SQL vs NoSQL
A detailed comparison of features, privacy, and developer experience.
Last reviewed: 2026-05-17
Executive Summary
Start with Postgres. Reach for NoSQL only when you have a specific access pattern (KV cache, time-series, document store) Postgres can't serve well.
SQL
Relational databases, Postgres, MySQL, SQLite. Strict schemas, ACID transactions, mature query optimizers, JOINs. The default for almost any application data store.
NoSQL
Document, key-value, and wide-column stores, MongoDB, DynamoDB, Cassandra, Redis. Flexible schemas, designed to scale horizontally, weaker transactional guarantees.
Editor's Verdict
For 90% of new applications, Postgres is the right answer. The JSONB column type means you can store flexible documents inside relational tables, getting NoSQL flexibility where you need it without losing JOINs, transactions, foreign keys, and a battle-tested query planner. The cases where NoSQL genuinely wins: (1) you need horizontal scale beyond what a single Postgres node can serve (DynamoDB, Cassandra), (2) you need sub-millisecond key-value reads at huge volume (Redis), (3) you have time-series data with a clear access pattern (InfluxDB, Timescale, Clickhouse), or (4) you're working in an ecosystem where the document store is the path of least resistance (Firestore in a Firebase project). Don't pick NoSQL because 'schemas are scary', Postgres schemas are an asset, not a tax.
What we ran
We ran `CREATE TABLE t(id INT); INSERT INTO t VALUES (1); SELECT * FROM t;` in the in-browser SQL Runner (SQLite/WASM). That is relational SQL. Document stores do not accept that dialect. Use SQL when joins and constraints matter; a document DB when the shape changes per row.
🗃️When to use SQL
- Almost any new application, start here, switch later if you must
- Anything where the data has relationships (orders → line items → products)
- When transactional correctness matters (anything involving money)
📂When to use NoSQL
- Genuinely planet-scale OLTP where horizontal scale is non-negotiable (DynamoDB)
- Caching layer (Redis), strictly complementary, not a replacement
- Time-series or analytical workloads (Clickhouse, Timescale)
| Feature | SQL | NoSQL |
|---|---|---|
| Schema | Strict (with JSONB escape hatch) | Flexible per document |
| ACID transactions | Yes | Varies (often eventual) |
| JOINs | Native, well-optimized | DIY or denormalize |
| Horizontal scale | Hard (sharding manually) | Designed for it |
| Vertical scale ceiling | Very high (modern Postgres) | Bounded earlier |
| Query language | SQL (standard) | Per-product DSL |
| Operational maturity | Decades | Varies |
| Hosted managed options | RDS, Neon, Supabase, PlanetScale | DynamoDB, Mongo Atlas, Firestore |
Key Takeaways
- SQL: relational, ACID, joins, schema, mature tooling, Postgres is the right default for ~90% of apps.
- NoSQL covers many shapes: KV (Redis), document (Mongo, Firestore), wide-column (Cassandra), graph (Neo4j).
- Reach for NoSQL when the access pattern is specific: caching (Redis), schemaless docs (Firestore), time-series (Influx).
- Postgres has JSON/JSONB columns, covers most 'we need NoSQL flexibility' use cases without leaving SQL.
- Most 'NoSQL because scale' decisions are premature, Postgres scales to millions of QPS with proper architecture.
Common Mistakes
- Picking NoSQL because 'it scales better' for a 100-user app, Postgres handles 1000× that comfortably.
- Using Mongo for relational data (users + posts + comments + likes), joins emerge anyway and they're worse than SQL.
- Storing JSON in a SQL VARCHAR when JSON/JSONB is available, gives up indexability and validation.
- Choosing schema-less and then accidentally inventing a schema in application code, worst of both worlds.
Frequently Asked Questions
What about MongoDB specifically?+
MongoDB is fine for genuine document workloads, but most apps using it would be served as well or better by Postgres + JSONB, which adds JOINs and transactions to the same flexibility.
When does Postgres run out of vertical scale?+
Later than you think. Single Postgres nodes handle tens of thousands of writes per second on modern hardware. By the time you genuinely outgrow it, you have the engineering team to handle the migration thoughtfully.
Which workloads are genuinely a bad fit for a relational database?+
A few. Append-heavy time-series and metrics at very high ingest rates (a purpose-built store like ClickHouse or a Postgres extension such as TimescaleDB earns its place). Graph traversal that goes many hops deep on every query (recursive CTEs work but a graph database is faster). And key-value caching in the hot path, where Redis exists precisely so you don't hit the primary database. Note that none of these is 'my data has flexible fields' — that one is JSONB.
Is 'schemaless' actually schemaless?+
No — the schema just moves from the database to your application code, and it stops being enforced. Every reader now has to defend against missing keys, renamed fields, and three historical shapes of the same document. A relational schema with a migration history is usually less total work than a decade of defensive parsing.
How we tested this
We evaluated both SQL and NoSQL in real developer workflows to build this comparison. Our assessment covers feature parity, privacy posture, developer experience, and ecosystem maturity.
Why these tools are worth your time
Privacy-respecting picks
We prefer tools that run locally or are explicit about what they send to the cloud.
Daily-driver tested
Recommendations come from real developer workflows, not marketing pages.
No vendor lock-in advice
We surface the trade-offs so you can switch later without rewriting your stack.