Category: All posts
Nov 26, 2025
Posted by
Jose Sahad
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.
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.
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:
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.
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 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:
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:
The results were immediate and transformative. The numbers speak for themselves:
This is why Postgres has quietly become the default database of the modern era. It isn’t hype-driven. It’s foundation-driven.
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.
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
Benchmarks confirm what architecture predicts: MongoDB slows as you succeed. Postgres scales with you.
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.
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.