PlanetScale vs Firebase(2026)
PlanetScale is better for teams that need non-blocking schema changes. Firebase is the stronger choice if real-time sync out of the box. PlanetScale is paid (from $39/month) and Firebase is freemium (from $25/month).
Full feature breakdown, pricing details, and pros & cons below.
By Bikram NathLast updated
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PlanetScale
PlanetScale is a MySQL-compatible serverless database with branching workflows for schema changes.
Starting at $39/month
Visit PlanetScaleFirebase
Firebase is Google's app development platform with realtime database, Firestore, auth, hosting, and cloud functions.
Starting at $25/month
Visit FirebaseHow Do PlanetScale and Firebase Compare on Features?
| Feature | PlanetScale | Firebase |
|---|---|---|
| Pricing model | paid | freemium |
| Starting price | $39/month | $25/month |
| MySQL-compatible | ✓ | — |
| Database branching | ✓ | — |
| Non-blocking schema changes | ✓ | — |
| Query insights | ✓ | — |
| Replication | ✓ | — |
| Firestore (NoSQL) | — | ✓ |
| Realtime Database | — | ✓ |
| Authentication | — | ✓ |
| Cloud Functions | — | ✓ |
| Hosting | — | ✓ |
| Storage | — | ✓ |
| App Check | — | ✓ |
PlanetScale Pros and Cons vs Firebase
PlanetScale
Firebase
Deep dive: PlanetScale
When to choose PlanetScale
Choose PlanetScale if you're a MySQL shop or need non-blocking schema changes without downtime. Deployments with schema migrations run asynchronously while reads and writes continue—a genuine advantage over RDS MySQL, which blocks during ALTER TABLE. The branch and pull-request workflow for schema changes is polished, making deployments safer and faster for teams doing weekly or daily releases. Ideal for MySQL-native teams scaling to high traffic. PlanetScale is wrong if you need foreign key constraints (disabled by default for horizontal scaling), you're on a tight budget (no free tier since 2024; minimum $39/month), or you need multi-region write replicas without architecture complexity. Projects that relied on PlanetScale's free tier in 2023 now have nowhere to go—Firebase or Turso become alternatives. Also wrong if you plan to migrate off MySQL later; PlanetScale's schema changes rely on disabled foreign keys, so migrating to Postgres requires rearchitecting your data model. Teams heavily invested in normalized relational schemas with FX constraints should stay on PostgreSQL.
Real-world use case
A B2B SaaS team (~8 engineers) managing a high-traffic invoice platform (2 million monthly transactions) does weekly schema deployments on PlanetScale. One Wednesday at 2pm, they added an index to a 2TB table. Historically on RDS MySQL, this 4-6 minute lock would've cost ~$1,200 in lost transactions. On PlanetScale, the index built asynchronously in 8 minutes with zero downtime. Cost: $99/month. Real tradeoff: PlanetScale recommends disabling foreign keys for sharding, so they removed them. That decision required adding application-level validation to backfill constraints—one engineer spent a week on this. They chose PlanetScale over Neon because they were MySQL-trained; switching to Postgres would've required learning a new dialect, and losing FX constraints in Postgres wasn't necessary (they handle validation in app code anyway). The benefit: zero downtime deployments cut their deployment risk and enabled faster iteration.
Hidden gotchas
Foreign keys are disabled by default, and the docs bury this critical fact. You only discover it when your INSERT fails with 'no referenced key' and realize the constraint never existed. Re-enabling them requires migrating your entire schema and application validation logic. Their connection pooling proxy ('Connect') adds 1-2 seconds to the first query in serverless functions because routing through their proxy is slow. Switching to native MySQL connection pooling bypasses Neon's schema safety features, creating a tradeoff between performance and safety. Billing is opaque: charged per 'read units' and 'write units,' but conversion rates are unclear from the dashboard. A single slow query can spike your bill 10x overnight without warning. They grandfather old customers on better rates while charging new signups 3x higher unit pricing—this disparity is undocumented. They killed the free tier in 2024, but many tutorials still reference it, misleading new developers. Multi-region setup requires paying for multiple deployments ($39 each), effectively doubling cost—not clearly stated upfront. Connection limits are lower than expected; you'll hit them at lower concurrency than on RDS.
Pricing breakdown
PlanetScale's Scaler plan starts at $39/mo for 10 GB storage, 1 billion row reads, and 10 million row writes. The Scaler Pro plan at $99/mo doubles these limits and adds SOC 2 compliance. They removed their free Hobby plan in April 2024. Row reads beyond the plan limit cost $1 per billion, writes cost $1.50 per million. The branching feature (Git-like schema management) is included in all paid plans. For a typical SaaS with moderate write volume, expect $39-99/mo. The main cost trap: write-heavy workloads (analytics ingestion, activity feeds) can blow past the included write quota quickly.
Deep dive: Firebase
When to choose Firebase
Choose Firebase if you're building a real-time collaborative app (Figma-like, live polling, chat) and want backend complexity handled by Google. Also ideal if you're starting a mobile app and need SDK convenience (auth, push notifications, analytics). Real-time Firestore sync is genuinely hard to replicate manually—handling subscriptions, conflict resolution, and multi-client consistency requires weeks of engineering. Great for small teams without backend expertise who just want to ship fast. Firebase is wrong if you need complex transactions, want control over data location/compliance, need to optimize costs (Firebase becomes expensive at moderate scale—$1k+/month quickly), or plan to outgrow it. Vendor lock-in is real; exporting Firestore data to Postgres is manual and lossy. Also wrong if you need SQL-like joins or plan to run complex analytics queries. Firestore forces denormalization, and backfilling denormalized copies when source data changes is tedious. Teams with strict GDPR requirements or data residency needs should avoid Firebase.
Real-world use case
A team of 2 built a collaborative whiteboard app using Firebase, needing real-time sync of canvas changes across 5+ concurrent users. On traditional Postgres + WebSockets, this would've been 2-3 weeks of engineering (managing subscriptions, conflict resolution). With Firebase Firestore's real-time listeners, every pen stroke synced to all users within 200ms; conflicts resolved automatically. Cost: $0 first month (free tier), then $150/month at peak (3k concurrent draws/day). Real numbers: they hit Firebase's default concurrent connection limit (100) at only 45 simultaneous users, forcing an upgrade. Real tradeoff: when adding a 'comments' feature, Firestore's lack of joins forced them to duplicate user names in every comment doc. Later, redesigning the user profile meant manually updating 10k comments—something a single SQL UPDATE would've solved in 50ms. They chose Firebase over building WebSocket infrastructure because the time savings were critical; reaching production in 5 days vs. 4 weeks justified the eventual cost.
Hidden gotchas
Billing is per read, write, and delete operation. A single document change counts as 1 write. If 100 users listen to the same document via real-time listeners and it changes once, that's 100 reads billed instantly. At scale, this becomes insanely expensive—a busy chatroom can rack up $500+ in daily read costs from a single change. Firestore's 'eventually consistent' reads return stale data. Google downplays this in docs, but writes to one region aren't instantly visible in another, leading to subtle race conditions in production that are nearly impossible to debug. Exporting data from Firebase is manual and incomplete. There's no built-in export-to-CSV for large datasets. Nested documents (e.g., user { profile { address } }) flatten awkwardly when exported. Authentication ties you to Google's OAuth/email systems; migrating to a different provider later is a months-long project because auth is baked into client SDKs. The free tier has a 1GB storage limit enforced harshly—one day you're building freely, the next day you get a quota-exceeded error. Google doesn't warn when approaching limits. Subcollections are stored differently than top-level collections, causing unexpected billing surprises. Array operations (adding one element to a 1k-element array) require reading and writing the entire array—performance scaling is nonlinear.
Pricing breakdown
Firebase's free Spark plan includes 1 GB Firestore storage, 50K reads/day, 20K writes/day, and 10 GB hosting bandwidth. The Blaze (pay-as-you-go) plan charges $0.06 per 100K reads, $0.18 per 100K writes, and $0.18/GB storage. Realtime Database is $5/GB stored and $1/GB downloaded. The real cost shock comes from Firestore reads — a poorly optimized query that reads 100 documents per page view can cost $150+/mo at 50K daily users. Cloud Functions are billed at $0.40 per million invocations plus compute time. A typical mobile app backend costs $20-100/mo on Blaze.
Should You Use PlanetScale or Firebase?
For most teams, PlanetScale is the better default: it offers non-blocking schema changes and is paid (from $39/month). Choose Firebase instead if real-time sync out of the box matters more than removed free tier in 2024. There is no universal winner — the right pick depends on your budget, team size, and whether you value non-blocking schema changes or real-time sync out of the box more.
Choose PlanetScale if…
- •Non-blocking schema changes
- •MySQL compatibility
- •Excellent performance
Choose Firebase if…
- •Real-time sync out of the box
- •Complete backend platform
- •Excellent mobile SDKs