Deepak Gupta

Developer Tools · Database as a Service

Top 5 Database-as-a-Service Platforms 2026: Supabase vs Neon vs PlanetScale vs MongoDB Atlas vs Amazon RDS/Aurora

Managed databases for application backends compared: Supabase, Neon, PlanetScale, MongoDB Atlas, and Amazon RDS/Aurora. Real pricing, branching, scale-to-zero cold starts, and the production trade-offs each platform makes.

By Deepak Gupta·Aug 15, 2026·13 min·5 tools compared

Database as a ServicePostgresSupabaseNeonPlanetScaleMongoDB Atlas

Quick Comparison

Platform	Data Model	Free Tier	Standout Feature	Starting Paid Price
Supabase	Postgres (relational)	500 MB DB, 50K MAUs, forever	Auth, storage, and realtime bundled with Postgres	$25/month
Neon	Postgres (relational)	10 branches, 0.5 GB, forever	Git-style branching plus true scale-to-zero	$0.106/CU-hour (Launch)
PlanetScale	MySQL (Vitess) or Postgres	None	Vitess sharding proven at YouTube/Slack/GitHub scale	$5/month (Postgres, single node)
MongoDB Atlas	Document (NoSQL)	512 MB, forever (M0)	Native flexible schema plus built-in vector search	roughly $8/month (Flex)
Amazon RDS / Aurora	Relational (Postgres/MySQL engines)	12-month new-account trial only	Deepest AWS-native integration and engine choice	$0.12/ACU-hour (Aurora Serverless v2)

Supabase

Data Model: Postgres (relational)
Free Tier: 500 MB DB, 50K MAUs, forever
Standout Feature: Auth, storage, and realtime bundled with Postgres
Starting Paid Price: $25/month

Neon

Data Model: Postgres (relational)
Free Tier: 10 branches, 0.5 GB, forever
Standout Feature: Git-style branching plus true scale-to-zero
Starting Paid Price: $0.106/CU-hour (Launch)

PlanetScale

Data Model: MySQL (Vitess) or Postgres
Free Tier: None
Standout Feature: Vitess sharding proven at YouTube/Slack/GitHub scale
Starting Paid Price: $5/month (Postgres, single node)

MongoDB Atlas

Data Model: Document (NoSQL)
Free Tier: 512 MB, forever (M0)
Standout Feature: Native flexible schema plus built-in vector search
Starting Paid Price: roughly $8/month (Flex)

Amazon RDS / Aurora

Data Model: Relational (Postgres/MySQL engines)
Free Tier: 12-month new-account trial only
Standout Feature: Deepest AWS-native integration and engine choice
Starting Paid Price: $0.12/ACU-hour (Aurora Serverless v2)

1

Supabase

Best Overall

Best for: Full-stack app backends that want Postgres plus auth, storage, and realtime in one platform

“Supabase is the best default Database-as-a-Service pick for 2026 because it solves the whole backend problem, not just the database problem. It is standard Postgres underneath, not a proprietary fork, wrapped with authentication, row-level security, file storage, realtime subscriptions, and edge functions that most teams would otherwise assemble from three or four separate vendors. For a solo developer or small team shipping an application backend, that bundling is the difference between a working product in a weekend and a working product in a month.”

Pros

Free tier ships with 500 MB Postgres storage, 50,000 monthly active users, and unlimited API requests, generous enough to run a real MVP with no credit card
Built-in auth, row-level security, file storage, realtime subscriptions, and edge functions mean most backends need zero extra services
Standard Postgres underneath, not a fork, so existing extensions like pgvector and PostGIS and existing ORMs work without adapters
Pro plan's $10/month compute credit covers one Micro instance, so most early-stage SaaS apps stay at exactly $25/month
Dashboard and CLI are fast for local development, migrations, and schema browsing

Cons

Free projects pause after 7 days of inactivity, which breaks demo links and staging environments left idle over a weekend
Supavisor's session-mode deprecation on port 6543 in February 2025 broke connection strings for anyone who had hardcoded the old pooler port
Compute costs above the $10/month credit escalate quickly once an app outgrows the Micro instance bundled into Pro
Direct database hostnames are IPv6-only, so IPv4-only environments must route through the pooler hostname instead

Honest Weakness: The all-in-one bundle is Supabase's strength and its lock-in risk at the same time: auth, storage, realtime, and edge functions are proprietary layers built on top of Postgres, not independently swappable, so migrating just the auth layer later means unwinding row-level security policies wired to Supabase's auth.uid() convention. Teams that want Postgres with total infrastructure independence, and are willing to wire up their own auth and storage, get less lock-in from Neon or RDS. For teams that want to ship a full backend without hiring a platform engineer, that trade-off is the right one.

Postgres, Not a Fork

Supabase's core differentiator is that the database is unmodified Postgres, reachable with any standard Postgres driver, ORM, or extension. Teams already comfortable with SQL, migrations, and Postgres extensions like pgvector for embeddings or PostGIS for geospatial data get those capabilities for free instead of waiting for a proprietary platform to add support. That also means the exit path, if a team ever needs one, is a standard pg_dump rather than a proprietary export format.

Where the Bundle Helps and Hurts

Auth, storage, realtime, and edge functions being part of the same platform as the database removes integration work that would otherwise mean standing up Auth0 or Clerk, an S3 bucket, and a WebSocket service separately, then wiring permissions across all three. The cost is that those pieces are Supabase-specific: row-level security policies reference Supabase's auth schema directly, and migrating away from Supabase later means re-implementing that logic elsewhere, not just swapping a connection string.

Free tier: $0 (500 MB DB, 50K MAUs, 1 GB storage). Pro: $25/month + usage past included limits. Team: $599/month. Enterprise: custom.

Visit Supabase

2

Neon

Best Value

Best for: Postgres with git-style branching for per-PR and per-developer database copies

“Neon is the platform to pick when the workflow, not just the database, is the pain point. Copy-on-write branching turns database-per-pull-request from a nice idea into something teams actually run in CI, and true scale-to-zero means a fleet of preview and staging branches costs close to nothing when idle. For production traffic that cannot tolerate a cold-start spike, Neon still works, it just means turning scale-to-zero off and paying for always-on compute like any other Postgres host.”

Pros

Copy-on-write branching creates a full database branch in seconds without duplicating storage, making database-per-pull-request practical instead of theoretical
True scale-to-zero: idle computes suspend after a default 5-minute idle period and stop billing for compute, only storage keeps accruing
Pay-per-second compute at $0.106/CU-hour (Launch) or $0.222/CU-hour (Scale), so a side project idling most of the day costs pennies
Standard Postgres, wire-compatible with existing drivers, ORMs, and extensions
No monthly minimum as of December 2025, so low-usage projects are not paying a floor fee for capacity they never use

Cons

Cold start after scale-to-zero adds 500ms to a few seconds of latency on the first query; only the pooled PgBouncer connection string reliably masks it
Branches beyond the free 10-per-project allotment cost $1.50/branch-month, which adds up for teams that branch per-PR at real velocity
No built-in auth, storage, or realtime layer, unlike Supabase, so the rest of the backend has to be assembled separately
Storage bills continue even while compute is suspended, at $0.35/GB-month, so a database with a lot of branch history is not actually free while idle

Honest Weakness: Scale-to-zero is the headline feature, but it is a real trade-off, not a free lunch: any workload that cannot tolerate a cold-start spike, a user-facing API with a strict p99 latency budget, a webhook receiver with a short timeout, has to either disable scale-to-zero and lose the cost benefit, or accept occasional multi-second latency on the first request after idle. Teams running always-on production traffic get less benefit from Neon's core feature than teams running spiky dev, staging, or per-branch preview environments, where scale-to-zero is close to free money.

Branching as a CI Primitive

Neon models a database the way git models a repository: branching a multi-gigabyte production database takes seconds because it is copy-on-write, no data is physically duplicated until it diverges. That makes it practical to spin up a fresh, production-like database for every pull request in CI and tear it down after merge, something that would be prohibitively slow and expensive on a traditional snapshot-restore workflow.

Scale-to-Zero Economics

Compute is billed per second and suspends entirely after a configurable idle window, as short as 1 minute on the Scale plan or as long as 7 days. For a database with genuinely spiky traffic, a staging environment, a per-branch preview, an internal tool used a few hours a day, that pricing model can cut the compute bill to a fraction of an always-on instance. The trade is the cold-start latency on the first request after each idle period, which teams building latency-sensitive production APIs need to plan around explicitly.

Free tier: $0 (10 branches, 0.5 GB storage). Launch: $0.106/CU-hour compute + $0.35/GB-month storage. Scale: $0.222/CU-hour. No monthly minimum since December 2025. Extra branches: $1.50/branch-month past the included 10.

Visit Neon

3

PlanetScale

Runner Up

Best for: MySQL (Vitess) or Postgres workloads that need proven horizontal scaling without hand-rolling sharding

“PlanetScale is the right pick once an application's write volume or table size has genuinely outgrown what a single primary can handle. The Vitess architecture that powers its MySQL product is the same technology YouTube built to shard MySQL at Google scale, and it now backs production databases at Slack, GitHub, and Etsy. PlanetScale for Postgres reached general availability on August 11, 2026, extending the same non-blocking schema-change workflow to teams that do not want MySQL, though it is a much younger product than the decade-plus-proven Vitess side.”

Pros

Vitess-based MySQL powers YouTube-scale sharding for companies like Slack, GitHub, and Etsy, so the horizontal-scaling story is production-proven, not experimental
Non-blocking schema changes via branching and deploy requests avoid the table locks that plague schema migrations on vanilla MySQL in production
PlanetScale for Postgres went generally available on August 11, 2026, bringing the same branching and deploy-request workflow to a non-MySQL engine
Transparent starting price: $5/month for a single-node Postgres cluster, $39/month for a 3-node highly available Vitess MySQL cluster (PS-10)
Runs on both AWS and GCP with automatic failover across availability zones on HA clusters

Cons

No free tier at all; the Hobby plan was removed in April 2024 and has not returned, so there is no zero-cost way to try it beyond a trial
Foreign key constraints are not enforced across shards; once a database is sharded, referential integrity across shards has to move into application code
The Postgres product is brand new (GA in August 2026), with far less production track record than the Vitess/MySQL side
Vitess's routing and topology add operational concepts (keyspaces, vindexes) that teams have to learn even if they never manually shard

Honest Weakness: Vitess's sharding model is what makes PlanetScale credible at scale, but it comes at the cost of relational guarantees developers take for granted: cross-shard foreign keys are not enforced by the database, so a bug in an application-layer integrity check becomes an orphaned row instead of a rejected write. Teams that need strict multi-table referential integrity at real scale are fighting the architecture, not benefiting from it. Teams nowhere near sharding scale yet get less from PlanetScale than from Neon or Supabase, while still paying a $5 to $39 monthly floor with no free tier to prototype on first.

Vitess and Proven Horizontal Scale

Vitess was built at YouTube to shard MySQL horizontally and is now maintained by PlanetScale, running in production at companies like Slack, HubSpot, Etsy, and GitHub. For an application that has outgrown vertical scaling on a single MySQL primary, that is a materially more proven path than adopting a newer distributed-SQL engine, because the failure modes of sharded MySQL at high write volume have already been found and fixed by companies operating at far larger scale.

Foreign Keys and the Sharding Trade-off

PlanetScale supports foreign key constraints on unsharded databases, which covers most applications early on. The moment a database is sharded, foreign keys that would reference rows on a different shard are no longer enforced by the database, because Vitess distributes data across separate MySQL instances and cannot efficiently check a constraint across a network boundary on every write. Teams that plan to shard need to design referential integrity into the application layer from day one, not retrofit it after the first sharding migration.

Postgres: from $5/month (single node). MySQL/Vitess HA: from $39/month (PS-10, 3-node). Metal (NVMe, unlimited IOPS): from $50/month. No free tier.

Visit PlanetScale

4

MongoDB Atlas

Honorable Mention

Best for: Document-shaped data, catalogs, content, and user profiles with variable fields, where schema rigidity is the wrong default

“MongoDB Atlas earns its place on this list for a specific reason: some application data genuinely is not relational, and forcing it into rows and foreign keys costs more in migration overhead than the flexibility of a document model costs in query complexity. For a product catalog with wildly different attributes per category, or user-generated content with optional fields, Atlas's schema flexibility is a real production advantage. For a standard multi-table SaaS backend with orders, customers, and invoices, it is usually the wrong tool.”

Pros

Schema flexibility is a real advantage for genuinely document-shaped data: product catalogs with variable attributes, event logs, content with optional fields, without a migration for every new field
M0 free tier is free forever, not a trial, 512 MB, enough for real prototyping and small internal tools
Atlas Search and Vector Search run on the same cluster as operational data, so teams needing full-text or embedding search alongside application data avoid standing up a separate system
Change streams make event-driven architectures, reacting to a document change, simpler to build than polling a relational table for updates

Cons

Serverless instances were retired on January 22, 2026 and force-migrated to Free, Flex, or Dedicated tiers, an unplanned migration for anyone who had built around the old serverless pricing model
$lookup, Mongo's join equivalent, is resource-intensive across large collections; teams routinely end up denormalizing (embedding) data specifically to avoid it
Dedicated tier (M10 and above, needed for predictable production latency instead of shared capacity) starts at $57/month, well above the equivalent relational starting price on this list
There is no database-enforced foreign key at all, so referential integrity between collections is entirely the application's responsibility

Honest Weakness: The document model that makes MongoDB fast for read-heavy, single-collection lookups is the same model that makes multi-entity consistency and relational queries harder: there is no database-enforced foreign key, and $lookup joins do not scale the way SQL joins do, so teams end up embedding data and manually keeping duplicated fields in sync across documents. If the application backend is genuinely relational, orders referencing customers referencing addresses, with real multi-table transactions, a Postgres-family platform on this list is the better default. Atlas earns its place when the data is document-shaped from the start, not when a relational schema is being forced into collections because Mongo is the familiar tool.

When the Document Model Actually Helps

MongoDB's schema flexibility pays off specifically when the data itself is variably shaped: a marketplace catalog where a book, a laptop, and a t-shirt all have different attribute sets, an activity feed with heterogeneous event types, or user profiles that accumulate optional fields over a product's life. In those cases, a relational schema forces either a wide table full of nullable columns or a separate attributes table joined on every read, both worse than storing the natural document shape directly.

The Join Problem in Practice

$lookup reads and processes two collections instead of one, and gets slower as either side grows, which is why MongoDB's own documentation steers teams toward the Extended Reference Pattern: embedding key fields from a related document directly into the primary document so most reads never need a $lookup at all. That works, but it is a deliberate trade of storage and write-time synchronization complexity for read speed, a trade that a relational database with a foreign key and an index simply does not require the application to make.

M0 free tier: $0 forever (512 MB storage, shared RAM/vCPU). Flex: roughly $8/month base plus usage, capped around $30/month, includes 5 GB storage. Dedicated M10 and up: from $57/month.

Visit MongoDB Atlas

5

Amazon RDS / Aurora

Best for Enterprise

Best for: Teams already on AWS that need the most mature managed-database ecosystem with fine-grained control

“RDS and Aurora are the right choice specifically for teams that are already deep in AWS and want a database that behaves like every other AWS service they operate: IAM-integrated, VPC-scoped, backed by the deepest operational track record in managed databases (RDS since 2009, Aurora since 2014). Aurora Serverless v2 closed the scale-to-zero gap with Neon in late 2024, but the resume time is measured in seconds, not milliseconds, and the pricing model is the most complex on this list. For a team without existing AWS expertise, it is rarely the fastest path to a working backend.”

Pros

Deepest managed-database maturity in the market, RDS launched in 2009 and Aurora in 2014, meaning more failure modes have already been found and documented than any competitor here
Aurora Serverless v2 supports true scale-to-zero (added late 2024) on Aurora Postgres 13.15+/14.12+/15.7+/16.3+ and Aurora MySQL 3.08+, closing part of the gap with Neon's serverless model
Deepest AWS-native integration: IAM auth, VPC peering, CloudWatch, AWS Backup, and Database Savings Plans (up to 35% off on a 1-year commitment, launched re:Invent 2025) all work the same way as every other AWS service a team already runs
Choice of engine, RDS Postgres, RDS MySQL, Aurora Postgres, Aurora MySQL, rather than one opinionated product

Cons

Aurora's resume from zero ACUs takes about 15 seconds, an order of magnitude slower than Neon's few-hundred-millisecond cold start, which rules out true scale-to-zero for anything user-facing
Pricing is the most fragmented on this list: separate meters for compute (ACU-hour or instance-hour), storage, I/O (unless on the I/O-Optimized tier), backup, and cross-AZ data transfer, so a real bill needs a calculator, not a pricing page
No git-style branching and no generous perpetual free tier, the 12-month free tier is a new-account trial, not a permanent plan like Supabase's or Mongo's M0
Setup requires VPC, subnet group, and security-group knowledge that the other four platforms on this list abstract away entirely

Honest Weakness: RDS and Aurora's control and maturity come from exposing AWS primitives (VPCs, subnet groups, IAM roles, parameter groups) that Supabase, Neon, and PlanetScale abstract away by design. That is the right trade for a platform team that wants that level of control and already runs AWS for everything else. For a solo developer or small team without AWS experience who wants a working database in five minutes, the setup overhead and multi-meter pricing model here are real friction, not a minor inconvenience, and any of the other four platforms on this list will get them to a working backend faster.

Serverless v2 Catching Up to Scale-to-Zero

AWS added support for scaling Aurora Serverless v2 down to 0 ACUs in late 2024, letting a cluster fully pause after a period of inactivity and resume on the next connection. Resume takes approximately 15 seconds, which is fine for dev, test, and internal tools that can tolerate a pause, but is a poor fit for anything a user is waiting on in real time. Setting a minimum of 0.5 ACU instead of 0 avoids the pause entirely at a small but nonzero baseline cost, which is what most production Aurora Serverless v2 deployments actually do.

Why the AWS-Native Integration Matters, and Only There

IAM database authentication, VPC security groups, CloudWatch alarms, and AWS Backup all working identically to the rest of a team's AWS footprint is a genuine operational advantage, but only for a team that already has that footprint. For a team building on Vercel, Cloudflare, or a non-AWS stack, none of that integration depth is reachable, and RDS/Aurora becomes just another database to configure network access for, without the payoff that makes it worthwhile for an AWS-native team.

Aurora Serverless v2: $0.12/ACU-hour (Standard) or $0.156/ACU-hour (I/O-Optimized), scales to 0 ACU on supported engine versions. RDS provisioned instances (e.g. db.t4g.micro) start roughly $15-30/month. 12-month free tier applies to new AWS accounts only, not a permanent free plan.

Visit Amazon RDS / Aurora

Which One Should You Pick?

Use Case	Our Recommendation
Building a full-stack SaaS MVP solo and need auth, storage, and a database without hiring a backend team	Supabase. Bundled auth, row-level security, storage, and realtime on top of standard Postgres gets a working backend shipped in a weekend instead of a month of vendor integration.
Running a database per pull request for staging and preview environments in CI	Neon. Copy-on-write branching creates a full database branch in seconds without duplicating storage, making database-per-PR practical instead of a theoretical nice-to-have.
Scaling a MySQL-based application past the point a single primary can handle write volume	PlanetScale. Vitess-based horizontal sharding is proven at YouTube, Slack, GitHub, and Etsy scale, with non-blocking schema changes that avoid production table locks.
Storing a product catalog or content library with wildly variable, per-item fields	MongoDB Atlas. The document model avoids a migration for every new field and handles heterogeneous attribute sets better than a wide nullable-column relational table.
Team already standardized on AWS that needs a database fitting existing IAM, VPC, and compliance tooling	Amazon RDS or Aurora. Deepest AWS-native integration and the longest managed-database track record, at the cost of setup complexity the other four platforms hide.

How we evaluated

Database-as-a-Service platforms live or die on trade-offs that only show up once real traffic hits production, not on feature checklists. This comparison weighs the dimensions that decide whether a platform fits an application backend or fights it.

Each platform was assessed on the criteria that decide real outcomes, the same dimensions you see in the comparison table above:

Best fit: whether the platform's data model (relational Postgres, sharded MySQL, or document) actually matches the shape of the application's data, not just which platform has the best marketing.
Branching and workflow: whether schema changes and per-environment database copies are a first-class, low-friction operation or an afterthought bolted onto snapshot-restore.
Cold start and scale behavior: what happens at the edges, an idle database waking up, a write-heavy table outgrowing a single primary, measured in actual seconds and actual dollars, not vendor claims.
Operational lock-in: how much of the platform is standard, portable SQL versus proprietary layers (auth conventions, sharding topology, document query language) that make leaving expensive later.
Pricing model: how cost scales with storage, compute, branches, and connections, and where a generous-looking free tier turns into a real bill.

What we reviewed

This comparison draws on official documentation and publicly posted pricing for Supabase, Neon, PlanetScale, MongoDB Atlas, and Amazon RDS/Aurora, cross-checked against engineering write-ups of production incidents and migrations (connection-pooler deprecations, foreign-key sharding limits, serverless-tier retirements) rather than vendor claims alone. It reflects the market as of August 2026 and is refreshed as platforms ship and reprice.

Note

Editorial independence: this is a vendor-neutral comparison with no paid placements, sponsorships, or affiliate links. Rankings reflect fit for the stated use cases, not commercial relationships.

Frequently Asked Questions

What is a Database-as-a-Service (DBaaS) platform, and how is it different from running Postgres on a VM?

A DBaaS handles provisioning, patching, backups, replication, and often scaling automatically, so a developer interacts with a connection string instead of an operating system. Running Postgres on a self-managed VM gives full control but means the team is on the hook for failover, backup verification, and security patching. For most application backends, the operational time a DBaaS saves is worth more than the control it gives up, which is why all five platforms here exist and compete primarily on how much of that operational burden they remove.

Which of these platforms has the best free tier?

Supabase (500 MB Postgres, 50,000 MAUs) and MongoDB Atlas (512 MB, M0, free forever) both offer real, permanent free tiers usable for a genuine prototype, not just a trial. Neon's free tier (10 branches, 0.5 GB) is also permanent and useful for small projects. PlanetScale has no free tier at all since removing its Hobby plan in April 2024, and Amazon RDS/Aurora's free tier is a 12-month new-account trial, not an ongoing plan.

Is Neon's scale-to-zero actually usable for production traffic, or just for dev environments?

It depends on latency tolerance. Cold start after suspension is typically 500ms to a few seconds, masked to sub-100ms on subsequent queries once the pooled PgBouncer connection is warm. For a production API with a strict p99 latency budget, that first-request spike after any idle period is usually unacceptable, and the fix is to disable scale-to-zero and pay for always-on compute, which erodes the cost advantage. For staging, preview branches, and internal tools with tolerant traffic patterns, scale-to-zero is close to free money.

Should I pick Supabase, or plain Postgres on Neon or RDS, if I only need a database and not auth?

If auth, storage, and realtime are not needed, Supabase's bundle is dead weight, and Neon or RDS give the same underlying Postgres with less platform-specific coupling. Neon is the better fit if branching or scale-to-zero pricing matters; RDS or Aurora is the better fit if the team already runs AWS and wants IAM-integrated access control. Supabase's advantage only shows up once auth or storage enters the picture.

Does PlanetScale support foreign keys?

Yes, but only on unsharded databases. Once a database is sharded across multiple MySQL instances, foreign keys referencing rows on a different shard are not enforced by Vitess, because checking a constraint across a network boundary on every write is not efficient at scale. Teams planning to shard need to move referential integrity checks into the application layer before they hit that point, not after.

Is MongoDB Atlas a good fit for a typical SaaS application backend?

Usually not as the primary database. Most SaaS backends are genuinely relational, users belong to organizations, invoices reference customers, permissions reference roles, and MongoDB's lack of enforced foreign keys plus the cost of $lookup joins at scale means teams end up denormalizing data to compensate. Atlas is the right choice when the core data is document-shaped from the start, a catalog, a content library, an activity feed, not when it is being used as a drop-in replacement for a relational schema.

About the author

Deepak Gupta is the founder and creator of LoginRadius, a customer identity platform he built and scaled to over a billion users. He is now the founder of GrackerAI, a GEO platform for B2B SaaS and cybersecurity teams, and has spent more than 15 years building identity and security products.

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