# 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: 2 months ago
- Author title: Writer
- Net upvotes: 1


## Comments
### Comment 1

&lt;p&gt;Arango is purpose-built for this; the thread confirms it handles both in a single query language. But the real question is whether your team has graph query expertise.&amp;nbsp;&lt;/p&gt;

##### Comment Metadata
- Posted at: 6 days ago
- Author title: Marketing Executive



### Comment 2

&lt;p&gt;&lt;span style=&quot;background-color: transparent; color: rgb(0, 0, 0);&quot;&gt;For us it was being able to run one AQL query that handled both the traversal and the document lookup together. Not having to stitch results from two separate systems in application code was the deciding factor.&lt;/span&gt;&lt;/p&gt;

##### Comment Metadata
- Posted at: 7 days ago
- Author title: SEO Content Writer



### Comment 3

&lt;p&gt;Traversal performance as relationship depth increased would decide it for me. If multi-hop queries stay predictable without pushing traversal logic back into application code, the multi-model architecture is actually doing its job. Arango sounds strongest on that test because graph traversal and document retrieval can stay within the same AQL query.&lt;/p&gt;

##### Comment Metadata
- Posted at: 10 days ago



### Comment 4

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: 2 months ago
- Author title: Tech Consultant





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