MongoDB vs PostgreSQL: Which Handles GenAI Workloads Better?

Generative AI (GenAI) applications use data differently from traditional online transaction processing (OLTP) workloads. While OLTP processes small, row‑oriented records, large language model (LLM) training requires rich document structures or binary objects that can be several kilobytes in size.

Retrieval‑augmented generation (RAG) is a common technique in GenAI that builds prompts from complex data structures and generates responses in real time. AI‑driven workloads demand that the underlying database handle such queries quickly and efficiently.

Traditional relational database management systems (RDBMS) were not designed for these workloads; their storage engines assume uniform row sizes and perform best with typed record rows. Best practices for RDBMS include minimizing the number of columns, using vertical partitioning, keeping rows small, and avoiding out‑of‑row storage. JSON columns can provide flexible schemas within relational tables, but they still inherit the row‑oriented limitations.

Document‑oriented NoSQL databases have emerged as a better fit for GenAI data. In open‑source PostgreSQL, the recommendation is to limit the number of JSON/JSONB columns and use sparse fill objects when necessary.

Compared to PostgreSQL, MongoDB’s storage engine is built to handle documents ranging from a few bytes to several megabytes, making it naturally suited for large, complex documents.

Modern OLTP workloads increasingly store large objects out‑of‑row, but PostgreSQL’s TOAST mechanism can become a performance bottleneck when document sizes exceed roughly 2 KB, because queries must retrieve data from the secondary storage layer.

MongoDB stores data in BSON, a binary JSON format that retains strong typing without requiring server‑side parsing, whereas PostgreSQL stores JSON as text (or JSONB as binary) and must parse or validate the data on the server.

Benchmark Setup

Hardware: Windows 10 Pro, 32 GB RAM, Intel Core i7‑8700K @ 3.70 GHz.

Software: PostgreSQL v16.2 and MongoDB v7.0.8.

Tests were run single‑threaded to isolate client‑server protocol overhead.

Insert Test – Single‑Attribute Documents

10,000 documents were inserted with payload sizes ranging from 10 B to ~4 KB. MongoDB uses BSON, avoiding serialization overhead, while PostgreSQL stores JSON or JSONB, incurring parsing costs.

10K Record Insert (Single‑Attribute Payload)

          MongoDB   Postgres (JSONB)   Postgres (JSON)

n=1, s=10      773          399                331
n=1, s=200    789         2184                969
n=1, s=1000   750         8393               4071
n=1, s=2000   850        16387               7944
n=1, s=4000   829        31705              15767

Results show PostgreSQL matches MongoDB for very small documents, but performance degrades sharply once payloads exceed a few hundred bytes due to TOAST activation.

Insert Test – Multi‑Attribute Documents

10,000 documents with multiple attributes were inserted, with total payloads again ranging from 10 B to ~4 KB.

10K Item Insert (Multi‑Attribute Payload)

          MongoDB   Postgres (JSONB)   Postgres (JSON)

n=10, s=10    531          415                322
n=10, s=200   569         2040                793
n=50, s=1000  641         9504               4419
n=100, s=2000 812        19213              9181
n=200, s=4000 1085       37278              17460

JSONB’s parsing overhead grows with the number of attributes, while MongoDB’s BSON remains consistently fast.

Read Test – Array Index Queries

Indexes were created on an array of 10 integer values selected from the inserted document IDs. PostgreSQL created indexes on the row itself, not on the JSON/JSONB document, to keep the comparison fair.

Array Index Query Test (10 K Docs)

          MongoDB   Postgres (JSONB)   Postgres (JSON)

n=10, s=10    3587        19933              18749
n=10, s=200   3810        23619              23946
n=50, s=1000  4741        27760              21311
n=100, s=2000 6023        36701              23264
n=200, s=4000 8352        53808              27789

Both JSON and JSONB incur significant serialization overhead during reads, whereas BSON avoids this entirely, giving MongoDB a clear advantage.

Conclusion

RDBMSs like PostgreSQL struggle with the wide rows and large data attributes required by generative AI workloads. Document‑oriented databases such as MongoDB provide superior performance for storing and retrieving complex, variable‑size documents.