AI App Backend

Postgres vs Firebase vs MongoDB for a first app

How Postgres, Firebase’s Firestore and MongoDB store data, what the same small app looks like in each, how their free tiers and billing work, and which to pick for a first project.

Floot ·

Key Takeaways

  • Postgres is the safe default for most first apps. Tables, relationships and transactions fit the data most apps have: users, orders, bookings, invoices. SQL is widely documented, so AI agents write it well, and many hosted services run on it.
  • Firestore is strongest when the app is mostly live, per-user documents. It is schemaless, syncs to clients in real time and integrates with Firebase Auth. Billing counts document reads, so screens that load many documents often can cost more than expected.
  • MongoDB suits data that is naturally nested and varied. Single-document writes are atomic and embedded documents reduce the need for joins. It supports multi-document transactions, but MongoDB’s own docs say good schema design should come first.
  • All three have usable free tiers. Supabase’s free Postgres gives 500 MB; Firestore’s Spark plan gives 1 GiB with 50,000 reads and 20,000 writes a day; MongoDB Atlas M0 gives 512 MB.
  • The database matters less than the access rules around it. Whichever you choose, each user must only reach their own data. Plan that at the start.

Quick-Start Prioritization Framework

Your app looks like…Lean towardWhy
Users, bookings, orders, invoices, reportsPostgresRelationships and totals are easy in SQL
A mobile app with live per-user data and offline syncFirestoreRealtime listeners and client SDKs built in
Records with very different shapes, like product catalogs or event logsMongoDBFlexible documents, nested fields
You want an AI agent to build and change it oftenPostgresExplicit schema and SQL make changes easy to review
You need dashboards with sums and group-bysPostgresAggregation across tables is a core strength

If you ask an AI agent to “add a database”, it will pick one for you, often based on whichever tutorial it has seen most. For a first app, that choice shapes how you query data, how you enforce privacy, and how the bill grows. This guide compares the three names you will hear most: Postgres, a relational SQL database; Firestore, Firebase’s document database; and MongoDB, a document database usually run on MongoDB Atlas. It models the same small app in each, walks through pricing with real numbers, and ends with a plain recommendation.

Three ways to store the same data

The biggest difference is the shape of the data, not speed.

Postgres: tables and relationships

Postgres stores rows in tables with defined columns and types. Related tables point to each other with keys, and a query can join them. You change the structure with migrations, such as adding a column. Postgres also stores JSON columns when part of your data is loose.

Firestore: documents in collections

Firebase’s docs describe Firestore as a NoSQL, document-oriented database. Every document belongs to a collection, documents hold key-value fields that look like JSON, and a document can contain subcollections, such as rooms/roomA/messages. Firestore is schemaless, so each document can have different fields.

MongoDB: flexible JSON-like documents

MongoDB also stores documents in collections, with rich nesting and arrays. MongoDB’s documentation says an operation on a single document is atomic, and that embedding related data in one document reduces the need for multi-document transactions.

Worked example: a small booking app in each

Take an app for a yoga studio: customers book classes, each class has a teacher and a capacity of 20, and the owner wants a monthly revenue report.

In Postgres

Four tables: customers, teachers, classes (teacher_id, starts_at, capacity, price) and bookings (customer_id, class_id, status). “Is this class full?” is SELECT count(*) FROM bookings WHERE class_id = $1 AND status = 'confirmed', run in a transaction with the insert so two people can’t take the 20th spot. The revenue report is one query joining bookings to classes and grouping by month.

In Firestore

Collections for customers, teachers and classes, with bookings as a subcollection under each class. To stop overbooking you keep a bookedCount field on the class and update it with the booking in a transaction. A customer’s “my bookings” screen needs either a collection-group query across all classes or a duplicate copy of each booking under the customer. The revenue report usually means reading many booking documents or maintaining running totals as bookings happen.

In MongoDB

A classes collection where each class document embeds a small array of bookings, since 20 is a known maximum. Checking capacity and adding a booking can then be a single atomic update on one document. The revenue report uses MongoDB’s aggregation pipeline. Customer details live in their own collection and are referenced by ID.

What the example shows

All three can do it. Postgres needed the least special design. Firestore needed duplicated data or running counters. MongoDB was elegant because bookings per class are bounded; if they weren’t, you would move them to their own collection and look more like Postgres.

Queries, transactions and reports

Most first apps grow a dashboard sooner than expected. That is where the models differ most.

PostgresFirestoreMongoDB
Query languageSQLSDK query methodsMongoDB Query API and aggregation pipeline
Joins across collections or tablesNativeNot in queries; duplicate data or read separately$lookup in aggregation; embedding preferred
Multi-record transactionsYesYesYes (replica sets from 4.0, sharded clusters from 4.2)
SchemaEnforced, changed by migrationNone enforcedOptional validation
Realtime updates to clientsThrough the hosting service, e.g. Supabase RealtimeBuilt inChange streams, via your server
Per-user access rulesRow-level security or server checksSecurity RulesServer checks; Atlas features vary

What it costs: free tiers and billing models

Prices below are from each provider’s pricing page on 2026-11-11. They bill in different units, which matters more than the headline price.

Postgres (using Supabase as the example host)

Supabase Free includes a 500 MB database, two active projects and pauses after one week of inactivity. Pro starts at $25 a month with 8 GB of disk per project and $10 of compute credits; extra disk is $0.125 per GB. You pay mainly for size and compute, not per query. Many other hosts sell managed Postgres too; Neon, for example, has a free plan with 1 GB of storage per project.

Firestore

The Spark plan needs no card and includes 1 GiB stored, 50,000 document reads, 20,000 writes and 20,000 deletes per day, plus 10 GiB of egress a month. On Blaze, the same amounts are free and usage beyond them is billed at Google Cloud rates. Firestore charges for documents and index entries read to satisfy a query, with a minimum of one read per query even if nothing is returned, and a read each time a document in a live listener’s result set is added or updated.

MongoDB Atlas

The free M0 cluster gives 512 MB of storage and up to 100 operations per second. Flex clusters cost $8 to $30 a month depending on throughput, with 5 GB of storage. Dedicated clusters start at M10 for $56.94 a month.

A read-billing example

This is our arithmetic, using Firestore’s documented rules. A dashboard lists a user’s 50 most recent bookings. If 1,000 people open it once a day, that is 50 × 1,000 = 50,000 document reads, the whole Spark daily read allowance, from one screen. Paginating to 20 items cuts it to 20,000. On Postgres the same screen is 1,000 queries, which a small instance handles without a per-read charge.

Working with an AI agent

Since this site is for people building with ChatGPT, Claude, Codex or Cursor, it is worth asking which database is easiest for an agent to change safely.

Explicit schemas are easier to review

With Postgres, every structural change is a migration you can read: “add column status text not null default 'confirmed'”. With schemaless stores, an agent can start writing a new field shape without any step you would notice, and old documents keep the old shape.

Access rules must be part of the prompt

Whether you use row-level security, Firestore Security Rules or server checks, ask the agent to write them at the same time as the data model, and to prove them with two test accounts.

Ask about backups before migrations

Agents can rename or drop fields. Know your restore option: Supabase Pro keeps daily backups for 7 days, with point-in-time recovery as an add-on; check the equivalent for any host you choose.

Which one to pick

For most first web apps, choose Postgres. Choose Firestore if you are building a mobile-first app on Firebase and want realtime sync and offline support from the client SDKs. Choose MongoDB if your records are genuinely varied and nested and you or your team already know it. Note that Firebase now also offers Postgres through Firebase Data Connect, so “Firebase” and “Postgres” aren’t mutually exclusive.

Common mistakes

These cost first-time builders the most time.

Choosing a document database to avoid “designing a schema”

Your data has a schema whether the database enforces it or not. Skipping the design only moves the work to every piece of code that reads the data.

Ignoring the billing unit

Per-read billing rewards caching and pagination. Storage-and-compute billing rewards small data and efficient queries. Estimate your busiest screen in the provider’s unit before launch.

Unbounded arrays inside documents

Embedding is great for a class of 20 bookings and bad for a chat with 50,000 messages. Documents have size limits; anything that grows without a ceiling belongs in its own collection or table.

Letting the free tier pause production

Supabase pauses free projects after a week of inactivity. A small app with a quiet week can go offline. Move anything real users depend on to a paid plan.

If you’d rather not choose and wire up a database

If you’d rather not choose and connect a database yourself, Floot gives every app its own Postgres database that the agent you build with sets up through Floot’s MCP server, with a database download on every plan and restore to any moment in the last 7 days on paid plans.

Frequently Asked Questions

Is Firebase a database?

Firebase is a platform with several products. Its main databases are Cloud Firestore and the older Realtime Database, both document stores. It also offers Data Connect, which uses Cloud SQL for PostgreSQL.

Is MongoDB faster than Postgres?

It depends on the workload and design, not the brand. For a first app, both are far faster than you need. Pick by data shape and how you will query it.

Can I switch databases later?

Yes, but it is real work: you rewrite queries and migrate data. Moving between Postgres hosts is much easier than moving between Postgres and a document database, because the SQL and the dump format stay the same.

Which database do AI coding agents handle best?

Agents handle all three, but SQL and Postgres are extremely well documented, and explicit migrations make an agent’s changes easier for you to review.

What does the Firestore free tier cover?

On the Spark plan: 1 GiB stored, 50,000 reads, 20,000 writes and 20,000 deletes per day, and 10 GiB of egress per month, according to Firebase’s pricing page.

Do I need a separate database for search or analytics?

Not at first. Postgres has full-text search and handles reporting queries well at small scale. Add specialised tools only when a real limit appears.

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