There is a recurring dilemma in data engineering: choosing between PostgreSQL’s proven reliability and MongoDB’s flexible document model. The decision often leads to costly migration cycles as teams discover limitations only after implementation.
Teams initially choosing PostgreSQL often migrate to MongoDB seeking schema flexibility and cloud-native features like Atlas triggers and APIs. Conversely, teams starting with MongoDB frequently return to PostgreSQL after encountering document size constraints, transaction limitations, or sharding complexity.
These migration cycles typically stem from insufficient upfront evaluation of each database’s strengths and limitations for specific use cases. The costs extend beyond technical debt: migration projects consume engineering resources, introduce system instability, and delay feature development.
This analysis provides enterprise decision-makers with a comprehensive comparison of PostgreSQL and MongoDB across critical dimensions: ACID compliance, scalability, schema design, security, and total cost of ownership.
Brief introduction to PostgreSQL and MongoDB
Although it’s common for data engineers to debate the choice between PostgreSQL and MongoDB, it requires recognizing that these represent fundamentally different database paradigms, not just competing products within the same category.
PostgreSQL is a relational database that stores data in structured rows and columns with strong schema enforcement, enhanced by robust JSON support for semi-structured data.
MongoDB is a document-oriented NoSQL database that stores data as BSON (Binary JSON) documents with flexible schema requirements.
Before choosing between two, consider making a decision about using a relational vs non-relational database.
We shared our thoughts on the matter in an earlier blog post. Nevertheless, a few ideas are AdTech-specific; most reflections are generally valid across domains.
PostgreSQL
PostgreSQL is one of the longest-running relational databases out there, developed back in the 1980s. It strongly follows SQL standards but expands upon them with additional features like custom data typing, object-oriented support, functions, and, more recently, JSON support.
Over nearly forty years on the market, PostgreSQL has become one of the most robust open-source relational databases.
Most enterprise companies, including Apple, Walmart, and Instagram, use PostgreSQL.
MongoDB
MongoDB emerged during the NoSQL movement with the premise that many applications could benefit from document-based data models rather than rigid relational schemas. The founders argued that JSON-like documents provide more intuitive data representation for modern applications.
This claim is now widely disputed among data engineers, who argue that all data should be treated as relational data in the long run. Still, MongoDB’s claim got attention and led to a fair share of enterprise companies migrating to the new database. Electronic Arts and Samsung are among MongoDB adopters.
Although the number of PostgreSQL proponents seems to be growing, it’s difficult to draw a clear line and claim that it is “better” than MongoDB. Only by understanding your use case and the key technical characteristics of both databases can enterprise teams make informed decisions.
Key differences between MongoDB and PostgreSQL: Detailed comparison
Besides obvious differences like relational and non-relational data type support and different query languages, this comparison focuses on critical dimensions that directly impact application performance, compliance requirements, and operational costs.
- ACID compliance and transaction guarantees
- Scalability architectures and performance characteristics
- Data recovery and backup capabilities
- Extension ecosystems and feature expansion
- Schema design approaches and data modeling flexibility
We did our best to keep these observations accurate at the time of writing (September 2025), but they may change over time with new versions of both databases.
ACID compliance and transaction handling
ACID, a shorthand for atomicity, consistency, isolation, and durability, defines how databases ensure data integrity during transaction processing
Atomicity ensures transactions execute as indivisible units; either all operations succeed or all fail, preventing partial updates that could corrupt data integrity even during system failures or power outages.
Consistency makes sure that there’s no invalid data in the database. All data in the database has to comply with a set of rules, constraints, and cascades. In a consistent database, transactions run with no missing steps, and all data is homogeneous.
Isolation prevents concurrent transactions from interfering with each other, enabling multiple users to modify data simultaneously without conflicts. Different isolation levels (serializable, snapshot, repeatable read) provide varying degrees of protection.
Durability guarantees that committed transactions survive system failures through persistent storage mechanisms, ensuring no data loss after successful transaction completion.
PostgreSQL: Built-in ACID guarantees
PostgreSQL implements full ACID compliance by design, making it the standard choice for applications requiring strict transaction integrity. This native ACID support has established PostgreSQL as the preferred database for financial systems, healthcare applications, and other regulated environments where data consistency is non-negotiable.
MongoDB: ACID evolution from BASE origins
MongoDB originally followed BASE principles (Basically Available, Soft state, Eventually consistent) that prioritized system availability over immediate consistency:
Basically available: Systems remain accessible during partial failures, allowing some operations while others might be temporarily unavailable
Soft state: Data consistency may change over time without external input as the system processes pending updates
Eventually consistent: the record will stay consistent only after completing all updates. In simpler terms, it means that all concurrent edits made by users will eventually merge and propagate across the database.
In its earlier days, MongoDB had no ACID compliance, which is why data engineers saw it as a less reliable option for applications in regulated domains like banking and healthcare.
Since MongoDB’s v.4.0, released in 2018, there’s both ACID compliance and support for multi-document transactions. Note that a standard practice is not to process over 1000 documents per transaction since MongoDB has a 16 MB document size cap.
Still, considering that Postgres is ACID-core, engineers still keep it as a go-to choice for finance and banking transactions, also because this data is usually relational.
MongoDB’s BASE properties, on the other hand, are helpful when the use case requires managing spikes of high data, think real-time AdTech applications or e-commerce products.
Query languages and data access patterns
PostgreSQL uses SQL as its query language but adds new features on top: inheritance, functions, extensible types, and others.
The PostgreSQL dialect of SQL is compatible with the standard version, so engineers can use them interchangeably.
MongoDB’s query language is MQL (MongoDB Query Language). It is designed specifically for non-relational databases and provides native support for:
- Document-based queries and filtering
- Aggregation pipelines for complex data processing
- Built-in text search via $text operator on self-managed deployments and Atlas Search in MongoDB Atlas
The query language choice often depends on team expertise: SQL skills are more widely available, while MQL requires document-database-specific training.
Data types and JSON handling capabilities
MongoDB stores documents in BSON (a binary JSON-like format) with a few native types, like Date, Int32/Int64, Decimal128, ObjectId, and Binary. This document-centric approach treats JSON as the fundamental data structure rather than an add-on feature.
PostgreSQL originally supported the standard array of data types used in relational databases: integers, dates, text, binary fields, IP-related data, and encrypted passwords.
The addition of JSONB support (PostgreSQL 9.4, 2014) and subsequent SQL/JSON standard compliance significantly expanded PostgreSQL’s semi-structured data capabilities.
The JSONB vs native document debate
PostgreSQL’s JSONB implementation has sparked considerable discussion about whether dedicated document databases remain necessary.
This Reddit comment sums up the common trajectory of going with PostgreSQL with JSONb over MongoDB – there is more use-case-specific advice in a similar vein.
Use PostgreSQL’s JSONB column.. You can dump some nested JSONs in there. I’ve used it before, and it is better than MongoDB.
Although this is a common view, it ignores potential scalability issues that appear when users try to dumb millions of data rows in the JSONb column.
I’m trying to do a group by, and it’s so slow that I can’t get it to finish, e.g., waiting for 30 min. I have an items table, and need to check for duplicate entries based on the property referenceId in the JSONb column… The table has around 100 million rows.
Reddit post describing a JSONb performance issue
Technical performance differences
PostgreSQL JSONB faces several limitations when handling document-heavy workloads.
Index-only scans require all query columns to be available in the index; complex JSON path queries may require expression indexes, and group operations on JSONB fields can become prohibitively slow at scale. Mixed relational-document queries add complexity to query planning that can impact performance.
There are possible solutions to this problem, but these workarounds are less efficient compared to using MongoDB for this use case. PostgreSQL developers themselves acknowledge indexing shortcomings in the DB’s documentation:
“PostgreSQL’s planner is currently not very smart about such cases. It considers a query to be potentially executable by index-only scan only when all columns needed by the query are available from the index.”
MongoDB’s native document architecture avoids these issues through purpose-built document indexing that doesn’t require expression definitions, efficient sorting and aggregation on nested document fields, and a query planner optimized specifically for document operations rather than adapted from relational query planning.
When each approach works best
PostgreSQL works best for applications with primarily structured data that occasionally need JSON storage for configuration or metadata. It’s also great when you need to mix SQL queries with document searches, or when your team already knows SQL really well. JSONB works best as a supplement for configuration data or metadata, not as the main way you store your data.
MongoDB makes more sense when JSON documents are basically your whole data model. If you’re constantly querying lots of documents and need that to be fast, or if your data structure changes frequently, MongoDB handles these situations better. It’s built specifically for document work rather than trying to fit documents into a table-based system.
The choice ultimately depends on whether JSON handling represents a core requirement or supplementary feature for your application architecture.
Database schema and ERD
A schema, an outline of how data is organized and structured in the database, creates a scaffold that shows relationships between database entries and enforces data integrity. The most common way to represent a data schema is an ERD, an entity-relationship diagram, that shows how tables relate to each other.
PostgreSQL implements schemas through traditional relational design principles. Tables follow predefined structures with explicit column definitions, data types, and relationship constraints.
The introduction of JSONB columns allows PostgreSQL to accommodate semi-structured data while maintaining its core relational integrity. This hybrid approach enables teams to store occasional flexible data within a predominantly structured environment, keeping the overall schema comprehensible and maintainable.
MongoDB initially marketed itself as “schemaless,” which created confusion among developers who needed to understand and communicate their data structures.
The MongoDB team clarified that the database offers “schema flexibility, not schema absence.” This means developers can implement varying levels of structural enforcement, from minimal constraints that allow maximum flexibility to strict validation rules that ensure data governance at enterprise scale.
Nonetheless, developers are not always fond of MongoDB’s flexibility.
For instance, default MongoDB settings will let errors like mispelled column names run but yield no result, whereas PostgreSQL will raise a SQLSTATE 42703 error by default.
Since the release of version 3.2. MongoDB supports schema validation and can reject invalid writes, but that requires a deeper understanding of the system and a dedicated setup of validationAction: “error”.
In practice, many development teams continue using default settings without comprehensive validation, which can lead to data inconsistencies and difficult-to-debug application issues.
Scalability
Enterprise applications demand databases that can grow with increasing user loads, data volumes, and transaction throughput.
Both PostgreSQL and MongoDB provide scalability mechanisms, though they take fundamentally different architectural approaches to handling growth.
Horizontal scaling through sharding
PostgreSQL does not offer sharding out of the box, but it is easy to set up via extensions like Citus DB.
Citus transforms PostgreSQL into a distributed database while maintaining ACID guarantees and SQL compatibility. Teams can start with a single instance and add sharding when growth demands it, without changing application code.
MongoDB offers built-in sharding, where data automatically partitions across servers based on shard keys, with configuration servers managing metadata and routing. This enables transparent data distribution from the application perspective.
The key difference: PostgreSQL treats sharding as optional, while MongoDB builds it into the core architecture.
Load balancing and read scaling
PostgreSQL uses external tools for load balancing. Connection poolers like PgBouncer manage connections, while streaming replication enables read replicas. This requires additional infrastructure but offers deployment flexibility. Writes concentrate on the primary server, with reads distributed across replicas.
In MongoDB, load balancing is part of the deployment topology. Teams can use official drivers to set up server selection and implement read preferences. Similar to PostgreSQL, engineers can send reads to a secondary server while write loads go to the primary server.
MongoDB also offers data rebalancing as a first-class feature, making it easier to distribute reads and writes as part of the default architecture.
Operational considerations
PostgreSQL lets you add scaling features as you need them, which keeps things simple at first. But as you grow, you’ll need to learn how to manage several different extensions. MongoDB comes with scaling built in, so you don’t need as many separate tools.
However, you have to understand how to choose the right “shard key”; this is really important because a bad choice can create performance bottlenecks.
Both databases can handle large enterprise workloads, but they require different skills from your team. With PostgreSQL, you need people who understand the extension ecosystem. With MongoDB, you need people who understand distributed databases and how to design good shard keys.
Extensions
A large library of third-party extensions is an important advantage PostgreSQL has over MongoDB.
PostgreSQL’s robust community has created thousands of extensions (like the Citus extension for sharding mentioned above) that help add new features to the standard functionality.
Setting up a third-party add-on is fairly straightforward; engineers simply need to download the provided Linux packages and don’t have to modify the core database code.
This means you can start with a basic PostgreSQL setup and add features as needed.
Key PostgreSQL extensions
Citus enables sharding and introduces horizontal scalability to PostgreSQL. It helps spread the database across multiple physical machines while still keeping management centralized.
PostGIS is the world’s leading geospatial database containing advanced datatypes and operators. It’s a go-to extension for data engineers who build localization-based features (e.g., a US map of high-yield segments for audience targeting based on census data).
HyperLogLog supports count preaggregation and a wide range of added operations: intersections, unions, and many more. It is often used for big data applications and distributed systems.
MongoDB’s extension landscape
MongoDB doesn’t have a similar extension ecosystem. The way MongoDB is built and licensed hasn’t encouraged the same kind of community development that PostgreSQL enjoys.
In the engineering community, it’s common to discuss PostgreSQL emulations in MongoDB, but these are MongoDB alternatives rather than true extensions, such as FerretDB, which translates the MongoDB protocol to PostgreSQL.
Data recovery
Both MongoDB and PostgreSQL handle backups at the block level and the logical level (with pg_dump and mongodump).
The key operational difference appears during backup operations. MongoDB requires exclusive access during backup mode, blocking concurrent write operations to ensure consistency.
PostgreSQL maintains full read-write availability during backup and recovery operations, minimizing downtime for mission-critical applications.
PostgreSQL also supports incremental back-ups that allow continuous recovery and archiving. MongoDB, at the time of writing, does not have incremental backups out of the box. To set them up, engineering teams need to upgrade to the enterprise version or look for third-party tools.
It’s important to note that MongoDB requires engineers to back up each shard independently, whereas PostgreSQL’s Citus extension allows consistent backups across the cluster, which is a simpler orchestration mechanism.
Here’s the summary of key PostgreSQL and MongoDB features and key differences.
When to use PostgreSQL or MongoDB?
The choice between PostgreSQL and MongoDB used to be simple: if you are working with relational data (i.e., a table), go with an SQL database like PostgreSQL.
If you are working with documents and prefer using JSON as your default data type, a NoSQL database is the right fit, and MongoDB may be your best choice.
However, now the two types of databases are merging to support both relational and non-relational data types. And when these solutions look very much alike, the choice becomes more granular.
PostgreSQL: The recommended starting point
Overall, seems like data engineers favor PostgreSQL for new projects, particularly for teams building their first production systems.
While PostgreSQL requires more structured thinking about data modeling, this constraint encourages good database design practices that benefit long-term maintainability.
PostgreSQL has several practical advantages: it’s completely open-source, so you’re not locked into any vendor, every cloud provider supports it well, and you can add new features through extensions as your needs grow.
The learning curve is steeper at first, but the SQL skills you develop work with almost every other database system.
MongoDB: When you need specific performance characteristics
MongoDB’s scalability strengths, like out-of-the-box sharding, vector search, and partitioning, earn the DB a place in data stacks that deliver a combination of high performance and low latency.
High-speed applications that need to handle massive traffic can use MongoDB’s built-in data distribution. For example, in AdTech and media, MongoDB supports hundreds of thousands of QPS by distributing user profile reads and writes across multiple regions.
Gaming platforms need extremely fast response times – under 10 milliseconds – to update player information without affecting other players. MongoDB’s document structure and fast writes make this possible.
IoT systems collecting data from many different types of sensors benefit from MongoDB’s flexible structure. You don’t need to know exactly what data format each sensor will send, and MongoDB can store time-based data efficiently.
E-commerce sites can use MongoDB’s built-in search and recommendation features without installing additional software, which would be necessary with PostgreSQL.
Security and compliance factors
PostgreSQL has a stronger reputation for security, especially in highly regulated industries like healthcare and finance. It has mature tools for data encryption and detailed audit logging that these industries require.
MongoDB has improved its security significantly, but it has had some data exposure problems in the past. Both platforms, using MongoDB and MongoDB’s own systems, have experienced unauthorized access incidents.
In 2016, GeekedIn, a platform matching companies and engineers, created a MongoDB security breach that leaked the data of over 8 million GitHub profiles.
In 2023, MongoDB itself grappled with a data leak that revealed the metadata and contact information of hundreds of its customers.
If you’re handling sensitive data or need to meet strict compliance requirements, PostgreSQL’s proven security track record usually makes it the safer choice for enterprise use.
The bottom line
The PostgreSQL vs MongoDB decision depends on your application’s specific requirements and your team’s technical expertise.
PostgreSQL works best when you want a database that can grow with lots of add-on features, has reliable ways to back up your data, and guarantees that your transactions won’t get corrupted. It’s built on solid SQL foundations, which makes it great for applications that need consistent data and complex queries that connect different pieces of information.
MongoDB is a solid choice when your application is built around storing documents and needs to handle huge amounts of traffic. It can automatically spread your data across multiple servers and lets you change your data structure easily as your application evolves.
What kind of data are you storing? If it’s mostly structured information that connects to other data, PostgreSQL is probably better. If you’re working with documents that change format often, MongoDB might be the way to go.
How fast do you need to scale? MongoDB gives you scaling tools right away. PostgreSQL lets you add them later when you actually need them.
What does your team know? If your developers are comfortable with SQL, PostgreSQL will be easier. If they understand document databases, MongoDB makes more sense.
Do you have compliance requirements? Industries like healthcare and finance often prefer PostgreSQL because it has a proven track record for security and compliance.
Successful database selection requires matching technical capabilities to your specific use case, growth projections, and team expertise rather than following technology trends.