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.
Named in two categories this edition.
Named in two categories this edition.
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.
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.
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| 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 |
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.
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
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
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.