SQL vs NoSQL 2025: Which Scales Better for Startups?

Honestly, it's not 'difficult' in 2025 like it used to be. You've got advanced features like logical replication, partitioning, and extensions like Citus for horizontal scaling. But, setting these up and managing them effectively usually requires more specialized expertise than just spinning up a sharded MongoDB cluster. It's a different kind of complexity, more about fine-tuning.

Well, yes and no. MongoDB's aggregation pipeline is super powerful and can do complex data transformations, essentially replacing many SQL join operations. But it's a different paradigm. If you're constantly needing to join many disparate collections with complex conditions, it can be less performant and harder to write than a well-indexed SQL query. It's not its strong suit, frankly.

It's a valid concern! Many NoSQL databases, including MongoDB by default, offer eventual consistency. That means if you write something, it might take a moment to propagate everywhere. For some applications, like counting likes on a social media post, it's perfectly fine. For critical data, like a bank transfer, you'd configure MongoDB for stronger consistency or stick with a database that offers ACID guarantees, like PostgreSQL, out of the box. You've gotta understand the trade-offs.

This is a tricky one, really depends on scale and expertise. PostgreSQL is open source, so the software itself is free. You're mostly paying for hosting and DBA time. MongoDB also has a community edition, but its managed cloud service, Atlas, is popular for its ease of scaling. If your team is small and wants minimal operational overhead, Atlas might seem more expensive per unit, but could save a ton on dev and ops salaries. But if you have SQL experts, they might optimize a PostgreSQL instance to be incredibly cheap per transaction. It's not a one-size-fits-all answer, honestly.

Absolutely, and frankly, it's a super common and often smart approach! It's called a polyglot persistence strategy. You'd use PostgreSQL for your core transactional data that needs strong consistency and relational integrity, and MongoDB for things like user profiles, analytics logs, or rapidly changing content. It lets you leverage the strengths of each, you know? Many successful companies operate this way; it gives you the best of both worlds.

Sharding is basically taking your big database and breaking it up into smaller, more manageable pieces called 'shards' or partitions. Each shard runs on its own server. So instead of one massive server trying to handle everything, you've got multiple servers working together. This is 'horizontal scalability.' It matters because it allows your database to handle way more data and user requests than a single server ever could, which is critical when your startup grows explosively. MongoDB's sharding is pretty much baked in; for PostgreSQL, it's more of an add-on or a complex architectural decision.

I'd argue both can work, but for different reasons. Microservices often champion the 'database per service' pattern. In that model, you might use PostgreSQL for some services that need strong transactional guarantees, and MongoDB for others that need rapid schema evolution or handle large volumes of unstructured data. So, you're not really choosing one for the whole architecture, you're choosing the right tool for each microservice's specific data needs. It's about matching the database to the service's domain, you know?