Best Document Databases (2025)
Ranked picks for document databases. No "it depends."
🧊Nice Pick
Amazon DynamoDB
AWS's NoSQL workhorse: scales like a dream, but you'll pay for every query and pray you never need a JOIN.
Full Rankings
#1
Details →Amazon DynamoDB
Nice PickAWS's NoSQL workhorse: scales like a dream, but you'll pay for every query and pray you never need a JOIN.
Pros
- +Fully managed with automatic scaling and multi-AZ replication
- +Single-digit millisecond latency for key-value operations
- +Built-in security, backup, and in-memory caching with DynamoDB Accelerator (DAX)
Cons
- -Pricing model can get expensive with high throughput or large datasets
- -Limited query flexibility compared to relational databases (no JOINs, complex queries)
The database for when you want to store JSON and pretend it's a schema.
Pros
- +Flexible schema allows rapid prototyping and iteration
- +Native JSON-like document storage fits well with modern web apps
- +Horizontal scaling with sharding is straightforward
- +Aggregation pipeline is powerful for complex queries
Cons
- -Lack of enforced schema can lead to messy data over time
- -Joins are clunky compared to relational databases
Compare:vs Amazon DynamoDB
The search engine that thinks it's a database. Great for logs, but good luck with transactions.
Pros
- +Blazing-fast full-text search and analytics
- +Scalable and distributed by design
- +Rich ecosystem with Kibana for visualization
Cons
- -Not ACID-compliant, so avoid for transactional data
- -Can be resource-hungry and complex to tune
Google's real-time database that makes syncing feel like magic, until you hit the query limits.
Pros
- +Real-time data synchronization out of the box
- +Offline support for mobile and web apps
- +Automatic scaling with minimal operational overhead
- +Seamless integration with Firebase and Google Cloud services
Cons
- -Query limitations can be restrictive for complex data structures
- -Costs can escalate quickly with high read/write volumes
AWS's NoSQL powerhouse that scales like a dream but makes you think in keys and indexes.
Pros
- +Serverless architecture with automatic scaling
- +Single-digit millisecond latency for most operations
- +Built-in backup and point-in-time recovery
- +Seamless integration with other AWS services
Cons
- -Pricing can be unpredictable with high throughput
- -Limited query flexibility compared to relational databases
Head-to-head comparisons
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