Akshith Varma Chittiveli

• 5 min read

Best Database for Side Projects

Most side projects don’t fail because of bad ideas. They stall because the stack becomes harder to maintain than the idea itself.

Best Database for Side Projects

The real problem: you don’t need the “best” database—you need the least painful one

Most side projects don’t fail because of bad ideas. They stall because the stack becomes harder to maintain than the idea itself.

You start with ambition:

“Let’s use microservices”
“Let’s pick a scalable distributed DB”
“Let’s future-proof everything”

Two weeks later:

You’re debugging connection pools instead of shipping features
Your schema is over-engineered
You’ve built infra for traffic that doesn’t exist

For side projects, the real goal is simple:

Ship fast. Stay flexible. Avoid operational drag.

Why database selection is harder than it looks

Even for small projects, database choice is not trivial.

Modern databases are optimized across multiple competing dimensions:

Consistency vs availability
Flexibility vs query power
Performance vs simplicity
Scaling vs operational overhead

These trade-offs aren’t theoretical—they show up immediately in developer experience.

And importantly:

A database optimized for “internet scale” is often the wrong choice for a side project.

Because side projects are not scaling problems. They’re iteration speed problems.

The core idea: database selection is a trade-off problem

Every database sits somewhere across a set of dimensions:

Consistency guarantees
Schema flexibility
Query complexity
Operational overhead
Scaling model

Modern systems thinking frames this as a “database genome”—a set of traits that determine how the system behaves under load and change

For side projects, you don’t need to optimize all dimensions.

You need to prioritize just a few:

Low setup cost
Minimal operational burden
Enough flexibility for evolving requirements

Everything else is secondary.

Key concepts that matter for side projects

1. Workload (what are you building?)

Most side projects fall into one of these:

CRUD apps (dashboards, tools, SaaS MVPs)
Content-driven apps (blogs, CMS, portfolios)
API backends
Small real-time apps (chat, notifications)

These are not high-scale, high-throughput workloads.

2. Constraints (what do you not have?)

Side projects typically lack:

Time
DevOps bandwidth
Monitoring/alerting setup
Dedicated DBA effort

This matters more than performance.

3. Trade-offs that actually matter

For side projects, prioritize:

Simplicity > Scalability
Iteration speed > Perfect schema design
Developer experience > theoretical performance

A practical decision framework

Step 1: Default to boring

Start with a relational database unless you have a strong reason not to.

Why:

Mature ecosystem
Strong querying capability
Predictable behavior
Easy debugging

Default choice: PostgreSQL

Step 2: Evaluate schema flexibility needs

Ask:

Are my data shapes stable?
Or will they evolve frequently?

If stable → SQL works great If evolving → consider JSON support or NoSQL

Note: PostgreSQL already supports JSON, which often eliminates the need for MongoDB.

Step 3: Think about operational friction

This is where most developers make mistakes.

Ask:

Do I want to manage servers?
Do I want backups, replication, scaling headaches?

If not:

Use managed DBs (Supabase, Neon, PlanetScale)
Or embedded/local-first DBs for very small apps

Step 4: Optimize for iteration, not scale

Avoid premature decisions like:

Sharding strategies
Multi-region replication
Eventual consistency systems

Side projects rarely need:

Extreme throughput
Distributed consensus
Complex scaling patterns

Those are solutions to problems you don’t have.

How different workloads change the choice

1. Simple CRUD / SaaS side project

Best choice: PostgreSQL

Why:

Strong consistency
Rich queries
Handles 90% of use cases

2. Rapid prototyping / flexible schema

Options:

PostgreSQL (with JSONB)
MongoDB

Trade-off:

MongoDB → faster iteration early
PostgreSQL → better long-term structure

3. Real-time features (chat, notifications)

Add-on approach:

PostgreSQL + Redis

Why:

Keep primary data in SQL
Use Redis for ephemeral, fast-changing state

4. Tiny projects / solo dev tools

Options:

SQLite
Embedded DBs

Why:

Zero setup
No infra overhead
Perfect for local-first or low-traffic apps

Common mistakes engineers make

1. Over-optimizing for scale

You don’t need:

Cassandra
DynamoDB
Distributed SQL

Unless you already have scale problems.

2. Choosing NoSQL for “flexibility”

Flexibility comes at a cost:

Poor query ergonomics
Harder data integrity
Migration pain later

Most flexibility needs are solved by:

Good schema design
JSON fields in SQL

3. Ignoring operational cost

The biggest hidden cost is not compute—it’s your time.

Running a complex DB setup:

Slows development
Adds debugging overhead
Creates failure points

4. Mixing too many databases early

Don’t start with:

Postgres + Redis + Kafka + ElasticSearch

Start with one.

Add complexity only when forced.

Practical takeaway: a simple mental model

When choosing a database for a side project:

Pick the database that lets you ship fastest with the least ongoing maintenance.

In most cases, that means:

Start with PostgreSQL
Add Redis only if needed
Use managed services whenever possible

Only deviate when:

Your workload clearly demands it
Or the default becomes a bottleneck

One last thing

If you’re unsure, don’t overthink it.

Use a structured approach to evaluate:

Workload
Constraints
Trade-offs

Tools like [https://whatdbshouldiuse.com] can help guide that decision—but the key is understanding why a database fits your use case, not just picking one from a list.

Because in side projects, the best database isn’t the most powerful one.

It’s the one you don’t have to think about.