Managed relational databases
Asked as “managed relational database service”, and as “hosted SQL database”, on behalf of a mid-market B2B company. 58 first choices recorded across the direct, paraphrase, budget and scale prompts, fourteen models each.
Standing · first-choice share
45%
Clear leader
45Amazon RDS12Amazon Aurora10Google Cloud SQL33others
45% of first choices, clear leader.
Since September 2026▲+7Since September 2026: 35% → 42%, +7 points. Inside the 11-point floor: within noise. Read over the models both editions asked.Amazon RDS held the lead, +7 points on 35%, inside the 11-point floor.
By buyer segment
The same question asked on behalf of a different buyer. Each standing is computed within its segment; they sit side by side and are never added together.
Small business
Amazon RDS24%Contested▼−2Since September 2026: 24% → 22%, −2 points. Inside the 11-point floor: within noise. Read over the models both editions asked.Standing →Mid-marketAmazon RDS45%Clear leader▲+7Since September 2026: 35% → 42%, +7 points. Inside the 11-point floor: within noise. Read over the models both editions asked.This pageEnterprise
Amazon Aurora26%Contested↗new leaderNew leader since September 2026: Amazon Aurora (30%) replaces Amazon RDS (36% then, 20% now), 9 points clear, inside the 11-point floor.Standing →
Amazon RDS24%Contested▼−2Since September 2026: 24% → 22%, −2 points. Inside the 11-point floor: within noise. Read over the models both editions asked.Standing →Mid-marketAmazon RDS45%Clear leader▲+7Since September 2026: 35% → 42%, +7 points. Inside the 11-point floor: within noise. Read over the models both editions asked.This pageEnterpriseAmazon Aurora26%Contested↗new leaderNew leader since September 2026: Amazon Aurora (30%) replaces Amazon RDS (36% then, 20% now), 9 points clear, inside the 11-point floor.Standing →The standing
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. Ordered by share.
ProductFirst-choice shareNegative rateLabelsQuadrantSince September 2026
01Amazon RDS45%23%70endorsed leader▲+7Since September 2026: 35% → 42%, +7 points. Inside the 11-point floor: within noise. Read over the models both editions asked.35% → 42%
Amazon RDS45%23%70endorsed leader▲+7Since September 2026: 35% → 42%, +7 points. Inside the 11-point floor: within noise. Read over the models both editions asked.35% → 42%02Amazon Aurora12%21%39accepted challenger▲+3Since September 2026: 11% → 14%, +3 points. Inside the 11-point floor: within noise. Read over the models both editions asked.11% → 14%
Amazon Aurora12%21%39accepted challenger▲+3Since September 2026: 11% → 14%, +3 points. Inside the 11-point floor: within noise. Read over the models both editions asked.11% → 14%03
Google Cloud SQL Lab in the set10%12%66accepted challenger▼−1Since September 2026: 11% → 10%, −1 point. Inside the 11-point floor: within noise. Read over the models both editions asked.11% → 10%
Google Cloud SQL Lab in the set10%12%66accepted challenger▼−1Since September 2026: 11% → 10%, −1 point. Inside the 11-point floor: within noise. Read over the models both editions asked.11% → 10%04
Neon10%0%23accepted challenger▲+4Since September 2026: 6% → 10%, +4 points. Inside the 11-point floor: within noise. Read over the models both editions asked.6% → 10%
Neon10%0%23accepted challenger▲+4Since September 2026: 6% → 10%, +4 points. Inside the 11-point floor: within noise. Read over the models both editions asked.6% → 10%05
DigitalOcean Managed Databases9%0%19accepted challenger▲+1Since September 2026: 7% → 8%, +1 point. Inside the 11-point floor: within noise. Read over the models both editions asked.7% → 8%
DigitalOcean Managed Databases9%0%19accepted challenger▲+1Since September 2026: 7% → 8%, +1 point. Inside the 11-point floor: within noise. Read over the models both editions asked.7% → 8%06
Supabase7%6%18accepted challenger▲+4Since September 2026: 4% → 8%, +4 points. Inside the 11-point floor: within noise. Read over the models both editions asked.4% → 8%
Supabase7%6%18accepted challenger▲+4Since September 2026: 4% → 8%, +4 points. Inside the 11-point floor: within noise. Read over the models both editions asked.4% → 8%07
Azure SQL Database2%20%56accepted challenger▼−2Since September 2026: 4% → 2%, −2 points. Inside the 11-point floor: within noise. Read over the models both editions asked.4% → 2%
Azure SQL Database2%20%56accepted challenger▼−2Since September 2026: 4% → 2%, −2 points. Inside the 11-point floor: within noise. Read over the models both editions asked.4% → 2%Show the two products at 0%, ordered by negative rate
09
Google Cloud Spanner Lab in the set0%42%12criticized challenger▼−2Since September 2026: 2% → 0%, −2 points. Inside the 11-point floor: within noise. Read over the models both editions asked.2% → 0%
Google Cloud Spanner Lab in the set0%42%12criticized challenger▼−2Since September 2026: 2% → 0%, −2 points. Inside the 11-point floor: within noise. Read over the models both editions asked.2% → 0%08
PlanetScale0%29%17criticized challenger▼−2Since September 2026: 2% → 0%, −2 points. Inside the 11-point floor: within noise. Read over the models both editions asked.2% → 0%
PlanetScale0%29%17criticized challenger▼−2Since September 2026: 2% → 0%, −2 points. Inside the 11-point floor: within noise. Read over the models both editions asked.2% → 0%The floor is 11 points of share, measured: how far the models move a leader on their own when the same questions are asked twice with nothing changed. A larger change is movement; a smaller one is noise, and both are shown. Movement is read over the twelve models both editions asked; GPT-6 Luna, Muse Glimmer 30B joined this edition and are in the standing but not yet in the comparison. How the floor is measured
Bars are the share of first choices, 0 to 100Every product with at least 10 labels here. Every product name links to its product page.
Google Cloud SQL is made by Google, whose model Gemini 3.5 Flash is in the set. On the four share prompts that model made it the first choice zero times of 4; the other thirteen models six times of 52. Google Cloud Spanner is made by Google, whose model Gemini 3.5 Flash is in the set. On the four share prompts that model made it the first choice zero times of 4; the other thirteen models zero times of 52. Lab treatment is defined on the method page; the row is marked, not excluded.
All twenty-one head-to-head pages: the top seven products, each against each
Amazon RDS vs DigitalOcean Managed DatabasesAmazon RDS vs SupabaseAmazon RDS vs Azure SQL DatabaseAmazon Aurora vs Google Cloud SQLAmazon Aurora vs NeonAmazon Aurora vs DigitalOcean Managed DatabasesAmazon Aurora vs SupabaseAmazon Aurora vs Azure SQL DatabaseGoogle Cloud SQL vs NeonGoogle Cloud SQL vs DigitalOcean Managed DatabasesGoogle Cloud SQL vs SupabaseGoogle Cloud SQL vs Azure SQL DatabaseNeon vs DigitalOcean Managed DatabasesNeon vs SupabaseNeon vs Azure SQL DatabaseDigitalOcean Managed Databases vs SupabaseDigitalOcean Managed Databases vs Azure SQL DatabaseSupabase vs Azure SQL Database
Recommended versus criticized
Every product with at least 10 labels here, on both axes. The 30% line names a quadrant, not the verdict above: that one needs more than 40%.
Criticized challengerCriticized default
Negative label rate →
010203040506070809Accepted challengerEndorsed leader
0%First-choice share → · lines at 30% share and 25% negative60%
Key
01Amazon RDS45%
Amazon RDS45%02Amazon Aurora12%
Amazon Aurora12%03Google Cloud SQL10%
Google Cloud SQL10%04Neon10%
Neon10%05DigitalOcean Managed Databases9%
DigitalOcean Managed Databases9%06Supabase7%
Supabase7%07Azure SQL Database2%
Azure SQL Database2%08PlanetScale0%
PlanetScale0%09Google Cloud Spanner0%
Google Cloud Spanner0%What they warned about
Five of fourteen models held their first choice under the paraphrase. Claude Haiku 4.5, GPT-5.4 mini, Gemini 3.5 Flash, Perplexity Sonar, Grok 4.1 Fast, Mistral Small, Llama 4 Maverick, GLM 4.7 FlashX and MiniMax M2.5 changed. A high negative share on a product with few labels is a warning. A low share on a product with many labels is salience, not sentiment.
Amazon RDS23%
16 of 70 labels negative · 12 of 14 models · 1 hard negative
“Some models explicitly recommend avoiding RDS for production core systems unless absolutely necessary.” Mistral Small, negative prompt
Azure SQL Database20%
11 of 56 labels negative · 11 of 14 models
“Azure SQL Database, while generally robust, has resource caps and licensing implications that require careful planning” Mistral Small, negative prompt
Google Cloud SQL12%
8 of 66 labels negative · 8 of 14 models
“extended support is paid, and Cloud SQL automatically enrolls EOL-version instances. Check the exact engine version and upgrade dates before committing.” GPT-6 Luna, negative prompt
Amazon Aurora21%
8 of 39 labels negative · 7 of 14 models · 1 hard negative
“Experts advise caution or avoidance for production environments where stable performance is critical.” Mistral Small, negative prompt
What they cite
Citations exist only for the models that return a source list: fourteen of the fourteen in this edition, and all six flagship models on the expanded tier.
Sites the answers cite
73 of 84 answers in this category came back with a source list, from 14 of 14 models: citations where the model returns them, or the search results it consulted. 999 links across 254 sites, every framing counted. Ranked by the number of answers carrying the site or page. 109 of the 252 answers across every segment cited this index's own page for the category; the method page measures whether that reading tilts an answer.
vendor site · DigitalOcean35 answers · 47 citations · 11 models
32 answers · 49 citations · 10 models
vendor site · G228 answers · 57 citations · 10 models
vendor site · Amazon27 answers · 46 citations · 11 models
vendor site · Microsoft27 answers · 37 citations · 11 models
vendor site · Northflank26 answers · 32 citations · 11 models
vendor site · Amazon24 answers · 34 citations · 11 models
23 answers · 25 citations · 10 models
22 answers · 27 citations · 10 models
17 answers · 18 citations · 5 models
15 answers · 15 citations · 8 models
vendor site · ZipDo.co13 answers · 17 citations · 7 models
Pages the answers cite
The ten pages named in the most answers, by full address. A page here is one the models returned with a recommendation, not one the index endorses.
25 answers · 8 models
23 answers · 10 models
22 answers · 10 models
21 answers · 10 models
15 answers · 8 models
13 answers · 5 models
12 answers · 7 models
12 answers · 5 models
11 answers · 8 models
11 answers · 4 models
Search against answers
Each company's standing in the answers beside its site's footprint in Google search, one row a site: the products the models named on it with their shares, and the share they add up to; monthly searches on Google, and DataForSEO's estimate of AI search demand (modeled from search signals, directional, not a count of queries to any assistant), for the most-searched of the company's and its products' names (the name is in each row's hover text); estimated monthly organic visits to the site; and its best position in Google's top ten for “best managed relational database service”, “managed relational database service”, “managed relational database services”. US estimates from DataForSEO and Google's Ads Transparency Center. A small company's site, or a mid-sized company's site for its flagship, is marked company; a product on a large parent's site (Google, Microsoft) has no site figures. A column with no figures for this category is left out, and an empty cell means none were seen, not none exist. Two measurements side by side: neither is read as the cause of the other.
| Company and products | Share | Own site cited | Name searches, Google | AI search demand, est. | Organic visits | Paid search a month, est. | Google ads, last 30 days |
|---|---|---|---|---|---|---|---|
| Amazon aws.amazon.com Amazon RDS 45%, Amazon Aurora 12% | 57% | 168 | 151,000,000 | 1,380,939 | 9,238,321 | $20,476 | |
| Google Cloud cloud.google.com Google Cloud SQL 10% | 10% | 165 | 165,000 | 7,596 | 7,405,347 | ||
| Neon neon.com | 10% | 68 | 135,000 | 66,147 | 73,379 | 1 | |
| DigitalOcean parent site DigitalOcean Managed Databases 9% | 9% | 50 | 74,000 | 942 | |||
| Supabase supabase.com | 7% | 65 | 165,000 | 1,974 | 91,424 | 4 | |
| Azure parent site Azure SQL Database 2%, Azure Database for PostgreSQL 0%, Azure Database for PostgreSQL/MySQL 0%, Azure Database 0% | 2% | 147 | 301,000 | 47,904 | |||
| Vultr vultr.com Vultr Managed Databases 2% | 2% | 4 | 22,200 | 302 | 81,453 company | 95 | |
| Clever Cloud | 2% | ||||||
| Linode linode.com Linode Managed Databases 2% | 2% | 5 | 20 | 0 | 15,263 company | 76 | |
| Aiven aiven.io Aiven 0%, Aiven for PostgreSQL 0% | 0% | 18 | 1,600 | 21 | 10,024 | $59 | 26 |
Google's top ten for the three searches hold 17 sites; 2 of them are among the sites the models cited here (g2.com, learn.microsoft.com). The first result for “best managed relational database service” is trustradius.com.
How they answered
Six framings of the same buying question, each sent to every model in a fresh session with search on. One row per model, so a row shows whether it held its answer under rewording, what it named when cost was the constraint, and what it argued against. Computed from the raw judge labels.ShowHide
How they answered
Six framings of the same buying question, each sent to every model in a fresh session with search on. One row per model, so a row shows whether it held its answer under rewording, what it named when cost was the constraint, and what it argued against. Computed from the raw judge labels.
ShowHideModelDirect“What is the best managed relational database service for a mid-market B2B company?”Paraphrase“Which hosted SQL database would you recommend to a mid-sized B2B company?”Comparative“What are the top managed relational database services and how do they differ?”Budget-constrained“What is the best managed relational database service for a company with a limited budget?”Scale-constrained“We are a 500 person company evaluating a managed relational database service. What should we look at?”Negative“Which managed relational database services should I avoid or be cautious about?”
Claude Haiku 4.5Amazon RDSAmazon RDS, Google Cloud SQLChangedagainst: Heroku PostgresAmazon RDSAmazon RDSagainst: MariaDB, MySQLno first choiceagainst: Amazon Aurora, Amazon RDS, Azure Database, Google Cloud SQL
One alternative
Azure SQL DatabaseOne alternative
DigitalOcean Managed DatabasesTwo alternatives
Azure SQL Database, Google Cloud SQLThree alternatives
Azure Database for MySQL/PostgreSQL, Google Cloud SQL, Heroku PostgresGPT-5.4 miniAmazon Aurora, Amazon RDSAmazon RDSChangedAmazon RDSAmazon RDS, Google Cloud SQLno first choiceagainst: Amazon RDS, Azure SQL Database, Azure SQL Managed Instance, Google Cloud SQL
Three alternatives
Azure Database for PostgreSQL, Azure SQL Database, Google Cloud SQLSix alternatives
Amazon Aurora, Azure Database for PostgreSQL, Azure SQL Database, Google Cloud SQL, Microsoft SQL Server, MySQLFive alternatives
Amazon Aurora, Azure SQL Database, Google Cloud SQL, Google Cloud Spanner, Oracle Autonomous DatabaseOne alternative
Azure SQL DatabaseGemini 3.5 FlashAmazon AuroraAmazon Aurora, NeonChangedAmazon AuroraNeonagainst: Amazon RDS, Azure SQL Database, Google Cloud SQLno first choiceagainst: AWS Aurora Serverless, Amazon Aurora, Google Cloud Spanner, Heroku Postgres, PlanetScale, Supabase
Four alternatives
Aiven, AlloyDB for PostgreSQL, Azure SQL Database, NeonFour alternatives
Azure SQL Database, Google Cloud SQL, PlanetScale, SupabaseSix alternatives
Amazon RDS, Azure SQL Database, CockroachDB Cloud, Google Cloud Spanner, Neon, PlanetScaleFour alternatives
DigitalOcean Managed Databases, Northflank, Supabase, TursoSix alternatives
Amazon RDS, CockroachDB, Google Cloud SQL, Google Cloud Spanner, Neon, SupabasePerplexity SonarAmazon RDSAmazon Aurora, Amazon RDSChangedAmazon RDS, Azure SQL Database, Google Cloud SQLDigitalOcean Managed Databasesagainst: Amazon RDSno first choiceagainst: Amazon RDS, Azure Database for MySQL, Azure SQL Database, Google Cloud SQL, PostgreSQL
Two alternatives
Azure SQL Database, Google Cloud SQLThree alternatives
Azure SQL Database, Google Cloud SQL, MySQLTwo alternatives
Oracle Autonomous Database, PlanetScaleOne alternative
NorthflankGrok 4.1 FastGoogle Cloud SQLAmazon RDSChangedno first choiceNeon, Supabaseagainst: Amazon RDSAmazon RDSagainst: Aiven, Amazon RDS, Azure SQL Database, DO Managed DBs, Google Cloud SQL, Linode Managed Databases, Oracle Autonomous Database
Four alternatives
Aiven, Amazon RDS, Azure SQL Database, DigitalOcean Managed DatabasesThree alternatives
Azure SQL Database, DigitalOcean Managed Databases, Google Cloud SQLTwo alternatives
DigitalOcean Managed Databases, PlanetScaleFour alternatives
Aiven, Azure SQL Database, Google Cloud SQL, InstaclustrMistral SmallAmazon Aurorano first choiceChangedno first choiceClever Cloudno first choiceagainst: Amazon Aurora, Amazon RDS, Azure SQL Database, PlanetScale
Two alternatives
Azure SQL Database, Google Cloud SQLTwo alternatives
Google Cloud SQL, NeonDeepSeek V4 FlashAmazon RDSAmazon RDSHeldno first choiceSupabaseagainst: PlanetScaleno first choiceagainst: CockroachDB, Google Cloud Spanneragainst: Amazon Aurora, Amazon RDS, Azure SQL Database, Oracle Database, PlanetScale
Two alternatives
Azure SQL Database, Google Cloud SQLThree alternatives
Azure Database for PostgreSQL/MySQL, Azure SQL Database, Google Cloud SQLSix alternatives
Amazon Lightsail Managed Databases, DigitalOcean Managed Databases, Neon, Railway, Render Postgres, Selfhost.devFour alternatives
Aiven, Amazon RDS, Azure SQL Database, Google Cloud SQLLlama 4 MaverickAmazon Aurora, Amazon RDSAmazon AuroraChangedAmazon RDSDigitalOcean Managed Databases, Linode Managed Databases, Vultr Managed Databasesno first choiceagainst: Amazon RDS
Two alternatives
Ntirety, Rackspace TechnologyTwo alternatives
Azure SQL Database, DigitalOcean Managed MySQLOne alternative
PythianTwo alternatives
Amazon Aurora, Amazon RDSQwen 3.7 FlashAmazon RDSAmazon RDSHeldno first choiceDigitalOcean Managed Databasesagainst: Oracle Cloud Free Tierno first choiceagainst: AWS Aurora Global, AWS RDS with Oracle Licensing, Amazon Aurora, Azure SQL Database, Azure SQL Elastic Pools, Azure SQL Hyperscale, CockroachDB, Google Cloud Spanner, Managed Instance
Three alternatives
Azure Database for SQL Server / PostgreSQL, Neon, Vercel PostgresTwo alternatives
Azure SQL Database, Google Cloud SQLThree alternatives
Amazon Web Services (AWS) RDS, Neon, SupabaseKimi K2Amazon RDSAmazon RDSHeldAmazon Aurora, Amazon RDSNeon, Supabaseagainst: Amazon RDSAmazon RDSagainst: Amazon Aurora, Amazon RDS, Aurora DSQL, Azure Cosmos DB, Azure SQL Database, ClearDB, Google Cloud SQL, Google Cloud Spanner, Heroku Postgres
Four alternatives
Amazon Aurora, Azure SQL Database, Google Cloud SQL, NeonFour alternatives
Amazon Aurora, Azure SQL Database, DigitalOcean Managed Databases, Google Cloud SQLTwo alternatives
Azure SQL Database, Google Cloud SQLTwo alternatives
DigitalOcean Managed Databases, RailwayTwo alternatives
Azure SQL Database, Google Cloud SQLGLM 4.7 FlashXGoogle Cloud SQLAmazon RDS, Google Cloud SQLChangedAmazon Aurora, Amazon RDSagainst: AlloyDB for PostgreSQLNeon, Supabaseagainst: Azure SQL Databaseno first choiceagainst: Amazon RDS, Elasticsearch / OpenSearch, Managed MySQL Services, MongoDB Atlas, Redis
Two alternatives
Amazon RDS, Azure DatabaseOne alternative
Azure SQL DatabaseFive alternatives
AlloyDB for PostgreSQL, Azure Cosmos DB for PostgreSQL, Azure SQL Database, Google Cloud SQL, Google Cloud SpannerFour alternatives
Aiven for PostgreSQL, Amazon RDS, DigitalOcean Managed Databases, Google Cloud SQLMiniMax M2.5Amazon RDSAmazon RDS, Azure SQL DatabaseChangedAmazon Aurora, Amazon RDSDigitalOcean Managed Databasesagainst: BuyVM Offloaded SQLno first choiceagainst: Amazon Aurora Serverless, Amazon RDS, Azure SQL Database, PlanetScale
Three alternatives
Amazon Aurora, DigitalOcean Managed Databases, Google Cloud SQLOne alternative
Google Cloud SQLThree alternatives
Azure SQL Database, Google Cloud SQL, Oracle Autonomous DatabaseTwo alternatives
MongoDB Atlas Serverless, NeonGPT-6 LunaAmazon RDSAmazon RDSHeldagainst: Amazon Aurorano first choiceagainst: Google AlloyDB for PostgreSQL, Google Cloud SpannerNeonno first choiceagainst: Amazon Aurora Serverless, Amazon RDS for MySQL 5.7 or 8.0, Azure Database for MariaDB, Azure Database for PostgreSQL – Single Server, Google Cloud SQL
Three alternatives
Amazon Aurora, Azure Database for PostgreSQL, Google Cloud SQLTwo alternatives
Azure Database for PostgreSQL, Google Cloud SQLOne alternative
SupabaseMuse Glimmer 30BAmazon RDSAmazon RDSHeldAmazon Aurora, Amazon RDSDigitalOcean Managed Databasesagainst: Amazon RDSAmazon RDS, Google Cloud SQLagainst: Amazon RDS, Azure SQL Database, Google Cloud SQL
Three alternatives
Amazon Aurora, Azure SQL Database, Google Cloud SQLThree alternatives
Azure Database for PostgreSQL/MySQL, Azure SQL Database, Google Cloud SQLTwo alternatives
Azure SQL Database, Google Cloud SQLTwo alternatives
Neon, SupabaseOne alternative
Azure SQL DatabaseBold is the first choiceAlternatives are counted; the count opens them.What the answer argued against
The record
One row per call: the version string exactly as returned, whether the model searched, sources cited, and latency. Full answer text is in the free responses file. Download the record
Eighty-four rows: every prompt, every model, every answer.
| Prompt | Model | Version string | Time (UTC) | Searched | Sources | Latency |
|---|---|---|---|---|---|---|
| Direct recommendation | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 07:47 | no | 0 | 4 s |
| Direct recommendation | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 08:48 | yes | 2 | 5 s |
| Direct recommendation | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 13:48 | yes | 9 | 24 s |
| Direct recommendation | Perplexity Sonar | sonar | 2026-10-01 11:51 | yes | 19 | 3 s |
| Direct recommendation | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 12:18 | yes | 25 | 6 s |
| Direct recommendation | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 13:46 | yes | 13 | 6 s |
| Direct recommendation | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 09:27 | yes | 21 | 25 s |
| Direct recommendation | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 09:32 | yes | 5 | 1 s |
| Direct recommendation | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 08:36 | no | 0 | 35 s |
| Direct recommendation | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 08:34 | yes | 14 | 24 s |
| Direct recommendation | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 08:23 | yes | 15 | 42 s |
| Direct recommendation | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 12:22 | yes | 19 | 44 s |
| Direct recommendation | GPT-6 Luna | gpt-6-luna | 2026-10-01 13:42 | yes | 3 | 17 s |
| Direct recommendation | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 10:09 | yes | 21 | 24 s |
| Paraphrase | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 12:27 | no | 0 | 5 s |
| Paraphrase | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 10:45 | yes | 4 | 4 s |
| Paraphrase | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 08:26 | yes | 10 | 25 s |
| Paraphrase | Perplexity Sonar | sonar | 2026-10-01 08:32 | yes | 17 | 2 s |
| Paraphrase | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 10:56 | yes | 24 | 7 s |
| Paraphrase | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 13:11 | no | 0 | 2 s |
| Paraphrase | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 12:06 | yes | 18 | 25 s |
| Paraphrase | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 10:57 | yes | 5 | 1 s |
| Paraphrase | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 12:01 | yes | 10 | 48 s |
| Paraphrase | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 12:04 | yes | 13 | 20 s |
| Paraphrase | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 10:41 | no | 0 | 27 s |
| Paraphrase | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 07:54 | yes | 5 | 39 s |
| Paraphrase | GPT-6 Luna | gpt-6-luna | 2026-10-01 09:35 | yes | 2 | 10 s |
| Paraphrase | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 08:58 | yes | 14 | 18 s |
| Comparative | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 11:14 | yes | 18 | 11 s |
| Comparative | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 09:23 | yes | 6 | 8 s |
| Comparative | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 13:54 | yes | 17 | 36 s |
| Comparative | Perplexity Sonar | sonar | 2026-10-01 12:31 | yes | 18 | 5 s |
| Comparative | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 09:41 | yes | 15 | 7 s |
| Comparative | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 08:23 | yes | 8 | 7 s |
| Comparative | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 08:54 | yes | 24 | 42 s |
| Comparative | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 10:43 | yes | 5 | 1 s |
| Comparative | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 09:46 | yes | 5 | 25 s |
| Comparative | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 11:00 | yes | 14 | 35 s |
| Comparative | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 08:16 | yes | 13 | 81 s |
| Comparative | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 13:50 | yes | 10 | 29 s |
| Comparative | GPT-6 Luna | gpt-6-luna | 2026-10-01 12:31 | yes | 6 | 17 s |
| Comparative | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 08:11 | yes | 14 | 33 s |
| Budget constrained | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 07:48 | no | 0 | 6 s |
| Budget constrained | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 12:39 | yes | 3 | 5 s |
| Budget constrained | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 08:49 | yes | 13 | 25 s |
| Budget constrained | Perplexity Sonar | sonar | 2026-10-01 10:30 | yes | 19 | 2 s |
| Budget constrained | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 09:40 | yes | 25 | 9 s |
| Budget constrained | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 09:46 | yes | 15 | 29 s |
| Budget constrained | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 07:42 | yes | 25 | 33 s |
| Budget constrained | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 08:49 | yes | 5 | 1 s |
| Budget constrained | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 09:04 | no | 0 | 30 s |
| Budget constrained | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 07:49 | yes | 20 | 22 s |
| Budget constrained | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 10:51 | yes | 23 | 61 s |
| Budget constrained | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 11:36 | yes | 18 | 47 s |
| Budget constrained | GPT-6 Luna | gpt-6-luna | 2026-10-01 07:56 | yes | 2 | 18 s |
| Budget constrained | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 12:09 | yes | 15 | 19 s |
| Scale constrained | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 12:48 | no | 0 | 5 s |
| Scale constrained | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 09:55 | no | 0 | 8 s |
| Scale constrained | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 13:47 | no | 0 | 15 s |
| Scale constrained | Perplexity Sonar | sonar | 2026-10-01 12:58 | yes | 16 | 6 s |
| Scale constrained | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 07:45 | yes | 20 | 8 s |
| Scale constrained | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 12:16 | no | 0 | 8 s |
| Scale constrained | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 11:17 | yes | 23 | 72 s |
| Scale constrained | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 10:40 | yes | 5 | 1 s |
| Scale constrained | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 09:50 | yes | 13 | 59 s |
| Scale constrained | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 12:41 | yes | 15 | 27 s |
| Scale constrained | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 10:15 | yes | 14 | 23 s |
| Scale constrained | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 08:06 | yes | 5 | 45 s |
| Scale constrained | GPT-6 Luna | gpt-6-luna | 2026-10-01 09:50 | yes | 4 | 16 s |
| Scale constrained | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 08:54 | yes | 15 | 27 s |
| Negative framing | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 11:19 | yes | 18 | 10 s |
| Negative framing | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 11:33 | yes | 3 | 8 s |
| Negative framing | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 12:55 | yes | 18 | 27 s |
| Negative framing | Perplexity Sonar | sonar | 2026-10-01 13:36 | yes | 20 | 6 s |
| Negative framing | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 10:52 | yes | 24 | 8 s |
| Negative framing | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 08:25 | yes | 5 | 4 s |
| Negative framing | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 12:21 | yes | 24 | 40 s |
| Negative framing | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 08:50 | yes | 5 | 2 s |
| Negative framing | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 12:27 | yes | 20 | 74 s |
| Negative framing | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 11:12 | yes | 25 | 21 s |
| Negative framing | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 10:39 | yes | 24 | 25 s |
| Negative framing | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 08:46 | yes | 10 | 33 s |
| Negative framing | GPT-6 Luna | gpt-6-luna | 2026-10-01 09:17 | yes | 5 | 18 s |
| Negative framing | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 09:56 | yes | 24 | 32 s |
Normalization in this category
Every judgment call made between the raw labels and the numbers above, listed so it is visible and reversible.
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Category-scoped readings
AWS RDS / Aurora read as Amazon RDS
AWS RDS/Aurora read as Amazon RDS
AlloyDB read as AlloyDB for PostgreSQL
Amazon RDS/Aurora read as Amazon RDS
Azure SQL read as Azure SQL Database
DigitalOcean read as DigitalOcean Managed Databases
Linode read as Linode Managed Databases
Oracle Autonomous AI Database read as Oracle Autonomous Database
Oracle Database Cloud Service read as Oracle Base Database Service
Render read as Render Postgres
Unresolved, counted raw
AWS Aurora Global
AWS Aurora MySQL
AWS Aurora Serverless (v2)
AWS RDS with Oracle Licensing
Amazon Lightsail Managed Databases
Amazon RDS for MySQL 5.7 or 8.0
Amazon Web Services (AWS) RDS
Azure Cosmos DB for PostgreSQL
Azure Database for PostgreSQL – Single Server
Azure Database for SQL Server / PostgreSQL
Azure SQL Elastic Pools
Azure SQL Hyperscale
BuyVM Offloaded SQL
ClearDB
Clever Cloud
CockroachDB Cloud
DO Managed DBs
DigitalOcean Managed MySQL
IBM Db2 managed offerings
Managed Instance
Managed MySQL Services (various providers)
MariaDB
MongoDB Atlas Serverless
OVH Managed DBs
RDS with MySQL/PostgreSQL
SQL Database in Microsoft Fabric
Discontinued, still offered
No shut-down product was recommended here.