Supabase vs Firebase: which backend should you build on?
Both give you a database, authentication, file storage and realtime updates without building a backend from scratch, and both run serious products in production. We ship on both. The decision comes down to two things that are genuinely hard to change later: whether your data is relational, and what leaving would cost you.
Choose Supabase if your data has real relationships and you want SQL, reviewable migrations and the ability to take the database elsewhere. Choose Firebase if your app is mobile-first and needs offline-first sync, your data is document-shaped, or you are already inside Google Cloud. For a new product with structured data, Supabase is our default; for offline-heavy mobile, Firebase is still the stronger answer.
When each one is the right call
Choose Firebase
- The app is mobile-first and has to work offline, then sync
- Your data is document-shaped rather than relational
- You are already in Google Cloud and want one bill and one identity system
- You want the most battle-tested option, with the largest body of prior art
- Push, crash reporting and analytics in the same platform genuinely matter
Choose Supabase
- Your data has real relationships — users, teams, orders, permissions
- You want SQL, joins, constraints and migrations you can review in a pull request
- You want the option to self-host or move the database somewhere else later
- You would rather express access rules as SQL policies than in a rules language
- You need a cost shape you can model before you build, not after
The differences that actually matter
| Firebase | Supabase | |
|---|---|---|
| The database | Firestore — a document store, schemaless by default | PostgreSQL — relational, with schemas, joins and constraints |
| Querying | Document queries with strict index requirements, and no joins | Full SQL: joins, views, window functions, common table expressions |
| Access control | Security Rules — a purpose-built language you learn for this | Row-level security policies, written in SQL you already know |
| Realtime | Mature and central to the product; listeners are the normal path | Streams Postgres changes, plus broadcast and presence channels |
| Offline and sync | First class on mobile — local persistence and conflict handling are built in | Not built in; caching and conflict resolution are yours to design |
| Server-side logic | Cloud Functions, with a long-established ecosystem around them | Edge Functions, plus anything Postgres itself can do in the database |
| Self-hosting | Not available | Available — the stack is open source |
| How you are billed | Per document read, write and delete | Mainly on instance size and storage, like renting a server |
| Cost of leaving | A data-layer rewrite — the query model does not port | A Postgres dump any Postgres will restore; your SQL still runs |
| Maturity | Older and larger, with more third-party material to draw on | Younger and moving quickly, with a smaller body of prior art |
Neither column is a verdict. Every row is a trade-off, and which side of it you want depends on the project — which is what the sections below work through.
The pricing model matters more than the price
Both are cheap to start and both have generous free tiers, so teams compare features, pick one, and move on. What surprises people a year in is the shape of the bill rather than its size.
Firestore charges per document read, write and delete. That is often genuinely cheap, but it ties your bill to how your app queries rather than to how much data you hold — so a screen that re-reads a collection on every render, or a list that refetches on focus, quietly multiplies cost as usage grows. It is fixable, but you fix it by changing your data model and access patterns, which is precisely the work a managed backend was supposed to save you.
Supabase bills mainly on instance size and storage, which behaves like renting a server: more predictable, and it does not punish a chatty client. The trade is that the things a managed document store handles quietly become yours — indexes that actually match your queries, and connection handling under load.
Neither model is wrong. But whichever you choose, model your real read and write pattern against the pricing page before you build on it.
Relational or not is the question that actually decides this
Almost every other difference follows from one thing: Firestore stores documents, Postgres stores rows in related tables. Get this question right and the rest of the decision mostly answers itself.
Document stores are excellent when each record is largely self-contained and read as a unit — a chat message, an activity entry, a device reading, a user profile. They are awkward when the interesting questions span records. "Show me every order from customers on the team plan who have not logged in this month" is one line of SQL and an uncomfortable amount of application code against a document store, because the joins have to happen in your app instead of in the database.
The usual workaround is duplicating data across documents so each read is cheap. That works, and it is the intended pattern, but every duplicate is now something you have to keep in step on write. Teams tend to accept that trade early because the product is small, and regret it once the product has grown a reporting requirement, an admin panel and a permissions model.
So the honest test is not which technology is better. It is: are the questions you will ask of this data mostly about one record at a time, or mostly about how records relate? If it is the latter, use the relational database. If your product genuinely is a stream of self-contained documents synced to phones, Firestore is doing exactly what it was designed for and Postgres will feel like extra work.
Lock-in, and what leaving each one actually costs
Lock-in is worth being unsentimental about. It is not a moral failing of a platform, it is a cost that arrives later, and the only question is whether you would be able to pay it if you had to.
Supabase is Postgres. Your schema, your queries, your indexes and your row-level security policies are standard Postgres, so leaving means restoring a dump somewhere else and pointing the connection string at it. The auth, storage and realtime layers around it are Supabase-specific and would need replacing, but the part that holds your business — the data and the logic expressed over it — travels intact. You can also self-host the whole stack, which means the exit exists even if you never take it.
Firebase is a genuinely excellent platform that you cannot take with you. Firestore queries, Security Rules and the client SDK behaviour have no equivalent to port to, so leaving means redesigning the data layer and rewriting every query and rule against it. That may well be a price worth paying for what you get, and plenty of successful products never leave. It is only a problem when nobody costed it before committing.
Our rule of thumb: if the product is a bet you expect to run for years and the data is the asset, weight portability heavily. If you are proving an idea and the data is disposable, pick whichever gets you to an answer fastest and treat the exit cost as a problem you would be lucky to have.
Where a managed backend stops being the right answer
Both of these exist so a small team can ship without running infrastructure, and for most products they are the correct default for far longer than engineers like to admit. There are three cases where they stop fitting.
The first is heavy background work. Long-running jobs, complex scheduled pipelines, document processing, model inference and anything that needs a queue with real retry semantics all fit awkwardly into a functions-and-database platform. It can be done, but you end up assembling something that a small dedicated service would have done more simply.
The second is data gravity. Once you have meaningful analytical workloads, a warehouse, or reporting that must not compete with your production traffic, you want control over where queries run. Supabase gives you more room here because it is Postgres and can have read replicas and downstream pipelines attached; a document store usually means exporting elsewhere to answer the question at all.
The third is regulatory. Specific residency, audit or retention obligations sometimes make a self-hosted or provider-agnostic setup the only workable option, and that is a requirements conversation rather than a preference.
Short of those, adding a hand-built backend early usually buys flexibility you do not need yet, at the cost of the months you were trying to save. We would rather ship on a managed backend, learn what the product actually is, and move the specific piece that outgrows it.
Answered before you ask
Is Supabase just an open-source Firebase?
It is positioned that way and the shape is genuinely similar — database, auth, storage, realtime and serverless functions behind one SDK — but the core is different in a way that matters. Firebase is built around Firestore, a document store with its own query model and its own rules language. Supabase is built around PostgreSQL, so you get SQL, joins, constraints, transactions and row-level security instead. That single difference changes how you model data, how you express permissions, how you report on anything, and what leaving costs. Treat the similarity as a similarity of scope, not of substance: choosing between them is choosing between a document database and a relational one, and everything else is packaging.
Can I move from Firebase to Supabase later?
Yes, and it is a real project rather than a switch you flip. The data itself is the tractable part: Firestore collections can be exported and reshaped into relational tables, and that work is mechanical once you have decided what the schema should be. The expensive parts are everything written against the old model — every query in the client, every Security Rule re-expressed as a row-level security policy, every Cloud Function, and any place the app relied on Firestore behaviour like offline persistence or realtime listeners. Plan it as a data-layer rewrite with the product frozen around it rather than as a migration you can do incrementally in the background. It is very doable, we have done it, and it is much cheaper to have thought about at the start than to discover at the point you need it.
Which one is cheaper to run?
For small projects both are effectively free, and at that stage the question does not matter. As usage grows the answer depends entirely on your access pattern rather than on the platforms. Firebase bills per document read, write and delete, so an app that reads a lot relative to the data it holds — dashboards, feeds, anything that refetches on navigation — can cost far more than the raw data volume suggests. Supabase bills mainly on instance size and storage, so heavy reads are close to free but you pay for capacity whether or not you use it. The practical way to answer this for your product is to write down your three heaviest screens, estimate reads and writes per user per day, and run both pricing pages against that number. Anyone who tells you one is simply cheaper has not seen your access pattern.
Do I still need a backend developer if I use one of these?
You need someone who can think about data, which is not quite the same thing. Both platforms genuinely remove the work of provisioning servers, wiring up authentication and building a basic API, and a competent front-end developer can build a working product on either without ever touching infrastructure. What neither removes is schema design, access control and access patterns — the decisions that determine whether your product is still fast and correct at ten thousand users. Those are backend decisions regardless of who makes them. In practice, most of the failures we are called in to fix on both platforms are not infrastructure failures; they are a data model that made sense for the first screen and has been fighting the product ever since.