databases specifically for this combination, what was the performance characteristic that actually made the decision?</p>

##### Post Metadata
- Posted at: 17 days ago
- Author title: Writer
- Net upvotes: 1


## Comments
### Comment 1

A quick way to decide if you need this at all: count the hops in your actual queries. If relationships go one or two levels deep, like order to customer to region, a document database with lookups is fine and simpler. The multi-model systems earn their keep when the depth is variable or unknown, like &quot;find everyone connected to this account within five steps.&quot; That&#39;s the query shape that falls apart in a document store, and if you don&#39;t have it, you may not have a graph problem.

##### Comment Metadata
- Posted at: 14 days ago
- Author title: Tech Consultant





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