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Managed relational databases · October 2026 Edition

Amazon RDS vs Amazon Aurora

Ten of fourteen models named Amazon RDS first on the direct prompt; four named Amazon Aurora. Amazon RDS was named by fourteen of the fourteen models and Amazon Aurora by thirteen and Amazon RDS carries 70 labels and Amazon Aurora 39, so the shares are not directly comparable.

Amazon RDS

endorsed leader

Named in two categories this edition.

Amazon Aurora

accepted challenger

Named in two categories this edition.

First-choice share45%12%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate23%21%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#1#2A position in a field of 9; printed, not drawn.
Labels7039A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Amazon RDS reading right to left. Rank and label count are printed, not drawn.Google Cloud SQL was named alongside these two in ten of the fourteen direct answers. Amazon RDS vs Google Cloud SQL · Amazon RDS vs Neon · Amazon RDS vs DigitalOcean Managed Databases

Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all fourteen models, for a mid-market B2B company; rank is within the category; every quote names the model and the prompt it came from. Both figures come from the managed relational databases page.

By framing

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
Amazon RDSFirst choices, of fourteen modelsAmazon Aurora
Direct104
Paraphrase1131 against Amazon Aurora
Comparative85
Budget-constrained205 against Amazon RDS
Scale-constrained30
Negative0011 against Amazon RDS · 6 against Amazon Aurora
Bars are first choices, 0 to 14 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to fourteen.

Across every category in the October 2026 Edition, Amazon RDS and Amazon Aurora were named in the same answer seventy-six times, of the 189 answers naming Amazon RDS and the 107 naming Amazon Aurora. In those answers Amazon Aurora took the first choice five times and Amazon RDS twenty.

Every model, every framing

The eighty-four answers behind the chart above, one cell each: where Amazon RDS and Amazon Aurora stood in it.
ModelDirectParaphraseComparativeBudget-constrainedScale-constrainedNegative
Claude Haiku 4.5
GPT-5.4 mini
Gemini 3.5 Flash
Perplexity Sonar
Grok 4.20
Mistral Small
DeepSeek V4 Flash
Llama 4 Maverick
Qwen 3.7 Flash
Kimi K2
GLM 4.7 FlashX
MiniMax M2.5
GPT-6 Luna
Muse Glimmer 30B
Amazon RDS Amazon Aurora first choice named as an alternative argued againstblank: not namedEach cell is one answer, Amazon RDS on the left and Amazon Aurora on the right.

The direct prompt

The plain question, one answer per model, grouped by where Amazon RDS and Amazon Aurora stood in it.

Both were the first choice

2 of 14 modelsThe answer named them together, and the judge labeled each a first choice.
GPT-5.4 miniAmazon Aurora, Amazon RDS alternatives: Azure Database for PostgreSQL, Azure SQL Database, Google Cloud SQL
Llama 4 MaverickAmazon Aurora, Amazon RDS alternatives: Ntirety, Rackspace Technology

Amazon RDS first, Amazon Aurora an alternative

8 of 14 modelsAmazon Aurora was named in the answer but not as the choice, or not at all.
Claude Haiku 4.5Amazon RDS alternatives: Azure SQL Database
Perplexity SonarAmazon RDS alternatives: Azure SQL Database, Google Cloud SQL
DeepSeek V4 FlashAmazon RDS alternatives: Azure SQL Database, Google Cloud SQL
Qwen 3.7 FlashAmazon RDS alternatives: Azure Database for SQL Server / PostgreSQL, Neon, Vercel Postgres
Kimi K2Amazon RDS alternatives: Amazon Aurora, Azure SQL Database, Google Cloud SQL, Neon
MiniMax M2.5Amazon RDS alternatives: Amazon Aurora, DigitalOcean Managed Databases, Google Cloud SQL
GPT-6 LunaAmazon RDS alternatives: Amazon Aurora, Azure Database for PostgreSQL, Google Cloud SQL
Muse Glimmer 30BAmazon RDS alternatives: Amazon Aurora, Azure SQL Database, Google Cloud SQL

Amazon Aurora first, Amazon RDS not the choice

2 of 14 modelsAmazon RDS was named in the answer but not as the choice, or not at all.
Gemini 3.5 FlashAmazon Aurora alternatives: Aiven, AlloyDB for PostgreSQL, Azure SQL Database, Neon
Mistral SmallAmazon Aurora alternatives: Azure SQL Database, Google Cloud SQL

Neither was the first choice, one was named

2 of 14 modelsThe answer put something else first and named one of the two as an alternative.
Grok 4.20Google Cloud SQL alternatives: Aiven, Amazon RDS, Azure SQL Database, DigitalOcean Managed Databases
GLM 4.7 FlashXGoogle Cloud SQL alternatives: Amazon RDS, Azure Database

Bold names in an answer are the products the judge labeled a first choice; a model naming several gives each of them that label. The full answer text for every row is in the record.

By buyer segment

The same question asked on behalf of a different buyer. Each standing is computed within its segment and they are never added together. The figures above are the mid-market standing, which is the one the category orders by.
Small business
Amazon RDS leads by twenty-two points.
Amazon RDS24%#1 of 8
Amazon Aurora1%#7 of 8
The full small business standing →
Mid-marketThe figures above
Amazon RDS leads by thirty-three points.
Amazon RDS45%#1 of 9
Amazon Aurora12%#2 of 9
The full mid-market standing →
Enterprise
The order flips: Amazon Aurora leads at enterprise.
Amazon Aurora26%#1 of 8
Amazon RDS20%#3 of 8
The full enterprise standing →

What the models said about Amazon RDS

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.

“Some models explicitly recommend avoiding RDS for production core systems unless absolutely necessary.” Mistral Small · negative prompt · hard negative
“are cautious with AWS RDS SQL Server for point-in-time restore expectations and with any RDS engine for globally distributed strong-consistency writes” Muse Glimmer 30B · negative prompt · soft negative
“It is widely used and legitimate, but multiple sources note that it can carry premium instance pricing, hidden ancillary costs” Perplexity Sonar · negative prompt · soft negative
“Amazon RDS, for example, supports MySQL, PostgreSQL, MariaDB, SQL Server, Oracle, and Amazon Aurora, and automates provisioning, patching, backups, and failover.” Llama 4 Maverick · comparative prompt · first choice
“Pick AWS RDS/Aurora for broadest engine breadth and ecosystem maturity, and for cloud-native Aurora performance with MySQL/PostgreSQL compatibility.” Muse Glimmer 30B · comparative prompt · first choice
“The top managed relational database services are Amazon RDS, Google Cloud SQL, Azure SQL Database, and Oracle Autonomous Database” Perplexity Sonar · comparative prompt · first choice

What the models said about Amazon Aurora

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.

“Experts advise caution or avoidance for production environments where stable performance is critical.” Mistral Small · negative prompt · hard negative
“Aurora uses internal optimizations and sometimes proprietary features ... that can make migrating back to standard MySQL/Postgres difficult” Qwen 3.7 Flash · negative prompt · soft negative
“While Aurora is popular, be cautious about: Complex pricing - I/O charges can escalate unpredictably” Kimi K2 · negative prompt · soft negative
“a recommended hosted SQL database could be PostgreSQL with a cloud-managed service like AWS Aurora, as it balances capability, cost, and operational simplicity” Llama 4 Maverick · paraphrase prompt · first choice
“Pick AWS RDS/Aurora for broadest engine breadth and ecosystem maturity, and for cloud-native Aurora performance with MySQL/PostgreSQL compatibility.” Muse Glimmer 30B · comparative prompt · first choice
“is Amazon Relational Database Service (RDS) or Amazon Aurora, as they are fully managed relational database services” Llama 4 Maverick · direct prompt · first choice
Also compared

Comparisons are drawn for the top eight products in each category, each against each. The output is the models' output; nothing here is a recommendation by the index.