Firebase vs AWS Amplify (2026): Pricing, Features & Migration Guide

Firebase vs MongoDB is really a choice between a bundled application platform and a database you assemble a stack around. Firebase is Google's managed backend platform, built around Firestore — a serverless NoSQL document database wired directly into authentication, hosting, cloud functions, and real-time sync. MongoDB is a general-purpose document database on its own: you run it on MongoDB Atlas (multi-cloud, or self-hosted) and bring your own auth, hosting, and compute layer around it. Both store flexible, JSON-like documents, which is exactly why teams so often assume they're interchangeable — and why the wrong pick tends to surface as a billing or scaling surprise, not a day-one bug. This guide compares the real differences in data modeling, real-time sync, pricing, and scaling, and ends with a decision framework rather than a verdict. If your team is mid-evaluation — or already facing a migration in either direction — Gart Solutions runs dedicated database migration services that handle exactly this kind of switch without the downtime or data-loss risk that keeps most teams putting it off for years. What Firebase and MongoDB Actually Are Firebase gives you an application platform. MongoDB gives you a database. That distinction explains most of the confusion in this comparison: "Firebase" isn't a single product, it's a suite — Firestore (the document database), Firebase Auth, Cloud Storage, Cloud Functions, Hosting, and Firebase Cloud Messaging, all provisioned from one console and billed on one account. MongoDB, by contrast, is purpose-built as a database, full stop. You choose where it runs — MongoDB Atlas as a managed service across AWS, Azure, or Google Cloud, or a self-hosted deployment on your own infrastructure — and you wire up authentication, hosting, and serverless compute yourself, from whichever vendors you prefer. That framing matters more than it sounds, because a team comparing "Firebase vs MongoDB" in isolation is often really comparing "a bundled backend-as-a-service" against "a database I'll assemble a stack around" — a broader decision than the two document databases alone. Our Supabase vs MongoDB comparison covers the same bundled-platform-vs-standalone-database tradeoff from the relational side, if that's the pairing you're actually weighing. The two also sit at very different points in the market. Per the DB-Engines document-store ranking for April 2026, MongoDB leads the category by a wide margin with a popularity score of 385.02, while Google Cloud Firestore ranks 7th at 9.76 and Firebase Realtime Database ranks 5th at 15.97 — roughly a 25–40x gap. That doesn't make Firestore the wrong choice for a given app, but it does mean MongoDB has a deeper hiring pool, tooling ecosystem, and third-party integration base to draw on at scale. Data Modeling and Query Capabilities Both Firestore and MongoDB store schema-flexible, JSON-like documents grouped into collections, so on paper the data models look similar. The differences show up in how you query that data. MongoDB's aggregation pipeline supports rich multi-stage queries, joins across collections ($lookup), and ad-hoc filtering on any field without pre-planning — closer to what a relational developer expects, minus the enforced schema. Firestore's query engine is deliberately simpler and more restrictive: compound queries need a matching composite index defined in advance, there's no native equivalent of a SQL join, and certain filter combinations (like range filters on more than one field) aren't supported at all without restructuring the data. DimensionFirebase (Firestore)MongoDBData modelSchema-less documents grouped in collections, nested subcollectionsSchema-less documents (BSON), flexible by defaultQuery languageFirestore query API — composite indexes required for compound queries, no native joinsMongoDB Query Language (MQL), aggregation pipeline, $lookup joinsHosting modelGoogle Cloud only, fully serverlessMongoDB Atlas (AWS, Azure, GCP) or self-hosted anywhereBundled servicesAuth, Hosting, Cloud Functions, Storage, Messaging — all includedDatabase only — auth, hosting, and compute are separate integrationsScaling modelFully serverless, auto-scales reads/writes, no cluster to sizeVertical scaling by cluster tier, plus native horizontal shardingBest fitMobile/web apps wanting a complete managed platform with minimal opsProducts needing complex queries, multi-cloud flexibility, or high-volume write scaleData Modeling and Query Capabilities Real-Time Sync: Firestore Listeners vs. MongoDB Change Streams Real-time sync is one of Firebase's strongest, most differentiated features. Firestore's onSnapshot() listeners push live updates to every connected client the moment a document changes, with offline persistence and automatic conflict resolution built in — it was designed from the ground up for chat apps, collaborative tools, and live dashboards. MongoDB offers a comparable capability through Change Streams, which let an application subscribe to real-time data changes on a collection, but it's an add-on capability layered onto a general-purpose database rather than the platform's core design point — you'll typically pair it with a WebSocket or pub/sub layer you build and operate yourself. If real-time collaboration is a core product requirement rather than a nice-to-have, Firestore's listeners will get you to a working prototype faster. If real-time updates are one feature among many in a broader system with complex query and reporting needs, MongoDB's Change Streams plus your own real-time layer give you more control over exactly how that layer behaves under load. Scaling and Architecture Firestore is fully serverless — there's no cluster to size, no instance to provision, and reads and writes scale automatically with traffic. That's a genuine operational advantage for small teams, but it also means you have less control over performance characteristics under unusual load patterns; you're scaling within Google's multi-tenant infrastructure on Google's terms. MongoDB Atlas requires picking and sizing a cluster tier (or scaling it manually/via auto-scaling rules), which is more operational overhead but gives you native horizontal sharding across both reads and writes — a pattern common for high-volume event streams, IoT telemetry, or activity feeds that outgrow a single serverless database's practical limits. In practice, most consumer and B2B SaaS products never hit genuine Firestore scaling ceilings — the operations question that actually matters day to day is less "can this scale" and more "can I predict and control my bill as it scales," which is where the two platforms diverge sharply (see the pricing section below). Pricing Compared: Per-Operation vs. Per-Cluster This is where the two platforms genuinely diverge, not just cosmetically. Per Firebase's own Firestore pricing documentation, the free Spark plan includes 50,000 document reads, 20,000 writes, and 20,000 deletes per day, plus 1 GiB of storage and 10 GiB of monthly outbound transfer. Past that, the paid Blaze plan bills per operation: $0.06 per 100,000 document reads, $0.18 per 100,000 writes, and $0.02 per 100,000 deletes. MongoDB Atlas, by contrast, bills for cluster capacity rather than individual operations — MongoDB's pricing page shows a free shared M0 tier, with dedicated clusters starting around $57–$85/month all-in for an entry M10 tier (compute, storage, and backups), regardless of how many reads and writes you run against it. Plan tierFirebase (Firestore)MongoDB AtlasFree tierSpark plan — 50K reads/day, 20K writes/day, 20K deletes/day, 1 GiB storageShared M0 cluster, 512 MB storageEntry production tierBlaze (pay-as-you-go) — $0.06/100K reads, $0.18/100K writes, $0.02/100K deletes~$57–$85/mo — M10 dedicated cluster, compute + storage + backupsBilling basisPer document operation — scales directly with traffic and query patternsPer cluster hour — predictable regardless of read/write volume within capacityWhat's bundledAuth, Hosting, Cloud Functions, Storage, MessagingDatabase only; auth/hosting/compute priced or built separately That per-operation model is exactly what makes Firebase billing feel unpredictable at scale, and it isn't a hypothetical risk. In our own cautionary tale about cloud cost overruns, a team running on Firebase's free/low-tier plan racked up over 116 billion document read operations in a matter of hours — a single inefficient query pattern, left unchecked, turned a near-zero database bill into a five-figure emergency almost overnight. MongoDB's cluster-based pricing doesn't eliminate cost risk (an undersized cluster still throttles or falls over under load), but it fails in a more contained, predictable way: performance degrades before the bill spikes. A few practical levers worth knowing before you commit to either model: On Firestore, watch composite-index-heavy queries and any client-side polling loop — both multiply read counts fast, and a client re-fetching a full collection on every render is the single most common cause of runaway Firestore bills. On MongoDB Atlas, right-size the cluster tier to actual query load rather than defaulting to the entry M10 — an undersized cluster degrades before it errors, so cost problems tend to show up as latency complaints, not invoice shock. On both platforms, set billing alerts and usage quotas before launch, not after the first spike — Firebase in particular has no default cap that stops a runaway workload from billing indefinitely on Blaze. Security, Compliance, and Vendor Lock-In Firestore's access control runs through Firebase Security Rules — a declarative, document-path-based rules language that lives alongside your data and is evaluated on every client request. It's powerful for mobile/web apps talking to the database directly, but the rules themselves become a genuine attack surface: a misconfigured or overly permissive rule set is one of the most common real-world Firebase security incidents, since client apps often query the database directly rather than through a server layer. MongoDB relies on more conventional role-based access control (RBAC), field-level encryption, and network-level controls (VPC peering, IP allowlisting on Atlas) — closer to what a security or compliance team evaluating a standalone database expects to review. Vendor lock-in is the other consideration teams underweight early on. Firestore only runs on Google Cloud — there's no self-hosted or multi-cloud option, so your database is permanently coupled to one vendor's infrastructure, pricing changes, and regional availability. MongoDB Atlas runs across AWS, Azure, and Google Cloud, and MongoDB itself can be self-hosted, which matters directly for teams with data-residency requirements or EU-sovereignty obligations. For regulated teams weighing either platform against specific compliance obligations, Gart's compliance audit service can assess the choice against your actual regulatory footprint before you commit, rather than after an auditor flags it. AI and Vector Search Both platforms now support vector embeddings for AI and retrieval-augmented generation (RAG) workloads. Firestore added native K-nearest-neighbor vector search directly on top of its existing document collections, so you can store embeddings alongside the documents they describe without standing up a separate vector database. MongoDB Atlas Vector Search offers the same idea with more maturity and tuning options — approximate nearest-neighbor indexing, hybrid search combining vector and traditional filters, and better support for very large vector counts on a single cluster. For most product teams adding a RAG feature to an existing app, the deciding factor isn't the vector search implementation itself — it's whichever database you're already running everything else on. Teams already deep in the Firebase ecosystem (Auth, Functions, Firestore) get a real simplicity win from keeping vectors in the same database; teams with heavier query and reporting needs around their AI features tend to find MongoDB Atlas Vector Search's additional tuning knobs worth the extra operational surface. Migrating Between Firebase and MongoDB Because both platforms store document-shaped data, a Firebase-to-MongoDB migration is less about schema translation than migrations from a relational database usually are — the harder work is untangling the coupling between Firestore and the rest of the Firebase suite. Security Rules need to be rebuilt as application-level authorization or MongoDB RBAC policies. Any logic that assumed Firestore's automatic offline sync or real-time listeners needs an equivalent built around Change Streams. And anything wired directly to Firebase Auth, Cloud Functions, or Hosting needs its own migration plan, independent of the database move itself. The reverse direction — MongoDB to Firestore — is less common in practice, but follows the same logic: denormalizing collections to fit Firestore's document-path model and rebuilding server-side authorization as Security Rules. A handful of practices consistently separate clean Firebase-to-MongoDB migrations from painful ones: Inventory every Firebase service in use, not just Firestore. Auth providers, Cloud Functions triggers, and Security Rules all need their own migration plan — treating this as "just a database swap" is the most common way these projects blow their timeline. Rebuild Security Rules as explicit authorization logic before cutover. Document exactly what each Firestore rule permits and denies, then translate it to MongoDB RBAC or application-level checks — don't discover a permissions gap in production. Run a dual-write or change-data-capture sync window. Streaming writes to both databases during a transition period lets you validate MongoDB against real production traffic before an irreversible cutover. Re-test every composite-index-dependent query. Query patterns tuned around Firestore's index constraints often have a faster, simpler equivalent once you have MongoDB's full aggregation pipeline available — worth a deliberate review pass, not just a lift-and-shift. Decision Framework: Which One Fits Your Product Rather than a single verdict, here's how the choice tends to shake out once teams get specific about their actual requirements: Choose Firebase if you want a complete managed platform (database, auth, hosting, functions) from day one, real-time sync is a core product feature, and you're comfortable being coupled to Google Cloud long-term. Choose MongoDB if you need complex queries and joins, multi-cloud or self-hosted flexibility, more predictable pricing at scale, or you're assembling a stack from best-of-breed vendors rather than a single bundled platform. Get outside help evaluating either if you're mid-migration, inheriting a database decision from an earlier team, or need the choice validated against specific compliance or cost obligations before committing — this is the exact gap Gart's SRE and reliability engineering and database migration engagements are brought in to close. Whichever direction fits, the sequencing that avoids regret is consistent: model your actual query patterns and realistic read/write volume before committing, not after a production billing spike or a scaling wall has made the decision expensive to reverse. For the narrower relational-engine question underneath this comparison, see our MongoDB vs. PostgreSQL comparison as a companion piece on the same evaluation logic applied to a different pairing. Choosing — or migrating — between Firebase and MongoDB? Gart Solutions plans and executes database migrations between Firestore and MongoDB (in either direction), with data-model redesign, security-rule translation, zero-downtime cutover, and post-migration performance tuning built into every engagement. 10+ Years in DevOps & Cloud 50+ Enterprise clients served 4.9★ Clutch rating Database Migration Cloud Migration Services DevOps Consulting SRE & Reliability IT Audit & Compliance Talk to a Gart Engineer → You might also like MongoDB vs MySQL: Choosing the Right Database Cloud Migration Services DevOps Consulting Lovable + Supabase Integration: What Breaks in Production Digital Sovereignty of Europe: A Cloud Guide Roman Burdiuzha Co-founder & CTO, Gart Solutions · Cloud Architecture Expert Roman has 15+ years of experience in DevOps and cloud architecture, with prior leadership roles at SoftServe and lifecell Ukraine. He co-founded Gart Solutions, where he leads cloud transformation and infrastructure modernization engagements across Europe and North America. In one recent client engagement, Gart reduced infrastructure waste by 38% through consolidating idle resources and introducing usage-aware automation. Read more on Startup Weekly.

A practical look at the reasons behind the move, what happens to your data and auth, and how to plan a migration that doesn't break production. Firebase is a great way to ship a first version fast. Firestore, Auth, and Storage come pre-wired, and for a prototype or an early MVP, that speed is exactly what you need. The problems tend to show up later — once the data model has real relationships, the bill has real traffic behind it, and the team wants row-level access control instead of a parallel rules language to maintain. That's the point where a Firebase to Supabase migration usually enters the conversation. This guide walks through why teams make the move, what actually changes under the hood — data model, authentication, storage, realtime — and how complex the process really is, based on the patterns we see across migrations at Gart Solutions. TL;DR 01 / Data Model Firestore's NoSQL model struggles with relational data and complex queries; Postgres handles both natively, with real joins instead of denormalization workarounds. 02 / Pricing & Scaling Firebase billing scales with reads, writes, deletes, and bandwidth — which gets unpredictable fast. Supabase's pricing is flatter and easier to plan against. 03 / Security & APIs Supabase ships Row Level Security, instant REST and GraphQL APIs, and Realtime on top of standard Postgres — no separate rules DSL to maintain. 04 / Migration Effort Migration complexity is moderate: reshaping the data model is the hard part, while Auth has an official, well-documented export/import path. Expert Migration Support Gart Solutions plans and executes migrations of any complexity — from Firebase specifically, and from the wider Postgres and BaaS ecosystem more broadly. Why teams are leaving Firebase for Supabase Firestore wasn't built for relational data Firestore is a document database. That's fine for flat, self-contained records — a user profile, a single chat message — but most real products don't stay flat for long. Orders reference customers and inventory. Bookings reference venues, staff, and availability windows. The moment your data has real relationships, Firestore forces a choice: denormalize (copy data into multiple documents and keep it in sync by hand) or fan out writes across collections to simulate a join that a relational database gives you for free. Composite indexes are often the tell. If your Firestore console is full of them, it's usually a sign the underlying data wants to be relational and is being modeled around a document store instead of with one. Postgres — the database underneath Supabase — handles joins, foreign keys, and multi-table queries as first-class operations, because that's what it was designed for. Firebase bills are hard to predict Firebase's pricing model charges per document read, write, and delete, plus bandwidth and storage. That's manageable at low volume, but it means the cost of a feature is only really visible once it's in production and traffic finds it. It's a familiar pattern in Firebase communities: an inefficient listener, a missing index, or a feature that goes viral for the wrong reasons can turn a predictable few-hundred-dollar month into a bill an order of magnitude higher, with little warning until the invoice arrives. Supabase's pricing is built around compute and storage tiers rather than per-operation billing, which makes it substantially easier to forecast costs as a product scales — and to catch a runaway query before it becomes a runaway bill. SQL and Row Level Security "out of the box" Firebase security is enforced through a separate rules language (Firestore Security Rules) that lives outside your schema and has to be kept in sync with it by hand as the app evolves. Supabase takes a different approach: Row Level Security (RLS) policies are written in SQL, live directly on the Postgres tables they protect, and are enforced by the database itself — not by a middleware layer your team has to trust blindly. On top of that, Supabase auto-generates REST and GraphQL APIs directly from your Postgres schema, so a properly modeled, RLS-protected table is immediately queryable from the client without writing a custom backend layer for it. What actually happens during a Firebase to Supabase migration Data: reshaping Firestore documents into Postgres tables There's no 1:1 mapping between a Firestore collection and a Postgres table, and that's the part of the migration that actually takes engineering judgment. Each collection needs a decision: does it become its own table, a foreign-keyed child table, or a JSONB column for genuinely flexible, schema-less fields? Subcollections, arrays of references, and denormalized copies of data all need to be resolved into a coherent relational schema before a single row gets migrated. In practice, this means exporting Firestore data (typically via the Firebase Admin SDK or a batch export), transforming it against the new schema, and bulk-loading it into Postgres — then validating record counts and referential integrity before cutting traffic over. A concrete example makes this less abstract. A typical Firestore orders document, denormalized the way Firestore encourages, looks something like this: { "orderId": "ord_8f21", "customerName": "Maria Kovalenko", "customerEmail": "maria@example.com", "items": [ { "sku": "SKU-100", "name": "Wireless Mouse", "qty": 2, "price": 19.99 }, { "sku": "SKU-204", "name": "USB-C Hub", "qty": 1, "price": 34.50 } ], "status": "shipped", "createdAt": "2026-06-12T10:04:00Z" } Customer details are copied directly into the order because Firestore has no native join — if customerName changes, every past order document with the old name stays stale unless you write a background job to fix it. The equivalent Postgres schema removes that duplication entirely: create table customers ( id uuid primary key default gen_random_uuid(), name text not null, email text unique not null ); create table orders ( id uuid primary key default gen_random_uuid(), customer_id uuid references customers(id), status text not null, created_at timestamptz not null default now() ); create table order_items ( id uuid primary key default gen_random_uuid(), order_id uuid references orders(id) on delete cascade, sku text not null, name text not null, qty integer not null, price numeric(10,2) not null ); Three tables instead of one denormalized document, a real foreign key relationship, and no background job needed to keep customer data in sync. This is the kind of decision that has to be made collection by collection — and it's the actual engineering work in a Firebase to Supabase migration, not the data transfer itself. Translating Firestore Security Rules into Postgres RLS policies The mental shift here is worth spelling out, because it changes how access control is written, not just where. A common Firestore rule looks like this: match /orders/{orderId} { allow read: if request.auth.uid == resource.data.ownerId; } The equivalent Supabase RLS policy is SQL, attached directly to the table: alter table orders enable row level security; create policy "Users can read their own orders" on orders for select using (auth.uid() = customer_id); Functionally similar, but the enforcement point has moved from an external rules engine to the database itself — which means every client (your app, a script, an admin dashboard, a future integration) gets the same protection automatically, instead of needing to independently respect the same rules logic. Auth: preserving users and password hashes Supabase publishes an official Firebase Auth migration path, including an import script that carries over user records and — for compatible authentication methods — existing password hashes, so end users aren't forced to reset their passwords the moment you cut over. Users who signed in via Google, Apple, or another OAuth provider migrate even more cleanly, since there's no password hash to carry over at all — just the account record and provider linkage. Storage and security rules Files in Firebase Storage move over to Supabase Storage buckets in a fairly direct transfer. The part that needs actual rework is the security layer: Firebase Storage Security Rules have to be rewritten as Supabase Storage RLS policies. The upside is that once that's done, storage access control uses the exact same policy language and mental model as your database access control — one system instead of two. Realtime listeners Firestore's onSnapshot listeners and Supabase Realtime solve the same problem — pushing live updates to clients — through fundamentally different mechanisms. Firestore uses its own sync protocol; Supabase Realtime is built on Postgres's logical replication (change data capture). That difference means realtime subscription code on the client needs to be rewritten against Supabase's API, not just repointed at a new URL. Cloud Functions and backend glue code Firebase Cloud Functions fall into two rough categories, and they migrate very differently. HTTP-triggered functions — the ones called directly from your client or a webhook — map fairly cleanly onto Supabase Edge Functions, which also run on-demand, close to the database, without you managing a server. The rewrite is mostly a change of runtime conventions, not of logic. Firestore-triggered functions — the ones that fire automatically onCreate, onUpdate, or onDelete for a document — don't have a direct equivalent, because that trigger model is specific to Firestore. The Postgres way to get the same behavior is a database trigger calling a function, or a Database Webhook that fires on row changes and calls an Edge Function or external endpoint. It's the same outcome, but it has to be deliberately rebuilt rather than ported. How to validate a migration before cutover A migration that "looks done" and a migration that's actually safe to cut traffic over to are two different things. The validation step is where that gap gets closed, and skipping it is how silent data loss makes it to production. A reasonably thorough validation pass covers: Row count reconciliation — every migrated table's row count matches the source collection's document count, accounting for any intentional filtering or splitting. Spot-check integrity — pull a random sample of records from both systems and compare field-by-field, not just record counts. Aggregate counts can match while individual records are subtly wrong. Referential integrity checks — confirm every foreign key actually resolves (no orphaned order_items rows pointing at an order_id that doesn't exist). Shadow-read testing — for a window before cutover, run the same read query against both Firestore and Supabase from the application layer and log any mismatches, without serving the Supabase result to users yet. Dual-write monitoring — during a staged cutover, writes go to both systems and get compared continuously, so a discrepancy surfaces before the old system is switched off, not after. None of this is exotic engineering — it's the same discipline any production data migration needs, regardless of source or destination. It's just easy to skip under deadline pressure, which is exactly when it matters most. How long does a migration actually take Complexity, not data volume, is the real driver of migration timelines. As a rough guide, based on the pattern of projects we scope at Gart Solutions: Project size Typical scope Rough timeline Small Few collections, mostly flat documents, OAuth-heavy auth, tolerant of a maintenance window 1–2 weeks Medium Moderate relational structure, mixed password/OAuth auth, some custom Cloud Functions, staged cutover required 3–6 weeks Large Deep relational data, heavy Security Rules logic, high-traffic production system, zero-downtime requirement 6–12+ weeks These ranges assume the schema design work happens up front rather than mid-migration — which is the single biggest lever on whether a project lands in the low or high end of its bracket. How complex is a Firebase to Supabase migration, really? Component Complexity Why Data model Moderate–High No 1:1 mapping from Firestore collections to Postgres tables; requires real schema design Authentication Low–Moderate Official export/import tooling exists; password-based users need hash migration, OAuth users migrate cleanly Storage Low Files transfer directly; security rules need to be rewritten as RLS policies Realtime Moderate Different underlying mechanism (sync protocol vs. Postgres replication); client code needs rework Taken together, the honest answer is: moderate. The data migration from Firestore's NoSQL model into a relational schema is the piece that needs the most thought, while authentication is comparatively well-trodden ground thanks to Supabase's official scripts. Neither part is trivial, but neither is a research project — this is a well-understood migration path with documented tooling on both ends, not an unmapped one. Common pitfalls we see Treating it as a lift-and-shift. A Firebase to Supabase migration is a data modeling project first and a data moving project second. Skipping the schema design step to move faster usually means redoing it later, under worse conditions. Underestimating how much logic lives in Security Rules. Firestore rules often quietly encode business logic, not just access control. That logic needs to be found and accounted for, not just translated line by line. No staged cutover plan. Migrating a live, actively-used database without a dual-write window or a clear rollback point is how "weekend migration" turns into "week-long incident." Ignoring what your composite indexes are telling you. If Firestore needed a composite index to make a query work, that's usually a preview of exactly where your new Postgres schema needs a proper join. A quick pre-migration checklist Before scoping a Firebase to Supabase migration, it's worth answering these questions honestly — they tend to predict how smooth the project will be far better than data volume does: How many collections have deep relational structure (references to other collections, denormalized copies of data) versus how many are genuinely flat, self-contained documents? How much business logic is embedded in Firestore Security Rules, beyond simple "is this user the owner" checks — conditional writes, rate limiting, cross-document validation? What percentage of your users authenticate via email/password versus OAuth providers? This directly affects how much of your Auth migration is a clean import versus a hash-compatibility exercise. Can the application tolerate a staged cutover, or does it need a hard switch at a specific moment? This shapes whether a dual-write window is realistic or whether you're planning around a maintenance window instead. Are there Cloud Functions or other Firebase-specific glue code that will need to be reimplemented as Supabase Edge Functions or moved elsewhere entirely? None of these questions have a "wrong" answer — they just change the shape of the migration plan. A team with mostly flat collections, mostly OAuth users, and tolerance for a staged cutover is looking at a materially simpler project than one with deep relational data, password-heavy auth, and a hard cutover requirement. Scoping this honestly up front is what keeps a migration on schedule. Planning a production migration, not a side project? Gart Solutions runs zero-downtime cloud migrations for teams moving off Firebase and onto Supabase — including schema design, Auth cutover, and RLS setup. See how our migration practice works How Gart Solutions approaches these migrations A Firebase to Supabase migration touches data modeling, authentication, security policy, and often the client application itself — which is exactly why we treat it as an engineering project with a plan, not a weekend script. Our process typically covers: Schema design — turning Firestore collections into a properly normalized (or deliberately denormalized, where it makes sense) Postgres schema Auth migration — carrying over users and password hashes where possible, with a clear path for OAuth-based accounts Row Level Security — building the RLS policy set that replaces Firestore Security Rules, table by table Zero-downtime cutover — staged migration and dual-write windows for production systems that can't afford an outage Just as important: we handle migrations of any level of complexity, not only from Firebase. The same team that plans a Firestore-to-Postgres schema redesign also runs migrations from Amazon RDS, Neon, Heroku, Render, and legacy MySQL or MSSQL databases onto Supabase — and, where it's the right call for a client, migrations away from Supabase too. If your source system isn't Firebase, the underlying question is usually the same one: what's the safest path from where your data is now to where it needs to be, and how do you get there without breaking production. Conclusion Firebase gets you moving fast, and for plenty of products, it stays the right choice for a long time. But once your data has real relationships, your bill has real traffic behind it, and your team wants access control that lives in the database rather than a parallel rules layer, a Firebase to Supabase migration starts to look less like an experiment and more like a fix. The migration itself is moderate in complexity, well-documented on the tooling side, and — with the right schema design up front — doesn't have to touch production uptime at all. Planning a Supabase or Postgres migration? Gart Solutions' database migration engineers plan and execute Postgres migrations — into Supabase, within a live schema, or off a managed platform entirely — with a rollback plan built in from day one, not bolted on after something breaks. 40% cost reduction on a comparable Postgres migration 25% query performance improvement Minimal downtime during cutover Database Migration Cloud Migration SRE & Monitoring Compliance Audit Talk to a migration engineer  →