Lv.2 BeginnerPostgreSQL / MongoDB

2026.05.2817 min readLv.2 Beginner

SeriesTransactions in PostgreSQL and MongoDB: What's the Difference? · Part 4View series hub

PostgreSQL vs MongoDB: A Practical Decision Guide for 2026 — Part 4

The final installment of a four-part series. Covers OLTP, OLAP, and write-heavy benchmark comparisons; vertical vs horizontal scaling strategies; pgvector vs Atlas Vector Search for AI workloads; licensing realities; and clear criteria for choosing the right database for your workload.

Series overview — Transactions in PostgreSQL and MongoDB: What's the Difference?

• Part 1 — Transaction Fundamentals and ACID Principles
• Part 2 — Deep Dive: PostgreSQL Transactions (MVCC, Isolation Levels, Deadlocks, VACUUM)
• Part 3 — MongoDB Transaction Evolution and Multi-Document Transactions
• Part 4 — Real-world Comparison: Performance, Scalability, and the 2026 Decision Guide (this article)

Table of Contents

1. Introduction — "Which one is better?" is the wrong question
2. Performance Benchmarks: Real numbers by workload
   • 2-1. OLTP (Transaction Processing)
   • 2-2. Complex Analytical Queries (OLAP)
   • 2-3. Write-Heavy Workloads
   • 2-4. Interpreting Benchmark Results
3. Scalability: Vertical vs Horizontal
   • 3-1. PostgreSQL Scaling Strategy
   • 3-2. MongoDB Scaling Strategy
4. The Vector Search Race in the AI Era of 2026
5. Licensing and Cost: The Reality You Cannot Ignore
6. Real-World Usage Patterns: Who Uses What
7. The Final Decision Guide: Which DB Fits Your Situation?
   • 7-1. Transaction Model Summary Comparison
   • 7-2. Scenario-Based Selection Criteria
   • 7-3. Decision Flowchart
8. Series Wrap-Up
9. Practical application notes

1. Introduction

Over four parts we have covered everything from transaction fundamentals and PostgreSQL's MVCC and VACUUM to MongoDB's WiredTiger engine and multi-document transactions. Now the real question is in front of us.

"So what should I actually use?"

In 2026, this question is significantly harder than it used to be. PostgreSQL is pushing into NoSQL territory with JSONB, pgvector, and partitioning, while MongoDB is targeting enterprise workloads with ACID transactions, vector search, and the Atlas platform. As the boundary blurs, the old "SQL vs NoSQL" framing is no longer useful.

This final part presents the latest benchmark figures, real enterprise adoption patterns, and a clear set of selection criteria.

2. Performance Benchmarks: Real Numbers by Workload

The most important thing to guard against in any performance comparison is the single, simple conclusion that "database X is faster." Workload characteristics can completely reverse the outcome.

Benchmark disclaimer: The figures below are drawn from public benchmarks and academic papers published by Percona, EDB (EnterpriseDB), and other major organizations in 2025–2026. Results can vary significantly depending on hardware, configuration, and query patterns.

2-1. OLTP (Transaction Processing)

On a standard OLTP environment (16-core CPU, 64 GB RAM, NVMe SSD), PostgreSQL 17 recorded 15,000–25,000 transactions per second on a mixed read/write workload.

In scenarios involving multi-document transactions, MongoDB incurs higher overhead than PostgreSQL because the transaction layer was added on top of an architecture not originally designed for it. In particular, under high concurrency (256 threads or more), MongoDB's p99 latency exceeded 250 ms, while PostgreSQL consistently stayed below 10 ms.

Concurrent transaction latency comparison (64 concurrent threads):

According to an e-commerce environment benchmark paper published in February 2026, PostgreSQL processed complex analytical queries 1.6–15.1x faster than MongoDB on the same dataset, and reduced execution time for multi-criteria filter queries by 65–80%.

2-2. Complex Analytical Queries (OLAP)

This is PostgreSQL's dominant domain. Multi-table joins, window functions, and CTEs (Common Table Expressions) give SQL a level of analytical expressiveness that MongoDB's aggregation pipeline cannot easily match.

-- PostgreSQL: monthly revenue by country with 3-month rolling average
SELECT
    u.country,
    DATE_TRUNC('month', o.created_at)    AS month,
    COUNT(*)                              AS order_count,
    SUM(o.total)                          AS revenue,
    AVG(SUM(o.total)) OVER (
        PARTITION BY u.country
        ORDER BY DATE_TRUNC('month', o.created_at)
        ROWS BETWEEN 2 PRECEDING AND CURRENT ROW
    )                                     AS rolling_3m_avg
FROM orders o
JOIN users u ON o.user_id = u.id
WHERE o.status = 'completed'
GROUP BY u.country, DATE_TRUNC('month', o.created_at);

// MongoDB: same query via aggregation pipeline (considerably more verbose)
db.orders.aggregate([
  { $match: { status: 'completed' } },
  { $lookup: {
      from: 'users', localField: 'user_id',
      foreignField: '_id', as: 'user'
  }},
  { $unwind: '$user' },
  { $group: {
      _id: {
        country: '$user.country',
        month: { $dateToString: { format: '%Y-%m', date: '$created_at' } }
      },
      order_count: { $sum: 1 },
      revenue:     { $sum: '$total' }
  }},
  // Rolling average requires $setWindowFields (MongoDB 5.0+)
  { $setWindowFields: {
      partitionBy: '$_id.country',
      sortBy: { '_id.month': 1 },
      output: {
        rolling_3m_avg: {
          $avg: '$revenue',
          window: { documents: [-2, 'current'] }
        }
      }
  }}
]);

2-3. Write-Heavy Workloads

MongoDB holds a clear advantage in certain areas. For write-heavy workloads such as simple document inserts, IoT telemetry, and clickstream data, MongoDB outperforms PostgreSQL.

MongoDB 8.0 achieved +54% bulk write throughput, +36% read throughput, and +59% update throughput compared to previous versions. Domains where MongoDB's strengths shine the most include event streaming with frequently changing schemas, dynamic product catalogs, and user behavior logs.

2-4. Interpreting Benchmark Results

3. Scalability: Vertical vs Horizontal

3-1. PostgreSQL Scaling Strategy

PostgreSQL's default strategy is vertical scaling (Scale Up) — throwing more powerful hardware (CPU, RAM, storage) at the problem. This approach is simple and preserves transactional consistency, but eventually hits hardware limits.

PostgreSQL horizontal scaling options:

Read Replica    — distribute reads (writes still go to Primary)
Partitioning    — table partitioning within a single node
Citus Extension — distributed PostgreSQL (horizontal scale-out)
Managed Cloud   — Supabase, Neon, AWS Aurora PostgreSQL

From a 2026 perspective, cloud services like Supabase and Neon have simplified PostgreSQL operations to a level comparable to MongoDB Atlas. The old perception that "MongoDB is easy to operate and PostgreSQL is hard" has largely faded.

3-2. MongoDB Scaling Strategy

MongoDB was designed from the start to support horizontal scaling (Scale Out) natively. Sharding distributes data automatically across multiple nodes.

Shard key design is the critical decision. A poorly chosen shard key creates hotspots where traffic concentrates on a single shard.

// Compound shard key example
sh.shardCollection(
  "iot.sensorData",
  { deviceId: 1, timestamp: 1 }
  // deviceId alone risks hotspots when the device count is small
  // timestamp alone causes monotonic growth, concentrating recent data on one shard
);

Scalability comparison:

4. The Vector Search Race in the AI Era of 2026

A significant new variable has entered the database selection conversation in 2026: AI workloads and vector search.

Both databases have begun embedding vector search natively, and the assumption that "AI applications require a dedicated vector database" is now under pressure.

PostgreSQL + pgvector

-- pgvector: relational filters combined with semantic search
CREATE EXTENSION vector;

CREATE TABLE products (
    id        SERIAL PRIMARY KEY,
    name      TEXT,
    price     NUMERIC,
    category  TEXT,
    embedding VECTOR(1536)  -- OpenAI text-embedding-3-small dimensions
);

SELECT name, price,
       1 - (embedding <=> '[0.1, 0.2, ...]'::vector) AS similarity
FROM products
WHERE price < 100000
  AND category = 'electronics'
ORDER BY embedding <=> '[0.1, 0.2, ...]'::vector
LIMIT 10;

pgvector 0.8 (released September 2025) significantly improved ANN (Approximate Nearest Neighbor) search accuracy. The pgvectorscale extension recorded 471 QPS on 50 million vectors, reaching a level competitive with some dedicated vector databases — though this figure is a benchmark claim for a specific configuration and is not a universal guarantee.

MongoDB Atlas Vector Search

// MongoDB Atlas Vector Search: metadata filter + vector search
db.products.aggregate([
  {
    $vectorSearch: {
      index:         "product_embedding_index",
      path:          "embedding",
      queryVector:   [0.1, 0.2],
      numCandidates: 100,
      limit:         10,
      filter: {
        price:    { $lt: 100000 },
        category: "electronics"
      }
    }
  }
]);

MongoDB Atlas Vector Search was substantially enhanced in MongoDB 8.0. For RAG (Retrieval-Augmented Generation) pipelines centered on unstructured text content, the document model fits naturally.

AI vector search selection guide:

5. Licensing and Cost: The Reality You Cannot Ignore

Licensing matters as much as technical capability.

SSPL note: MongoDB's SSPL poses no issue for typical internal use. However, if you offer MongoDB functionality as a cloud service to third parties, you may be required to open-source all related management code. This is the source of friction between MongoDB and major cloud providers who offered compatible managed services.

PostgreSQL has no such restrictions. Anyone can build a commercial product on top of PostgreSQL.

6. Real-World Usage Patterns: Who Uses What

Companies that chose PostgreSQL

Companies that chose MongoDB

A notable pattern: Polyglot Persistence

Large-scale services rarely use a single database.

Core transactions (orders, payments, inventory)   → PostgreSQL
Dynamic content, catalogs, user profiles          → MongoDB
Caching, sessions                                 → Redis
Search                                            → Elasticsearch or pgvector / Atlas Search
AI embeddings                                     → pgvector or Atlas Vector Search

An interesting 2026 trend is the consolidation movement. As major cloud companies have acquired PostgreSQL-based businesses, the "just use PostgreSQL for everything" approach has grown in appeal — handling vector search, queues, and full-text search through PostgreSQL extensions to reduce operational complexity.

7. The Final Decision Guide: Which DB Fits Your Situation?

7-1. Transaction Model Summary Comparison

A single-place summary of the key transaction differences covered across all four parts.

7-2. Scenario-Based Selection Criteria

Choose PostgreSQL when:

Finance, fintech, accounting    — strictest ACID and audit trail are non-negotiable
Complex joins and aggregations  — analytical systems, BI, reporting
Strict schema requirements      — ERP, CRM, regulated industries (healthcare, finance)
SQL-proficient team             — projects with long maintenance horizons
SaaS backends                   — operational simplicity via Supabase, Neon
AI applications                 — relational data + vector search together (pgvector)

Choose MongoDB when:

Schema changes frequently       — early-stage startups, rapid prototyping
IoT telemetry                   — different payload structures per device
Content management systems      — each document has distinct attributes
Real-time event streaming       — write-dominant workloads
Global distributed services     — native sharding for regional horizontal scale-out
JS / TS teams                   — BSON document model maps naturally to application code
RAG pipelines                   — AI services centered on unstructured content

Consider both databases when:

Microservices          — fundamentally different data shapes per service
Platform services      — core transactions (PostgreSQL) + flexible catalog or reviews (MongoDB)

7-3. Decision Flowchart

8. Series Wrap-Up

Four parts — a long journey now complete. Here is the distilled essence.

The Philosophical Difference in Transactions

PostgreSQL = "Every data operation is a transaction."
              ACID is the default; developers get it without thinking about it.

MongoDB    = "A well-designed document makes transactions unnecessary."
              Transactions are a tool to reach for explicitly, when needed.

Core Selection Principles

1. Consistency vs Flexibility: Data integrity above all else → PostgreSQL. Schema flexibility above all else → MongoDB.
2. Query Complexity: Many complex joins and analytical queries → PostgreSQL. Simple document reads and writes dominate → MongoDB.
3. Scaling Direction: Vertical scaling + SQL ecosystem → PostgreSQL. Global horizontal scale-out is mandatory → MongoDB.
4. Team Expertise: SQL-proficient team → PostgreSQL. NoSQL / JS team → MongoDB.
5. Discard the myths: "MongoDB has no transactions," "PostgreSQL cannot scale" — both statements belong to a world that ended before 2018.

The 2026 Final Verdict

Both databases have matured and are absorbing each other's strengths. The question is not "which one is better" but "which one fits my workload and team." For most general web and application backends, PostgreSQL remains the safer default choice in 2026. MongoDB earns its place when the data schema is fluid, writes are explosive, and horizontal scale-out is a hard requirement.

Full Series Summary

References

• tech-insider.org: MongoDB vs PostgreSQL 2026 (April 2026)
• Airbyte: MongoDB vs. PostgreSQL (2025)
• MDPI Big Data & Cognitive Computing: PostgreSQL vs MongoDB E-Commerce Benchmark (Feb 2026)
• leanware.co: MongoDB vs PostgreSQL (Mar 2026)
• knowi.com: MongoDB vs PostgreSQL Updated 2026
• DEV.to: What's Changing in Vector Databases in 2026
• singhajit.com: When to Use PostgreSQL vs MongoDB vs DynamoDB (Mar 2026)
• EnterpriseDB: Performance Comparison MongoDB vs PostgreSQL