Databases · head to head
Amazon Aurora vs DuckDB

Amazon Aurora
Databases
MySQL and PostgreSQL-compatible relational database built for the cloud
- From
- Free
- Rated
- -

DuckDB
Databases
MIT-licensed analytical SQL database that runs inside your process, with no server, no dependencies and one writer at a time.
- From
- Free
- Rated
- -
The short version
- Each has a real cost: Amazon Aurora aurora requires AWS ecosystem knowledge and integration with other AWS services; DuckDB a database file accepts one read-write process at a time; other processes must open it read-only and will not see subsequent writes, so DuckDB cannot be the shared database behind several services.
- They diverge on capability: Amazon Aurora covers MySQL/PostgreSQL Compatible, DuckDB covers In-process execution.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which Amazon Aurora and DuckDB actually diverge.
| Attribute | Amazon Aurora | DuckDB |
|---|---|---|
| Pricing model | usage-based | open-source |
| Platforms | AWS Cloud | Linux, macOS, Windows, WebAssembly |
| Founded | 2006 | 2019 |
Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Databases).
What each one covers
Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.
Only in Amazon Aurora
- MySQL/PostgreSQL Compatible
- 5x MySQL Performance
- Auto-scaling Storage
- Global Database
- Serverless v2
- Multi-master
- Fault Tolerant
- AWS Lambda
Only in DuckDB
- In-process execution
- Vectorised columnar engine
- Direct file querying
- Zero dependencies
- Larger-than-memory queries
- MIT licence
- Postgres-flavoured SQL
- Extension ecosystem
What people use each for
The jobs each tool is most often brought in to do.
Amazon Aurora
- Transaction processingnot DuckDB
- Data storagenot DuckDB
- Application backendnot DuckDB
- Reportingnot DuckDB
- Data analyticsnot DuckDB
DuckDB
- Transformation steps in a data pipeline that would otherwise need Spark, replaced by SQL over Parquet in a single processnot Amazon Aurora
- Analytical queries embedded in an application or a dashboard where shipping a database server alongside it is not acceptablenot Amazon Aurora
- Local exploration of files that are too large for a pandas dataframe but far too small to justify a warehousenot Amazon Aurora
- Continuous integration and testing of analytical SQL, where a real engine can run in the test process without provisioning anythingnot Amazon Aurora
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
Amazon Aurora
- Aurora requires AWS ecosystem knowledge and integration with other AWS services
- Pricing can become expensive with high-traffic applications using many read replicas
- Limited support for non-relational data types compared to NoSQL alternatives
DuckDB
- A database file accepts one read-write process at a time; other processes must open it read-only and will not see subsequent writes, so DuckDB cannot be the shared database behind several services.
- There is no network protocol, authentication or user management, so exposing it to remote clients means writing and securing your own service around it.
- It is built for scans and aggregations, not for many small transactions, so a workload of high-frequency single-row inserts and updates performs badly compared with SQLite or Postgres.
- Storage files are backwards compatible but not forwards compatible, so a file written by a newer version cannot be read by an older one and every consumer of a shared file must be upgraded together.
- Query memory settings matter: some operations still need to hold significant state, so an under-configured memory limit turns a large join or a high-cardinality aggregation into a spill-heavy query or an out-of-memory failure rather than a slow success.
Pricing, plan by plan
Amazon Aurora
Free- Serverless v2$0.12/hour
- Auto-scaling
- Pay per ACU
- Instant scaling
- Provisioned$29/month
- Dedicated instances
- Predictable performance
- Reserved capacity
DuckDB
FreeNo published plan breakdown. See the DuckDB review.
Which should you pick?
Choose Amazon Aurora if
- You need mysql/postgresql compatible.
- You want to start without paying.
- You work on AWS Cloud.
- You also want 5x mysql performance.
Choose DuckDB if
- You need in-process execution.
- You want to start without paying.
- You work on Linux, macOS, Windows, WebAssembly.
- You also want vectorised columnar engine.
Questions people ask
- Is Amazon Aurora or DuckDB better?
- Neither clearly leads. Amazon Aurora starts at Free and DuckDB at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Amazon Aurora or DuckDB?
- Amazon Aurora starts at Free and DuckDB at Free.
- Does Amazon Aurora or DuckDB run on more platforms?
- Amazon Aurora runs on AWS Cloud. DuckDB runs on Linux, macOS, Windows, WebAssembly.
- Can I use Amazon Aurora for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Amazon Aurora best used for?
- Amazon Aurora is most often used for transaction processing, data storage, application backend, reporting. Of those, transaction processing and data storage are not what DuckDB is typically brought in for.
- What can Amazon Aurora do that DuckDB cannot?
- Amazon Aurora covers MySQL/PostgreSQL Compatible, 5x MySQL Performance, Auto-scaling Storage, Global Database. DuckDB covers In-process execution, Vectorised columnar engine, Direct file querying, Zero dependencies.
Answered from the vendors’ own pages
Amazon Aurora: Is Amazon Aurora compatible with MySQL and PostgreSQL?
Yes, Amazon Aurora offers MySQL and PostgreSQL compatibility with full compatibility to their open-source counterparts, allowing you to migrate existing databases with standard tools.
SourceDuckDB: Can multiple applications share one DuckDB database?
Not for writing. One process holds the database read-write; others may attach read-only and will not see later writes. Shared multi-writer access needs a different database or a table format with a catalogue.
Amazon Aurora: What uptime SLA does Amazon Aurora provide?
Aurora is designed for up to 99.99% single-region uptime and 99.999% multi-region uptime with automatic failover.
SourceDuckDB: Is it a replacement for a data warehouse?
For single-node analytical workloads up to a few hundred gigabytes it very often is. It is not a replacement when many concurrent users need a shared, governed, always-on service.
Amazon Aurora: How much does Amazon Aurora cost?
Aurora uses serverless, usage-based pricing where you pay only for consumed capacity. Typical pricing ranges from $50-70 per month for minimal setups to $400-600 per month for small production clusters.
SourceDuckDB: Do I have to load data into it?
No. It queries Parquet, CSV, JSON and Arrow in place, including on object storage. Its own storage format is optional and mainly useful when you want indexes, constraints and faster repeated access.
Amazon Aurora: Can Amazon Aurora scale automatically?
Yes, Aurora automatically scales to match workload demands without performance degradation, supporting both read and write scaling.
SourceDuckDB: What is MotherDuck's relationship to it?
MotherDuck is a separate company offering a managed and hybrid service built on the DuckDB engine. DuckDB itself remains MIT-licensed and independent of it, with the IP held by the DuckDB Foundation.
Amazon Aurora: How many read replicas does Aurora support?
Aurora supports up to 15 low-latency read replicas for distributing read traffic across your application.
SourceDuckDB: Is it suitable for OLTP?
No. It is designed for analytical scans. For transactional workloads with frequent small writes, SQLite or Postgres is the right tool.
Related pages
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