Every generation of technology proposes shortcuts in search of a way to move faster with less complexity. But time and again, these shortcuts aren’t really shortcuts at all. Instead, they postpone difficult choices that must be thoughtfully made and undone with significant cost. In the 2010s, MongoDB was such a shortcut.

When companies need to get started quickly with flexible options, MongoDB seemed to make sense. Consider Mechademy who monitors assets for some of the world's largest oil, gas, and energy companies. Mechademy initially selected MongoDB for building digital twins, a virtual representation of physical assets for optimizing performance. MongoDB allowed for quick iterations on changing data structures without rigid schema constraints. Combined with their MERN stack, Mechademy could move fast, prototype ideas quickly, and focus on data orchestration rather than database design.

But that flexibility introduced unpredictable costs because it incurred technical debt. As Mechademy scaled, the data model itself became the bottleneck. Workarounds resulted in deeply nested aggregation pipelines that became increasingly fragile and expensive to operate. Technology that was selected because it enabled fast flexible iteration now required constant tuning and maintenance to stay performant.

As Mechademy’s diagnostic workloads scaled, MongoDB’s resource utilization skyrocketed. Even for small tenants processing around 10,000 tests every half hour, CPU utilization hovered above 95%. Each new diagnostic capability demanded more complex queries and higher performance thresholds, leading to an unsustainable cycle of scaling and reengineering.

The freedom initially offered by MongoDB had become a trap.

The Trap of Flexibility Without Structure

MongoDB’s NoSQL schemaless design at first feels liberating. Add fields whenever you want. Change data types without migrations. Skip the upfront design and proceed without friction.

But documents drift, types diverge, and queries slow down. What initially feels like speed becomes fragile later on, until production debugging means digging through JSON blobs, and performance tuning feels like a guessing game.

When collections are isolated and untyped, data doesn’t compound. Each dataset becomes its own island. Postgres, by contrast, uses schemas and relationships to make data more valuable together than apart. That’s why SQL queries can grow more sophisticated over time, while MongoDB queries often collapse under their own weight.

Flexibility always comes at a cost which can be seen as undefined technical debt or unexpected operational burden. You might not know how big the cost is or when it will be necessary to pay until it's too late.

Bolting On What Should Have Been Core

Why is scaling with MongoDB such a challenge? Every time the market demands new functionality, MongoDB has chosen to bolt-on features rather than engineer new core foundational updates, which makes implementation and scaling increasingly complex.

Let's consider a few examples of MongoDB’s approach:

Transactions: Added by MongoDB decades after relational systems perfected them. Transactions in MongoDB work, but at a performance penalty that makes them impractical for serious, high-volume workloads.
Analytics: MongoDB’s aggregation pipelines look neat in a demo. In real workloads, they’re verbose and brittle, a hundred lines of transformations that break the moment the shape of your documents change. Teams end up exporting data to Spark, warehouses, or custom pipelines just to answer questions.
Time-series: MongoDB markets “time-series collections,” but in reality, the collections are nothing more than a patch on a document store. Compression is weak. Retention is manual. There’s no equivalent of incremental materialized views.
Observability: Search and graph were layered in, too, but on top of an architecture that wasn’t designed for them. The result is surface-level features that don’t scale deeply in practice.
Query language: MongoDB Query Language (MQL) locks you into a custom syntax that only the Mongo-trained team can use, rather than encouraging cross-team collaboration using standard SQL for complex queries across different databases.

Each of these is a patch to address specific customer demands rather than a database built for architectural scaling. NoSQL doesn’t have a future in a merged relational/analytics environment. MongoDB can add features, but it can’t change the fact that its core architecture wasn’t designed for modern workloads.

Operational Burden vs Operational Ease

The real cost of MongoDB isn’t just performance pain, it’s the ongoing burden of running it at scale. MongoDB suffers from index bloat, constant aggregation maintenance, and risky upgrades because features were bolted-on rather than designed in. Over time, your team spends more energy keeping MongoDB alive than building your product.

Over time, your team spends more energy keeping MongoDB alive than building your product.

This isn’t just theoretical. Infisical, a fast-growing security startup handling tens of millions of secrets per day, migrated from MongoDB to Postgres in 2024. They cited the operational headaches of MongoDB’s replica sets and version inconsistencies across environments as reasons driving their migration, problems that disappeared once they switched to Postgres. Migration didn’t just improve reliability; it cut database costs by nearly 50%.

Postgres, by contrast, gets easier the more you scale. Replication, backups, and failovers are boring, and boring is exactly what you want from your operational database. Decades of maturity mean the playbooks are known, the tools are abundant, and every cloud provider offers first-class managed Postgres.

Decades of maturity mean the playbooks are known, the tools are abundant, and every cloud provider offers first-class managed Postgres.

At scale, MongoDB creates work. Postgres removes it.

Postgres: A Foundation That Compounds

Postgres tells a different story. It started with discipline: relational schemas, ACID transactions, and a mature query planner. That discipline became the foundation for decades of community sourced evolution driven by the needs of its users.

Over time, Postgres didn’t bolt on features. Instead, they absorbed them gracefully:

JSONB for document storage without chaos.
PostGIS for geospatial workloads.
pgvector for AI and embeddings.
Hypertables, compression, and continuous aggregates with Tiger Data, creators of TimescaleDB, for time-series and real-time analytics.

The result is an architecture that compounds value. Where MongoDB’s shortcuts postpone rigor and force painful rewrites, Postgres enables steady expansion and a platform that scales with data and unpredictable industry changes.

And it’s not just the technology—it’s the community: Postgres has one of the most active, trusted, and global open-source communities in the world. Thousands of contributors have advanced it year after year, and an entire ecosystem of companies has grown around it. That kind of compounding innovation doesn’t come from a vendor roadmap. It comes from developers who care and are deeply invested in making the best tech stack possible for their companies and customers.

That’s why Mechademy chose managed Postgres with Tiger Data. The platform delivered:

Native time-series support: Hypertables eliminated manual bucketing and schema maintenance, automatically partitioning data by time and space.
Continuous aggregates: Automated rollups provided data at multiple resolutions, perfectly suited for diagnostic tests with different time horizons.
Built-in compression: Reduced storage costs dramatically while boosting query performance.
Unified SQL + Postgres familiarity: Simplified onboarding, debugging, and development with a standard, proven query language.

The results were immediate and transformative. The numbers speak for themselves:

87% reduction in infrastructure costs
50× increase in workload capacity (200,000 → 10,000,000 tests per half hour)
Near-zero maintenance overhead with hypertables and compression
Unified transactional + analytical workloads with no ETL complexity
Predictable performance scaling and vastly simplified operations

This is why Postgres has quietly become the default database of the modern era. It isn’t hype-driven. It’s foundation-driven.

Postgres vs MongoDB in Benchmarks

Benchmarks make architecture visible in numbers. And when Postgres and MongoDB are tested side by side, the story is consistent: Postgres is faster, more predictable, and more efficient at scale.

Transactions (OLTP): In head-to-head comparisons by OnGres/EnterpriseDB, Postgres outperformed MongoDB by a wide margin. Under transaction-heavy workloads, Postgres delivered 4–15× higher throughput, with median latencies under 10 ms and 99th-percentile latencies under 50 ms. MongoDB, by contrast, lagged at 5–20× slower on median latency and up to 17× slower at the tail.
Analytics and Real-Time Queries: On RTABench, an open benchmarking suite for real-time analytics, Postgres consistently delivers lower query latencies and higher throughput than MongoDB when datasets grow into the millions of rows. Complex filters and aggregations that take seconds in MongoDB return in milliseconds in Postgres, a reflection of Postgres’s cost-based planner and mature indexing strategies.
Semi-Structured Data: Where MongoDB should have a big advantage given its architecture is semi-structured data, but a recent benchmark comparing PostgreSQL 16.1 and MongoDB 7.0.3 from DocumentDatabase.org found a nuanced picture:
- Postgres had a slight edge for bulk loads, and it’s significantly faster for single inserts.
- Single inserts: Postgres was significantly faster than MongoDB.
- Storage: MongoDB was more space-efficient due to compression.
- Queries: Postgres was faster at small scales, but MongoDB held steadier performance as datasets grew beyond ~0.5M rows.
- Takeaway: Postgres has closed the gap on JSON performance, even surpassing MongoDB in inserts and smaller workloads, while MongoDB retains an edge in storage efficiency and very large JSON-only queries.

MongoDB still shines in certain scenarios: its append-only design and tunable durability means it can ingest simple JSON documents at very high throughput, often faster than Postgres when consistency and indexing aren’t required. For raw telemetry or log capture, MongoDB can look appealing. However, as soon as workloads evolve beyond inserts, when you need queries, joins, analytics, or reliable transactions, Postgres consistently outperforms MongoDB.

At Scale, the Gap Is Clear

OLTP: Postgres = 4–15× faster throughput
Analytics: ms vs seconds
Predictability: Postgres latencies stay flat; MongoDB slows with growth

Benchmarks confirm what architecture predicts: MongoDB slows as you succeed. Postgres scales with you.

The Strategic Cost of Dead-End Architectures

Here’s the real lesson: database choices aren’t just technical. They’re strategic.

Mongo lets you start fast. But at scale, it slows you down. The cost isn’t just measured in infrastructure bills. It’s measured in opportunities lost working through technical debt instead of innovating.

Postgres flips that script. The longer you use it, the more powerful it becomes. The ecosystem grows. The extensions multiply. You can scale without technical debt slowing you down.

And developers know it. In the 2025 Stack Overflow Developer Survey, Postgres was ranked the most admired database by developers worldwide, for the third year in a row. The industry has standardized on Postgres while NoSQL continues to decline in popularity.

Shown below in sequence: 2025 vs 2023 Stack Overflow Developer Survey results

The Future Belongs to Compounding Architectures

MongoDB is an architectural dead-end. It was never designed to be an operational database. It was built for a narrow moment when web apps valued quick prototyping over long-term scalability. NoSQL complexity doesn’t have a future in a merged relational/analytics environment.

Postgres tells the opposite story. It has become the default operational database precisely because it can be both flexible and disciplined, transactional and analytical, reliable and extensible. It compounds.

If you’re building for the future, don’t pick a dead end. Build on a foundation. Build on Postgres.

MongoDB was the shortcut of the last era. Postgres is the foundation of the next.

Why MongoDB Is an Architectural Dead-End