# Amazon Aurora vs Neo4j

Canonical: https://inetgeek.com/compare/amazon-aurora-vs-neo4j/

Every value below is read from Amazon Aurora's and Neo4j's own documentation. See https://inetgeek.com/methodology/ for how.

## At a glance

| Criterion | Amazon Aurora | Neo4j |
| --- | --- | --- |
| Free tier | Yes — capped Free plan plus time-limited credits | Yes |
| Pricing model | Pay per use with no plan fee: instances plus storage plus I/O, or ACU-hours in serverless mode. AWS's own worked example prices serverless compute at $0.12 per ACU-hour on Aurora Standard and $0.156 on Aurora I/O-Optimized in US East (N. Virginia) — the difference being whether I/O is metered separately or folded into a higher compute and storage rate. Provisioned instances also offer Reserved Instance pricing, and usage may qualify for Database Savings Plans against a committed hourly spend. | Usage-based, metered per GB-hour of memory on the Professional tier. A paused database cuts cost by roughly 80%. |
| Regions | 34 AWS regions, the same list for the MySQL- and PostgreSQL-compatible editions. Counted from the two availability tables in Aurora's own docs, which publish the regions and no total. | AWS, Azure and Google Cloud, across more than 60 global cloud regions; also available through the AWS, Azure and Google Cloud marketplaces. |
| Engine | MySQL- and PostgreSQL-compatible. Aurora is AWS's own engine rather than a hosted build of either, so compatibility is the claim being made — existing code, tools and applications are stated to work, which is not the same as being the upstream database. | Neo4j, a native property graph database. |
| Point-in-time recovery | Any point inside the backup retention period, so up to 35 days. Aurora's backups are continuous and incremental against the cluster volume rather than periodic snapshots plus log replay, which is why AWS states no performance impact while they are taken. | Not included on the entry-paid (Professional) tier — point-in-time restore is a Business Critical feature, with hourly granularity and 30-day retention. |
| Read replicas | Supported — 15 per cluster, a quota AWS will not raise | Supported |
| Backup retention | 1 to 35 days. Unlike an RDS DB instance it cannot be set to 0 — Aurora backs the cluster volume up continuously and there is no way to turn that off. | Daily backups, 7-day retention on the entry-paid (Professional) tier. Business Critical adds 30-day retention with hourly point-in-time restore; Virtual Dedicated Cloud gets hourly backups with 60-day retention. |

## Where they differ

### Pricing model

- Amazon Aurora: Pay per use with no plan fee: instances plus storage plus I/O, or ACU-hours in serverless mode. AWS's own worked example prices serverless compute at $0.12 per ACU-hour on Aurora Standard and $0.156 on Aurora I/O-Optimized in US East (N. Virginia) — the difference being whether I/O is metered separately or folded into a higher compute and storage rate. Provisioned instances also offer Reserved Instance pricing, and usage may qualify for Database Savings Plans against a committed hourly spend. ([source](https://aws.amazon.com/rds/aurora/pricing/))
- Neo4j: Usage-based, metered per GB-hour of memory on the Professional tier. A paused database cuts cost by roughly 80%. ([source](https://neo4j.com/pricing/))

### Regions

- Amazon Aurora: 34 AWS regions, the same list for the MySQL- and PostgreSQL-compatible editions. Counted from the two availability tables in Aurora's own docs, which publish the regions and no total. ([source](https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/Concepts.RegionsAndAvailabilityZones.html))
- Neo4j: AWS, Azure and Google Cloud, across more than 60 global cloud regions; also available through the AWS, Azure and Google Cloud marketplaces. ([source](https://neo4j.com/pricing/))

### Engine

- Amazon Aurora: MySQL- and PostgreSQL-compatible. Aurora is AWS's own engine rather than a hosted build of either, so compatibility is the claim being made — existing code, tools and applications are stated to work, which is not the same as being the upstream database. ([source](https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/CHAP_AuroraOverview.html))
- Neo4j: Neo4j, a native property graph database. ([source](https://neo4j.com/docs/aura/auradb/platform/backup-restore/))

### Point-in-time recovery

- Amazon Aurora: Any point inside the backup retention period, so up to 35 days. Aurora's backups are continuous and incremental against the cluster volume rather than periodic snapshots plus log replay, which is why AWS states no performance impact while they are taken. ([source](https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/Aurora.Managing.Backups.html))
- Neo4j: Not included on the entry-paid (Professional) tier — point-in-time restore is a Business Critical feature, with hourly granularity and 30-day retention. ([source](https://neo4j.com/pricing/))

### Backup retention

- Amazon Aurora: 1 to 35 days. Unlike an RDS DB instance it cannot be set to 0 — Aurora backs the cluster volume up continuously and there is no way to turn that off. ([source](https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/Aurora.Managing.Backups.html))
- Neo4j: Daily backups, 7-day retention on the entry-paid (Professional) tier. Business Critical adds 30-day retention with hourly point-in-time restore; Virtual Dedicated Cloud gets hourly backups with 60-day retention. ([source](https://neo4j.com/pricing/))

## Which should you choose?

Pick Amazon Aurora if Teams on AWS who want a Postgres or MySQL cluster that can throw off a copy-on-write clone of production in minutes, keep up to fifteen readers behind it, and pause to nothing between bursts in serverless mode.

Pick Neo4j if Teams whose data is naturally relationships-first — recommendation engines, fraud detection, knowledge graphs — where the query itself is a graph traversal Cypher expresses directly and a relational join chain would not.

Consider something else: Amazon Aurora — You want the upstream database rather than a compatible one, or a branch workflow rather than a clone. AWS documents cloning as a way to build a test environment, not as something you do per pull request.

Consider something else: Neo4j — The data model is tabular and the graph shape is incidental — most CRUD apps fit a relational or document database better, and Neo4j's per-GB memory pricing is a worse fit for large flat datasets than a row-store.

## Documented by only one

- Entry paid plan: Neo4j: $65/GB/month (minimum 1GB cluster) on AuraDB Professional, or $0.09/hour ($65.70/month for 1GB) metered hourly. 14-day free trial available.
- Startup credit program: Neo4j: Neo4j for Startups: up to $16K in Aura credits. Requires Series B or earlier funding, a single product sold to many customers (not custom per-customer work), a functioning website, and a company LinkedIn profile.
- Scales to zero: Amazon Aurora: Supported — serverless capacity mode only
- Database branching: Amazon Aurora: Limited — clones, not a branch workflow
- Rewind in place: Amazon Aurora: Limited — Aurora MySQL edition only
- ISO 27001: Amazon Aurora: Covered under AWS's account-wide ISO/IEC 27001:2022 certification — not scoped per-service the way SOC, HIPAA and PCI DSS are on AWS's own site.
- HIPAA: Amazon Aurora: Listed as a HIPAA-eligible service on AWS's own reference page.
- GDPR / data residency: Amazon Aurora: AWS customers can process personal data on all AWS services in compliance with GDPR, and choose the storage type and geographic region for their data.

## Questions this comparison answers

**Should I choose Amazon Aurora or Neo4j?**

Pick Amazon Aurora if Teams on AWS who want a Postgres or MySQL cluster that can throw off a copy-on-write clone of production in minutes, keep up to fifteen readers behind it, and pause to nothing between bursts in serverless mode.
Pick Neo4j if Teams whose data is naturally relationships-first — recommendation engines, fraud detection, knowledge graphs — where the query itself is a graph traversal Cypher expresses directly and a relational join chain would not.