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Database Selection Guide: PostgreSQL vs MySQL vs MongoDB for Startups

databasesarchitecturestartup
Database Selection Guide: PostgreSQL vs MySQL vs MongoDB for Startups

Choosing a database is one of the first technical decisions a startup makes, and one of the hardest to reverse. We have built products on all three of the major options — PostgreSQL, MySQL, and MongoDB — and after shipping 12+ products over five years, we have a clear default. But defaults are not dogma. The right choice depends on what you are building.

This post is a practical comparison based on our experience building products like MindHyv, Trackelio, and LancerSpace. We are not going to benchmark TPS numbers. We are going to talk about what matters when you are a startup with a small team trying to ship.

The Short Answer

We default to PostgreSQL, hosted through Supabase. It handles 90% of the projects we encounter. The remaining 10% usually involves a specific constraint — an existing MySQL codebase, a genuinely document-oriented data model, or a client mandate — that pushes us elsewhere.

If you just want to know what to pick and move on: PostgreSQL. Now let us talk about why.

Relational vs Document: The Core Trade-Off

PostgreSQL and MySQL are relational databases. Your data lives in tables with defined columns, and you model relationships through foreign keys. MongoDB is a document database. Your data lives in collections of JSON-like documents with flexible schemas.

The relational model forces you to think about your data shape upfront. This feels like friction early on, but it pays dividends as your application grows. Foreign keys, constraints, and migrations mean your data stays consistent even when your application code has bugs.

The document model lets you move fast initially. You can store whatever shape of data you want without running migrations. But “schemaless” is misleading. Your application code still expects certain fields to exist. You have just moved the schema from the database into your application, where it is harder to enforce and easier to corrupt.

For most startup products — SaaS apps, marketplaces, platforms — the data is inherently relational. Users have organizations. Organizations have projects. Projects have tasks. These are relationships, and a relational database models them naturally.

When we built LancerSpace, the data model included clients, proposals, invoices, projects, and time entries, all with clear relationships between them. A relational database was the obvious choice. Trying to model those relationships in MongoDB would have meant denormalization headaches and data consistency problems.

Data storage infrastructure with server racks and networking equipment

PostgreSQL: Our Default

PostgreSQL is the most capable open-source relational database. Here is why we reach for it:

JSONB columns for flexible data. When you do need document-style flexibility within a relational model, PostgreSQL’s JSONB type gives you the best of both worlds. You can store arbitrary JSON and query it efficiently with indexes.

-- Store flexible metadata alongside structured columns
CREATE TABLE products (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  name TEXT NOT NULL,
  price_cents INTEGER NOT NULL,
  metadata JSONB DEFAULT '{}',
  created_at TIMESTAMPTZ DEFAULT now()
);

-- Query into the JSON
SELECT name, metadata->>'color' as color
FROM products
WHERE metadata @> '{"category": "electronics"}';

-- Index for fast JSON queries
CREATE INDEX idx_products_metadata ON products USING GIN (metadata);

This pattern comes up constantly. On MindHyv, we used JSONB columns for user profile customization options, notification preferences, and integration settings — data that varies per user and changes shape as features evolve. The core tables (users, bookings, invoices, social posts) stayed strictly typed.

Row-Level Security. PostgreSQL’s RLS lets you define access control at the database level. This is a killer feature when paired with Supabase, because it means your API does not need to implement authorization logic for every query. The database itself enforces who can read and write what.

-- Only allow users to see their own organization's data
ALTER TABLE projects ENABLE ROW LEVEL SECURITY;

CREATE POLICY "Users can view their org's projects"
  ON projects FOR SELECT
  USING (
    org_id IN (
      SELECT org_id FROM org_members
      WHERE user_id = auth.uid()
    )
  );

We lean on RLS heavily in every Supabase project. It eliminates an entire class of authorization bugs. We talk more about this in our post on building real-time features with Supabase.

Advanced types and extensions. Full-text search, PostGIS for geolocation, array types, enums, generated columns, CTEs, window functions. PostgreSQL rarely forces you to reach for a secondary database or service. When we built SpotsMexico, a photography location directory, PostgreSQL’s PostGIS extension handled all the geospatial queries for finding nearby locations. No separate geo service needed.

Ecosystem and tooling. The PostgreSQL ecosystem is mature. ORMs, migration tools, monitoring, backups — everything exists and works well. Supabase gives us a managed PostgreSQL instance with auth, storage, real-time subscriptions, and edge functions built in. That is our entire backend for most projects.

MySQL: When It Makes Sense

MySQL is a solid relational database that powers a massive share of the web. We do not avoid it. We just find fewer reasons to choose it over PostgreSQL for new projects.

MySQL makes sense when:

  • You are inheriting an existing MySQL codebase. Migration is expensive and risky. If the product already runs on MySQL and works fine, keep it.
  • Your team knows MySQL deeply. Database expertise matters more than feature comparisons. A team that knows MySQL’s query optimizer, replication quirks, and backup strategies will outperform a team learning PostgreSQL from scratch.
  • You are using a framework that defaults to MySQL. Some ecosystems (Laravel, WordPress) are MySQL-first. Fighting that default costs time.

Where MySQL falls short compared to PostgreSQL for our use cases:

-- PostgreSQL: Upsert with conflict handling
INSERT INTO user_preferences (user_id, theme, language)
VALUES ('user_123', 'dark', 'en')
ON CONFLICT (user_id)
DO UPDATE SET
  theme = EXCLUDED.theme,
  language = EXCLUDED.language,
  updated_at = now();

-- MySQL equivalent (similar, but fewer conflict target options)
INSERT INTO user_preferences (user_id, theme, language)
VALUES ('user_123', 'dark', 'en')
ON DUPLICATE KEY UPDATE
  theme = VALUES(theme),
  language = VALUES(language),
  updated_at = NOW();

Both work, but PostgreSQL’s ON CONFLICT is more flexible — you can target specific constraints, use WHERE clauses in the conflict condition, and combine it with CTEs for complex upsert workflows.

MySQL’s JSON support has improved significantly, but it still trails PostgreSQL’s JSONB in terms of indexing options and query ergonomics. MySQL lacks row-level security, full-text search is less capable, and the extension ecosystem is smaller.

For a startup evaluating both with no existing codebase, PostgreSQL gives you more room to grow without bolt-on services.

Developer writing SQL database queries on a computer screen

MongoDB: The Document Database

MongoDB is not a relational database, so comparing it directly to PostgreSQL and MySQL is slightly unfair. It solves a different problem. But startups often evaluate all three side by side, so let us be direct about when MongoDB is and is not the right choice.

MongoDB makes sense when:

  • Your data is genuinely document-oriented. Content management systems where each document has a different structure. IoT data ingestion where events have varying payloads. Catalog systems where product attributes vary wildly by category.
  • You need horizontal write scaling from day one. MongoDB’s sharding is a first-class feature. If you are building a system that will ingest millions of writes per second across geographic regions, MongoDB’s architecture handles that more naturally.
  • Your team has MongoDB expertise and your product’s data model fits.

MongoDB is a poor fit when:

  • Your data has clear relationships. Joins in MongoDB are expensive and limited. If you find yourself doing $lookup aggregations frequently, you are fighting the database.
  • You need transactions across collections. MongoDB added multi-document transactions, but they come with performance costs and complexity that relational databases handle natively.
  • You want to query your data in ways you did not anticipate at write time. Relational databases excel at ad-hoc queries. MongoDB requires you to think about access patterns upfront and design your documents accordingly.

A common pattern we see in startups that chose MongoDB early:

// This starts simple
const user = {
  name: "Alex",
  email: "alex@example.com",
  subscription: {
    plan: "pro",
    startedAt: new Date(),
  },
};

// Then grows into this
const user = {
  name: "Alex",
  email: "alex@example.com",
  subscription: {
    plan: "pro",
    startedAt: new Date(),
    invoices: [
      /* embedded array that grows unbounded */
    ],
  },
  organizations: [
    {
      orgId: "org_1",
      role: "admin",
      projects: [
        /* nested arrays of nested objects */
      ],
    },
  ],
};

Embedding related data in documents works until the documents grow large, updates become partial-document patches, and you need to query across the embedded data. At that point, you are wishing you had separate tables with foreign keys.

The Supabase Factor

A significant reason we default to PostgreSQL is Supabase. Supabase gives us a managed PostgreSQL database plus:

  • Auth with email, OAuth, and magic links
  • Real-time subscriptions over WebSockets
  • Storage for files with access policies
  • Edge Functions for server-side logic
  • Auto-generated REST and GraphQL APIs from the database schema

This means PostgreSQL is not just our database. It is our entire backend platform. A startup that chooses PostgreSQL via Supabase gets authentication, real-time features, file storage, and an API layer without writing backend boilerplate.

When we built Trackelio, the feedback platform, Supabase’s combination of PostgreSQL, real-time subscriptions, and RLS meant we could build a public-facing voting board with live updates and proper access control using almost no custom backend code.

Compare that to choosing MongoDB, where you would still need to set up auth, real-time, file storage, and an API layer separately. The database itself is only one piece. The ecosystem around it matters enormously for startup velocity.

Server room with rows of technology infrastructure and blinking lights

Query Comparison

Here is the same business logic — “get all feedback items for a board with vote counts, ordered by most votes, paginated” — in each database:

PostgreSQL:

SELECT
  f.id,
  f.title,
  f.category,
  f.created_at,
  COUNT(v.id) AS vote_count
FROM feedback f
LEFT JOIN votes v ON v.feedback_id = f.id
WHERE f.board_id = 'board_abc'
GROUP BY f.id
ORDER BY vote_count DESC
LIMIT 20 OFFSET 0;

MySQL:

-- Nearly identical for this query
SELECT
  f.id,
  f.title,
  f.category,
  f.created_at,
  COUNT(v.id) AS vote_count
FROM feedback f
LEFT JOIN votes v ON v.feedback_id = f.id
WHERE f.board_id = 'board_abc'
GROUP BY f.id
ORDER BY vote_count DESC
LIMIT 20 OFFSET 0;

MongoDB:

db.feedback.aggregate([
  { $match: { boardId: "board_abc" } },
  {
    $lookup: {
      from: "votes",
      localField: "_id",
      foreignField: "feedbackId",
      as: "votes",
    },
  },
  {
    $addFields: {
      voteCount: { $size: "$votes" },
    },
  },
  { $sort: { voteCount: -1 } },
  { $skip: 0 },
  { $limit: 20 },
  {
    $project: {
      title: 1,
      category: 1,
      createdAt: 1,
      voteCount: 1,
    },
  },
]);

The relational queries are more readable and the join is a native operation. The MongoDB aggregation pipeline works, but it is more verbose and the $lookup is an expensive operation compared to a SQL join with proper indexes.

Our Decision Framework

When a client comes to us and asks what database they should use, we run through these questions:

  1. Is there an existing database? If yes, the cost of migration almost never justifies switching for a new feature. Stay with what you have.
  2. Is the data inherently relational? Users, organizations, subscriptions, orders, products — yes. Use PostgreSQL.
  3. Is the data genuinely document-oriented with no cross-document queries? Rare, but it happens. Consider MongoDB.
  4. Does the team have deep expertise in a specific database? Expertise trumps features. A team that knows MySQL inside out will ship faster on MySQL than on PostgreSQL they are learning.
  5. Does the project benefit from the Supabase ecosystem? If you need auth, real-time, and storage alongside your database, PostgreSQL via Supabase gives you the most for the least setup.

For 90% of the startups we work with, the answer is PostgreSQL through Supabase. It is not the most exciting answer. It is the most productive one.

What About Scaling?

Startups worry about scaling too early. PostgreSQL on a modest Supabase instance handles tens of thousands of concurrent users without breaking a sweat. By the time you genuinely need to think about horizontal scaling, you should have the revenue and team to evaluate your options properly.

We have never had a client outgrow PostgreSQL. We have had multiple clients waste weeks on premature optimization of database architectures designed for traffic they never received.

Pick the database that lets you ship your MVP fastest. Optimize later with real data about real bottlenecks. For us, that database is PostgreSQL.

If you are starting a new product and want help making the right technical decisions from day one, reach out at hello@threshline.com.

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