What is the difference between Firebase and Supabase?

Firebase is a proprietary, NoSQL-backed platform from Google optimised for real-time features and rapid setup. Supabase is an open-source alternative built on PostgreSQL that gives teams relational data, standard SQL, and a portable architecture. The right choice depends on your data model, team skills, and tolerance for vendor lock-in.

What each platform is built on

Firebase grew from a real-time data-synchronisation product acquired by Google. Its design philosophy is to reduce friction: authentication, a document database (Firestore), file storage, and serverless functions are all available with minimal configuration. Everything runs on Google's infrastructure, and the SDK handles a great deal of complexity on your behalf.

Supabase was created as an open-source response to Firebase. Rather than building a proprietary data layer, the Supabase team chose PostgreSQL as the foundation and added a REST and GraphQL API (auto-generated from your schema), authentication, storage, and edge functions around it. Because it is built on a standard database engine, the mental model is familiar to anyone who has worked with relational data.

Both platforms are forms of backend as a service, but they represent different points on the control-versus-convenience spectrum.

Database model and querying

Firebase uses a NoSQL document model (Firestore) or a JSON tree (Realtime Database). This suits applications with simple, self-contained records and heavy real-time requirements. Complex relationships across entities require data denormalisation, which can introduce duplication and make certain queries awkward.

Supabase exposes a full PostgreSQL instance. Joins, foreign keys, transactions, and row-level security policies are all first-class features. If your application has a naturally relational data model, or if you need reporting and aggregation, Supabase gives you the tools to express that directly in SQL rather than working around a document store.

Real-time and offline support

Firebase's real-time capabilities are mature and battle-tested. Firestore listeners push changes to connected clients automatically, and the SDK includes built-in offline persistence with automatic conflict resolution. For collaborative tools, chat applications, or anything requiring live synchronisation across many users, Firebase's infrastructure handles this well at scale.

Supabase offers real-time subscriptions via PostgreSQL logical replication, which has improved considerably. For most production use cases it is sufficient, though Firebase's real-time layer has a longer track record and more sophisticated offline behaviour.

Authentication and security

Both platforms handle authentication (email, social providers, magic links, phone) with client SDKs that abstract the token lifecycle. The security models differ.

Firebase uses security rules, a proprietary declarative language that governs read and write access to Firestore and Storage. It is powerful but requires learning a specific syntax and can become difficult to reason about at scale.

Supabase uses PostgreSQL row-level security (RLS) policies written in SQL. If your team already understands databases, this approach is more transparent and composable. Policies live alongside your schema and can be version-controlled and tested like any other code.

Pricing and cost predictability

Firebase's free tier is generous, and small projects may never incur a bill. As usage grows, costs can increase sharply and unpredictably, particularly around Firestore reads and writes, Cloud Functions invocations, and bandwidth. Applications with high read volumes or poorly optimised queries can generate unexpected bills.

Supabase pricing is tiered with clearer boundaries, and because the platform is open source, self-hosting is a genuine option for teams whose usage makes managed hosting uneconomical. Neither platform is universally cheaper; the right answer depends on your application's usage pattern. Modelling expected reads, writes, and data transfer before committing is worth the time.

Vendor lock-in and migration

Migrating away from Firebase is a significant undertaking. Data is stored in a proprietary format, authentication is tied to Firebase Auth, and functions depend on Firebase's runtime. If priorities change, rebuilding substantial parts of the backend is often unavoidable.

Supabase's reliance on PostgreSQL means your data is portable. Standard SQL dumps, standard JWT tokens, and an open codebase mean a migration path exists if you need to move to a different provider or bring infrastructure in-house. Firebase can still be the right choice; the trade-off is simply worth understanding before committing.

Developer experience

Firebase's SDKs, console, and documentation are polished. Error messages are generally useful, and the conventions are clear. Teams that prefer to move fast within established patterns tend to find Firebase comfortable.

Supabase appeals to engineers who want visibility into what is happening. The built-in SQL editor, auto-generated API documentation, and direct database access make it easier to inspect and debug. Teams with database experience typically reach productive velocity quickly.

Choosing between them

Choose Firebase if your application is real-time-heavy, your team prefers higher-level abstractions, you need reliable offline support, or you are already using Google Cloud Platform services.

Choose Supabase if your team is comfortable with SQL, your data model is relational, you want predictable pricing, or avoiding lock-in to a proprietary ecosystem is a priority.

A practical approach: build a small proof of concept with each. The difference in how your team moves and where friction appears will often make the decision obvious.

In an AI-native team

Coding agents can scaffold Supabase schemas, generate RLS policies, and write typed client code from a brief description, but the correctness of generated SQL and security rules requires a developer who can read and reason about the output. With Firebase, agents generate Firestore security rules and data-access patterns that can look plausible but contain subtle logic errors, making review of AI-generated code essential. Engineers working with either platform need enough depth to verify what the agent produces, not just accept it.

What we test for

Our assessment process is described in detail at how we vet. In the fundamentals session, conducted without AI tools, we check that a candidate can reason about data modelling, security rules, and trade-offs between document and relational approaches from first principles. In the AI-native session, we observe how they direct agents to generate schema or access-layer code, and whether they can catch errors in output they did not write themselves, a skill covered in testing software you did not design.

Short answers