# I asked 6 AIs to pick a database, 430 times – Postgres wins, Oracle is invisible > Source: > Published: 2026-07-21 13:02:30+00:00 AI responses, an independent measurement # Which database do AI assistants recommend ## PostgreSQL leads overall, but the winner changes with the need. Across the 12 situations tested, PostgreSQL is the tool most often recommended by the 6 assistants. But in 4 of 12 situations, the tool highlighted varies by AI. The choice therefore depends as much on the stated need as on the assistant used. **58%** **37%** **33%** **4/12** use cases where AIs disagree **430** responses actually analyzed ## The panel**The 14 compared tools** View panel ## Which tools do AIs recommend most often? [¶](#part-de-voix) The average recommendation rate is calculated across all tested situations, weighted equally. One assistant may recommend several tools, so these percentages are not expected to add up to 100%. | # | Tool | Average recommendation rate | | |---|---|---|---| | 1 | PostgreSQL | 58% | | | 2 | MySQL | 37% | | | 3 | SQLite | 33% | | | 4 | MongoDB | 33% | | | 5 | Supabase | 33% | | | 6 | CockroachDB | 27% | | | 7 | Redis | 24% | | | 8 | DynamoDB | 22% | | | 9 | Firebase Firestore | 12% | | | 10 | ClickHouse | 12% | | | 11 | DuckDB | 3% | | | 12 | MariaDB | 2% | | | 13 | Microsoft SQL Server | 1% | | | 14 | Oracle Database | 1% | ## The tool each model spontaneously surfaces [¶](#par-modele) Use case by use case, the tool each model most often puts first (every use case counts the same, chatty or not). Tool most often highlighted by each assistant: **PostgreSQL** (5) · **CockroachDB** (1) ## For the same use case, a different dominant tool depending on the model [¶](#divergences) ## Which use cases AIs associate with which tool [¶](#terrains) For each cell, the percentage shows the share of responses recommending the tool. Select a dot for details. ‘n’ is the number of responses analyzed; results based on few responses are indicative. ↔ Swipe the matrix to explore use cases. | Tool | Database for a SaaS startup MVP | Database for a side project | First database for a beginner learning backend development | Database for a mobile app backend | Database for analytics and reporting | Database for a high-traffic app at scale | Database for an enterprise app with strict compliance | Database for an e-commerce store | Database for a real-time collaborative app | Database for an AI app with vector search (RAG) | Embedded or local-first database for a desktop app | Database for a serverless or edge deployment | |---|---|---|---|---|---|---|---|---|---|---|---|---| | ClickHouse | |||||||||||| | CockroachDB | |||||||||||| | DuckDB | |||||||||||| | DynamoDB | |||||||||||| | Firebase Firestore | |||||||||||| | MariaDB | |||||||||||| | Microsoft SQL Server | |||||||||||| | MongoDB | |||||||||||| | MySQL | |||||||||||| | Oracle Database | |||||||||||| | PostgreSQL | |||||||||||| | Redis | |||||||||||| | SQLite | |||||||||||| | Supabase | Click a dot to show the detail of an association. [Your tool ranks poorly, or is missing? Submit it for the next wave →](#proposer) [Are you a brand? See what AI says about you →](/en/mesurer) ## Why do AIs recommend each tool? [¶](#pourquoi) The main argument the models invoke for each tool, the price they quote, and the caveat they attach. *×7* *×3* *×38* *×8* *×7* *×4* *×2* *×25* *×5* *×5* *×3* *×15* *×2* *×16* *×9* *×7* *×7* *×18* *×11* *×8* *×6* *×21* *×19* *×12* *×11* *×13* *×7* *×4* *×4* *×49* *×4* *×2* *×2* *×19* *×8* *×6* *×3* ## Which assistants give a clear recommendation? [¶](#prescription) Share of answers where the model commits to one tool, vs “it depends…”, vs doesn't commit. *n=70*63 37 *n=72*54 46 *n=72*53 42 6 *n=72*51 47 1 *n=72*39 58 3 *n=72*39 61 ## The same need, a different profile, a different tool [¶](#profils) For each buyer profile, the tools the models surface most (existing answers sliced by profile). *n=30* - ClickHouse97% - PostgreSQL33% - MySQL27% **alternative** *n=24* - PostgreSQL21% - Redis17% - MySQL12% **alternative** *n=24* - Supabase96% - PostgreSQL46% - Firebase Firestore42% **alternative** *n=23* - MySQL87% - PostgreSQL83% - MongoDB70% **alternative** *n=18* - PostgreSQL83% - CockroachDB67% - MongoDB44% **alternative** *n=18* - Redis89% - DynamoDB78% - CockroachDB61% **alternative** ## Other tools that appeared spontaneously [¶](#hors-panel) Comparable tools the models cited spontaneously, beyond the tracked panel: worth watching (or adding to the comparison). ## How do AIs present each tool? [¶](#tonalite) Breakdown of favorable / neutral / critical verified mentions. Click a tool for a favorable and a critical verbatim. ## How it is measured, and what it is not [¶](#methode) ### What we measure *· claude-sonnet-5* *· deepseek-v4-pro* *· gemini-3.5-flash* *· mistral-medium-3-5* *· gpt-5.2-chat* *· grok-4.3* 430 responses · 6 models · 12 use cases · 14 tools · query cost $5.31 Each question asks for advice without offering a list of tools. We measure the tools the assistant chooses to mention spontaneously, then how it presents them. ### What these results do not prove - Not a ranking of the tools' actual quality. - Not a feature or pricing comparison. - We measure model behavior, not the truth. Low-n rates are indicative. ### To cite this page “According to the Opinion Radar observatory dated July 20, 2026, PostgreSQL has the highest average recommendation rate in the tested situations (58%), ahead of MySQL (37%).” Reference link: this page (public methodology included). Attribute the claims to the AI models, never to the tools themselves. ### Ask the question yourself You can reuse these questions to check the result. AI responses may nevertheless vary between attempts and change over time. ### Your tool isn't measured, or ranks poorly? Submit it: it joins the queue for the next wave and will be measured under the same conditions as the panel. ### Discover what AIs recommend, and why Are you a brand or a vendor? Measure precisely where AIs mention you (or don't) and why. [Measure my tool →](/en/mesurer)