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No single backend wins across all apps. The right choice depends on how your data is related, whether phones or browsers must keep working offline, how access to records is enforced, and whether your team wants a managed Google service or a Postgres database it can move later.
In short: choose Supabase when your data is highly relational and you want Postgres with SQL, row-level security, and built-in auth and storage around one database. Choose MongoDB when your records are naturally nested documents and you want its document model and ecosystem, including multi-document transactions when you need them. Choose Cloud Firestore, the database inside Firebase, when mobile or web clients must read and write while offline, your data fits a document model, and a Google-managed service suits your team.
Three different kinds of backend
These three names do not describe parallel product categories. Comparing them as if they were interchangeable leads to bad conclusions.
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- Supabase is a backend platform with a full Postgres database at its center. Every project receives that database, and authentication, REST and GraphQL APIs, real-time, storage, and edge functions build on it. Supabase describes the platform as built from existing open-source tools. It is not a Firebase clone: its core is a relational database, not a document store. Supabase architecture documentation
- MongoDB is a document database. Its manual describes the database itself, so authentication, file storage, and any serverless functions are separate choices you make and run. MongoDB Manual
- Firebase is a broader Google platform, and Cloud Firestore is one database option within it. The document model, live listeners, and offline caching discussed here belong to Firestore. Do not assume every Firebase service behaves the same way. Firestore documentation
A test app to measure the options against
Abstract comparisons are hard to act on, so this article follows one hypothetical app. It is an illustration for working through trade-offs, not a system that was built or load-tested.
#1 Best Overall
The app is an equipment-inspection tool for a small maintenance company. It has these requirements:
- Entities: sites, assets (boilers, lifts, pumps), inspections, findings, photos, technicians, and client contacts.
- Reads that cross records: a supervisor needs every open finding across all sites in a region, grouped by asset type and showing the technician who raised each one. A client wants a 12-month history for one site.
- Writes: a technician completes one inspection form with nested checklist answers and a list of photo references. Once submitted, the inspection is locked.
- Access: technicians edit only inspections assigned to them, supervisors read their region, and clients read only their own site without seeing internal notes.
- Offline: technicians work in plant rooms with no signal. They must complete forms and attach photos, then sync when they return to coverage.
- Live updates: the regional dashboard should show new submissions without a page refresh.
How each backend models the test app
Supabase: normalized tables on Postgres
The natural model in Supabase is a set of linked tables: sites, assets, inspections, findings, and photos, connected by foreign keys. The regional finding report becomes a SQL query with joins across those tables, which is the kind of question Postgres is built for. Supabase’s architecture documentation puts it plainly: “Most notably, we use Postgres rather than a NoSQL store.” Supabase architecture documentation
The cost of this model is up-front schema design. Constraints such as foreign keys and not-null columns catch bad data early, but they require you to decide the shape of your data before the first release, and to write migrations as it changes.
MongoDB: nested documents in collections
MongoDB’s manual, version 9.0 at the time these pages were checked, describes documents as field-and-value structures similar to JSON objects, which can contain nested documents and arrays. Collections group documents but do not require a rigid predefined schema. For the test app, one inspection, including its checklist answers and finding entries, can be stored as a single document, so the technician’s form becomes one write. MongoDB Manual
Rank #2
Two cautions apply. First, a flexible schema is still a design decision. If findings are copied into inspection documents as well as stored elsewhere, a change to a finding must be applied everywhere it appears. Second, the regional report crosses inspections, assets, and sites. The manual describes multi-document transactions, which let an inspection and its findings change together, along with replication with automatic failover and sharding for horizontal scale. These are documented capabilities. They are not evidence that a given deployment will outperform the alternatives.
Cloud Firestore: documents, collections, and no joins
Cloud Firestore stores documents in collections and supports nested data, filters and sorts, and real-time listeners. An inspection can be one document with its findings in a nested map or a subcollection, depending on how you query them. Firestore does not perform relational joins between collections. The regional report therefore has to be served by a query on fields you planned for, usually by storing the values the report filters on (region, asset type, status) directly in each finding document. Firestore documents atomic batches and ACID transactions, so multi-document updates are possible, but the query you need determines the layout of the data. Firestore documentation
Where the three differ
The table compares each axis as it applies to the test app. The sections that follow explain the mechanisms behind the cells that need more context.
The Tool Desk
Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →| Axis | Supabase | MongoDB | Firebase (Cloud Firestore) |
|---|---|---|---|
| Data shape | Relational tables on Postgres | Documents in collections, with nested fields and arrays | Documents in collections, with nested data and subcollections |
| Cross-record reports | SQL joins and aggregates | Queries and aggregation over documents | Filters and sorts on fields planned for each query |
| Multi-record writes | Postgres transactions | Multi-document transactions | Atomic batches and ACID transactions |
| Offline use in clients | Needs a client caching strategy | Not covered here | Built in for actively used data |
| Live updates | Real-time service streams database changes | Not covered here | Real-time listeners |
| Access control | Row-Level Security policies written in SQL | Not stated in the MongoDB Manual pages cited here | Security Rules for mobile and web; IAM for server-side access |
| Deployment | Managed or self-hosted; Postgres-based portability | Own deployment options; not compared here | Google-managed service |
| Cost drivers | Not compared here | Not compared here | Operations, stored data, and network egress, with published allowances |
Access control: where the products diverge most
Client-side access is the area where these products differ most, and their mechanisms are not interchangeable. Each one requires you to write and test rules before the app is safe to ship.
Supabase: Row-Level Security
Supabase’s database overview names Row-Level Security as the way to secure a database that an app client queries directly. Policies are SQL statements per table that decide which rows a signed-in user may read, insert, update, or delete. Supabase’s documentation stresses that exposing a table to a client requires carefully designed and tested policies. Supabase database overview
An illustrative policy for the test app looks like this. It lets a technician update only their own unsubmitted inspections, and the with check clause stops them from reassigning a row to someone else:
alter table inspections enable row level security;
create policy technicians_update_own_open_inspections
on inspections for update
to authenticated
using (technician_id = auth.uid() and submitted_at is null)
with check (technician_id = auth.uid());Select, insert, and delete each need their own policy. A table with row-level security disabled, or a policy that is broader than intended, is open to any client that can reach the API.
Firebase: Security Rules and IAM
Cloud Firestore uses Security Rules for mobile and web clients and IAM for server-side access. Rules are written in their own language and evaluated against each request, so “technicians edit only their own inspections” becomes a rule that matches the signed-in user to the document. Because the rules sit apart from your queries, test them on their own. Firestore documentation
Rank #4
MongoDB: the access layer is yours to define
The MongoDB Manual pages cited here do not describe a client-facing authorization model comparable to Row-Level Security or Security Rules. If the app reaches MongoDB only through your own server, that server becomes the place where access rules are enforced. If clients connect to the database directly, check MongoDB’s current access-control documentation before you decide.
Failure modes to test
- A technician can read or change an inspection assigned to someone else, because a read or update policy or rule is missing.
- A technician can edit a submitted inspection, because the rule checks ownership but not the locked state.
- A client sees internal notes, because the rule grants access to the whole site record rather than to client-visible fields.
- A rule or policy that depends on related data fails because the data it checks is not available at evaluation time.
Offline behavior and live updates
Firestore’s documentation describes its offline behavior this way: “Cloud Firestore caches data that your app is actively using, so the app can write, read, listen to, and query data even if the device is offline.” Firestore documentation For the test app, a technician’s assigned inspections and checklists can be cached while online, then the completed form can be written locally and synchronized on reconnection.
Two limits matter. The cache covers data the app is actively using, not the whole database, so the app must load the work a technician needs while connected. And the documentation cited here covers document data only. Photo uploads need their own queueing approach, which Firestore’s documentation does not address.
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Supabase’s own comparison page, dated 2025-08-20, says offline behavior requires a client caching strategy. In practice, that means a local store, a queue of pending writes, and reconciliation on reconnect. This is more engineering work, and you own it. Supabase’s real-time service streams database changes to subscribers, which can drive the regional dashboard. Supabase database overview Firestore listeners provide the same kind of live updates. Neither source establishes which delivers faster or handles more subscribers, so measure delivery delay and subscriber load in a prototype.
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Deployment, portability, and lock-in
Supabase documents self-hosting and migration using familiar tools: pg_dump for the database and CSV for tables. Because the core is Postgres, the data layer is the most portable part of the stack, and Supabase presents this as a design goal. Moving a whole application, including auth, storage, policies, and functions, is a separate project and should not be assumed to be simple. Supabase architecture documentation
Firebase is a Google-managed service, so hosting, operations, and billing sit inside Google’s ecosystem. MongoDB has its own deployment options, which this article does not compare. Check the MongoDB Manual for the options that fit your team’s hosting policy. MongoDB Manual
Cost: Firestore’s published allowances
Firebase’s pricing page lists the following Firestore Standard no-cost allowances, as checked in 2026. They are plan allowances, not performance figures, and they do not add up to a total bill for any app.
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|---|---|
| Stored data | 1 GiB |
| Network egress | 10 GiB per month |
| Document writes | 20,000 per day |
| Document reads | 50,000 per day |
| Document deletes | 20,000 per day |
Usage beyond these amounts is billed at Google Cloud rates that depend on edition, region, and workload, and the pricing page links to Google Cloud pricing for those rates. Allowances can change after publication, so confirm them on the live page before planning around them. Firebase pricing
To estimate cost for your own app, work through these steps:
- List each user action and count the reads, writes, and deletes it triggers. For the test app, that means loading the dashboard, opening an inspection, and saving a form.
- Multiply those counts by daily active technicians and supervisors.
- Add the cost of live dashboards. Confirm on the pricing page how listener updates are metered before you estimate them.
- Estimate stored document data and monthly network egress from realistic document sizes and read volumes.
- For Supabase and MongoDB, price compute, storage, and bandwidth from each provider’s current pricing page using the same usage assumptions. This article does not compare their totals.
Decision checklist
- Do most of your important screens join three or more entity types, such as sites, assets, inspections, and findings? If so, a relational model on Postgres fits most naturally.
- Are the records mostly self-contained, so that one form or one object maps to one document? If so, the document model reduces the number of writes per action.
- Must technicians or customers read and write without a connection, without you building a sync layer? Firestore’s documented offline caching is the strongest built-in option among the three.
- Who will write the authorization rules, and how will you test a denied request for every role?
- Does the team prefer a Google-managed platform, a self-hosted Postgres database that can move, or a MongoDB deployment of its own choosing?
- Which cost driver dominates your usage: reads, writes, storage, egress, or compute?
Run a prototype before you commit
No benchmark in this article ranks the three for speed or cost, and the documentation cited here describes capabilities rather than measured performance. A short prototype on your own workload will answer the questions that matter.
Quick Recap
- Build the regional finding report and the technician form submission in each candidate.
- Create one account per role (technician, supervisor, client) and confirm that each denied request is actually denied.
- Turn off the network on a test device, submit three forms with two photos each, reconnect, and check for lost or duplicated records.
- Run the dashboard with the number of subscribers you expect at peak, and record how long each update takes to appear.
- Log reads, writes, and data transfer for a simulated day, then price that log against each provider’s current pricing page.
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
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