PostgreSQL vs MongoDB: Which is better for web apps?
Choosing a database shouldn't be based on vibes. We compare speed and JSONB flexibility to help you pick the right engine for your next application.
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The 2026 verdict is that Postgres has essentially consumed the document-store use case for 90% of web apps. It is the boring, reliable, and shockingly fast choice that survives every trend.
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How Postgres and Mongo DB differ and how they are the same. What solution is better for web apps?
Foire aux questions
In tests involving one million rows, PostgreSQL 17 handled complex analytical aggregations nearly three times faster than MongoDB 8.0. This performance gap is largely due to PostgreSQL’s sophisticated cost-based query planner, which is designed to manage complex relationships and joins with high efficiency. While MongoDB’s aggregation pipelines have improved, they often struggle with operations that span multiple entities because the engine was originally optimized for single-record isolation rather than complex relational stitching.
The two systems use different storage mechanics that impact performance during write-heavy workloads. PostgreSQL uses Multi-Version Concurrency Control (MVCC), which creates a new version of an entire row even when only a small field within a JSONB document is updated, leading to "write amplification" and increased CPU usage. In contrast, MongoDB’s WiredTiger engine can often modify specific fields within a BSON document without rewriting the entire record, resulting in steadier throughput and more predictable latency during sustained update stress tests.
The hybrid model involves using PostgreSQL to store core entities in strict relational columns while using the JSONB data type for flexible metadata or variable attributes. This approach allows developers to benefit from relational integrity and ACID transactions for critical data, like user IDs and emails, while maintaining the "escape hatch" of document flexibility for parts of the data that may change frequently. This strategy was highlighted as a way to avoid "schema regret," where a purely flexible database becomes a disorganized collection of inconsistent records.
MongoDB is considered a "horizontal scaling specialist" because it was built with native sharding as a core feature, making it easier to distribute data across multiple machines or geographic regions. This makes it a superior choice for apps dealing with hundreds of terabytes of data or high-volume IoT telemetry. While PostgreSQL can scale vertically with more RAM and faster drives, or horizontally through specific extensions and managed services, MongoDB’s built-in ability to handle massive write-heavy volumes across clusters remains its primary architectural advantage.
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"BeFreed turned my guilty doomscrolling into something that feels productive and inspiring."
"BeFreed turned my commute into learning time. 20-min podcasts are perfect for finishing books I never had time for."
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"Instead of endless scrolling, I just hit play on BeFreed. It saves me so much time."
"I never knew where to start with nonfiction—BeFreed’s book lists turned into podcasts gave me a clear path."
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"Feels effortless compared to reading. I’ve finished 6 books this month already."
"BeFreed turned my guilty doomscrolling into something that feels productive and inspiring."
"BeFreed turned my commute into learning time. 20-min podcasts are perfect for finishing books I never had time for."
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![[PDF] PostgreSQL 16.13 Documentation](/_next/image?url=https%3A%2F%2Fd1y2du6z1jfm9e.cloudfront.net%2Fassets%2Fpodcast%2Fpurple.png&w=1024&q=50)
