Database & Backend Platform · decision tool
Best database for an AI SaaS
AI SaaS workloads mix user data with embeddings and spiky usage. Set your scale and constraints — get a ranked verdict with the real monthly bill.
Prices verified 2026-07-15 against official pricing pages · fit weights are our estimates (see methodology) · share the URL to share your configuration
As of Jul 2026, with 20,000 monthly active users, 10 GB database size, 100 GB monthly egress, 1,000 db operations per user / month, 24 compute active hours / day, a $100 budget for this slot, Supabase ranks first for these inputs at 95% fit and a StackSays-estimated $25.25/month (Neon: 89% fit, $22.83/month). Fit weights are our estimates; prices trace to official pages. Change any input below and the verdict recomputes.
Your situation
The verdict updates as you move these.
Clear pick for your inputs
Managed Postgres bundled with auth, realtime, storage and edge functions
vs Neon: fit 95% against 89%, est $25.25 against $22.83 per month
The decision, in plain words
For a solo builder at 20,000 monthly active users and a $100.00/mo budget for the database slot, Supabase leads at 95% fit and $25.25/mo. The two rules doing the work: budget fit (~$25.25/mo is 25% of your $100/mo budget); batteries included (Bundles auth, file storage, realtime — fewer services to glue at team size 1).
The gap to Neon (89% fit) is wide at these inputs — you'd need to change your requirements, not your taste, to flip it.
Ruled out entirely: Firebase (Firestore) (no vector search — you said you need it); PlanetScale (no vector search — you said you need it) — hard requirements, not scoring.
Generated from the calculation above — every number traces to the sourced data; nothing here is written by an AI making things up.
Why Supabase scores 95%
How the $25.25/mo estimate is calculated
What could change this verdict
Cost risk
Official prices verified 2026-07-15; calculation is deterministic code over those prices.
At a glance
| Tool | Free tier | Paid entry | Verified |
|---|---|---|---|
| Supabase | 500MB database, 50K MAU, 1GB file storage, 5GB egress, 2 projects | Pro plan $25/mo | 2026-07-15 |
| Neon | 0.5GB storage + 100 compute-hours per project, 5GB egress, 60K auth MAU | Pay-as-you-go — Compute billed per CU-hour ($0.106); suspended compute costs $0; storage $0.35/GB-month | 2026-07-15 |
| Firebase (Firestore) | 1GiB Firestore, 50K reads / 20K writes / 20K deletes per day, 50K auth MAU, 10GiB egress | Pay-as-you-go — Firestore nam5 rates: $0.06/100K reads, $0.18/100K writes, $0.18/GiB storage; deletes ($0.02/100K) not modeled. Ops derived from your per-user activity input (80/20 read/write split, our estimate) | 2026-07-15 |
| Convex | 1M function calls, 0.5GB database, 1GB file storage, 1GB egress | Professional $25/mo | 2026-07-15 |
| Turso | 100 databases, 5GB storage, 500M rows read / 10M rows written per month | Developer plan $4.99/mo | 2026-07-15 |
| PlanetScale | No free tier | PS-5 HA cluster $15/mo | 2026-07-15 |
| MongoDB Atlas | 512MB storage, shared CPU/RAM, ~100 ops/sec cap | Flex tier $8/mo | 2026-07-15 |
Data sources
| Tool | Source | Verified |
|---|---|---|
| Supabase | Supabase pricing page | 2026-07-15 |
| Neon | Neon pricing page | 2026-07-15 |
| Firebase (Firestore) | Firebase pricing page | 2026-07-15 |
| Convex | Convex pricing page | 2026-07-15 |
| Turso | Turso pricing page | 2026-07-15 |
| PlanetScale | PlanetScale pricing page | 2026-07-15 |
| MongoDB Atlas | MongoDB Atlas pricing page | 2026-07-15 |
A monitor re-checks these pages for changes; when a vendor moves a price, the date updates and every verdict recomputes.
People also ask
Can I use MongoDB as a vector database?
Yes — Atlas Vector Search is built in, so if your app data already lives in MongoDB you can add embeddings without a second store. Note M0's free 512MB fills fast with vectors; realistic RAG workloads start at Flex or M10 pricing (verified 2026-07-15).
Can I use Firebase as my whole backend?
You can — auth, Firestore, storage and functions cover most app needs. The tradeoffs: limited queries vs SQL, and deep lock-in (Firestore has no drop-in replacement, so leaving is a rewrite). Complex reporting or relational data are the usual reasons teams pair it with, or swap it for, Postgres.