MemetikEdition 2026-09

Lists / Startup stack

Best managed databases for startups (2026): What ChatGPT, Claude & Gemini Recommend

Ten models, 50 recorded answers, 17 products named. Supabase leads both counts, AWS RDS and Neon follow, and the per-model split shows where they diverge.

Supabase is the product the models name most often for a new startup: 49 of 50 answers (Supabase 98%), and it is named first in 26 of those answers (Supabase 52%). AWS RDS follows, named in 46 of 50, and Neon in 45 of 50, but neither opens as many answers. Everything below is a managed database service a startup can buy, ranked by how often ten models named it across five fixed buyer prompts.

TL;DR

1. Supabase

Pick Supabase if the team wants the database plus the services around it, and would rather buy auth, storage and APIs once than assemble them from three vendors.

Measured: named 49 of 50 (Supabase 98%), first 26 of 50 (Supabase 52%), average position 2.06.

Supabase is the product the answers treat as the default: Postgres with authentication, row-level security, file storage, realtime subscriptions, edge functions and generated APIs attached. Every model except ChatGPT named it in all five of its answers, which is why its named count sits at the top of the edition. The captured answers also describe the trade: Supabase-specific auth and storage create more coupling than plain Postgres, so a team that later wants to move the database has more to unpick. Choose it when the bundled services replace work the team would otherwise build, and treat the Supabase database as the part that is easiest to move.

Pros

Cons

Pricing: a free tier and paid plans, with point-in-time recovery recorded as a paid feature. Best for: small teams that want the backend services in the same product as the database.

2. AWS RDS

Pick AWS RDS if the application already lives on AWS and the priority is conventional managed Postgres with that cloud’s own networking, identity and compliance controls.

Measured: named 46 of 50 (AWS RDS 92%), first 12 of 50 (AWS RDS 24%), average position 3.02.

AWS RDS is the answer the models give when a startup is already inside AWS. The captured answers describe it as mature and conventional rather than modern: instance-based sizing, private networking, Multi-AZ failover, read replicas, automated backups and IAM integration, with Aurora as the step up when scale justifies it. It is the second most named product in the edition and the only other product named first more than ten times. The trade the answers flag is operational. More configuration and a more complicated pricing model than the startup-oriented platforms. The per-model split on this product is the sharpest signal in the edition, and it is covered in the disagreement section below.

Pros

Cons

Pricing: paid and instance-based, with pricing complexity recorded in the answers. Best for: teams already running on AWS that want native networking, identity and managed Postgres.

3. Neon

Pick Neon if the team already has a backend and wants only the database, with branching for preview environments and compute that scales to zero when nothing is running.

Measured: named 45 of 50 (Neon 90%), first 11 of 50 (Neon 22%), average position 2.4.

Neon is the database-only answer. The captured record describes serverless Postgres that separates storage from compute, branches like Git for preview and staging environments, and scales to zero when nothing is running. Three separate recorded answers, including ChatGPT’s reply to the first prompt, name it as the default pick, which is unusual in a panel where the models usually converge on one product. Two cautions sit in the same answers. Idle databases take a moment to wake, and a production database that must answer instantly should run with compute always active. Teams that want auth and storage bundled are pointed at Supabase instead.

Pros

Cons

Pricing: usage-based, with a free tier recorded. Best for: teams with an existing backend that want Postgres alone, plus branching for preview environments.

4. Google Cloud SQL

Pick Google Cloud SQL if the application is committed to Google Cloud and the requirement is a conventional managed Postgres inside the same network and identity system.

Measured: named 33 of 50 (Google Cloud SQL 66%), first 0 of 50 (Google Cloud SQL 0%), average position 4.03.

Google Cloud SQL is the equivalent of RDS on Google Cloud, and the models treat it the same way: the conventional choice when the application already runs on that cloud. The captured answers describe managed backups, failover, replication, patching, encryption and integration with the rest of Google Cloud, and note that it asks for more infrastructure work than the startup-first platforms. It is named in two thirds of the panel’s answers and never once named first, which makes it the page’s clearest example of a product the models include as an option rather than reach for as the answer. Claude Fable 5 and Gemini 3.5 Flash name it in two of five answers each.

Pros

Cons

Pricing: paid, with committed-use options recorded for sustained workloads. Best for: startups committed to Google Cloud that want provider-native identity, networking and managed Postgres.

5. PlanetScale

Pick PlanetScale if the product is MySQL-native, expects high transactional volume, and schema changes without downtime are a hard requirement.

Measured: named 32 of 50 (PlanetScale 64%), first 0 of 50 (PlanetScale 0%), average position 4.75.

PlanetScale is the MySQL answer. It is built on Vitess, and the captured record describes it as the reliability-first choice for teams that have already committed to MySQL, with schema branching and non-blocking schema changes so a large table can be altered without taking the application offline. Claude’s answer to the first prompt describes it as “MySQL-compatible, serverless, built on Vitess (the tech that powers YouTube’s database).” Postgres support was added later, which the answers note alongside the MySQL heritage. The model split is the interesting part here. Claude Opus 5 and Claude Fable 5 name it in all five of their answers and Claude in four. ChatGPT names it in two, and GPT-5.6 Sol and GPT-5.6 Luna in one each.

Pros

Cons

Pricing: paid, with the free tier recorded as ended. Best for: MySQL-native teams with high transactional volume and a need for zero-downtime schema changes.

6. MongoDB

Pick MongoDB if the data is genuinely document-shaped, with nested records and few relational constraints, and the team wants the managed service run by the vendor that makes the engine.

Measured: named 32 of 50 (MongoDB 64%), first 0 of 50 (MongoDB 0%), average position 5.16.

MongoDB is the document answer, and the recorded advice about it is conditional in a way few other entries are. Atlas is described as a mature managed service with a free tier and multi-cloud coverage, and then the same answers warn against defaulting to it. A team that picks documents because the schema might change is often better served by Postgres with JSON columns. It appears at least once in every model’s answers, and the split runs from five of five for Claude to two of five for Perplexity and GPT-5.6 Sol. No answer places it first.

Pros

Cons

Pricing: free tier recorded, with no public prices in the captured answers. Best for: applications with genuinely nested, document-shaped data and few relational constraints.

7. Turso

Pick Turso when the database has to sit next to users at the edge, or when multi-tenancy means one small isolated database per customer.

Measured: named 16 of 50 (Turso 32%), first 0 of 50 (Turso 0%), average position 5.75.

Turso is the edge answer: a managed database built on libSQL, the open source fork of SQLite. The captured record puts it in the local-first and offline-first bracket, with replicas placed near users, very fast reads, and a model that suits giving each tenant a separate small database. One Sonar Reasoning Pro answer notes that a cost-focused comparison ranked it first among startup database services, which is a different kind of endorsement from being a model’s own default. The split shows who was not persuaded. Neither Claude Fable 5 nor GPT-5.6 Luna named it in any answer, while the Perplexity models named it in three of five each.

Pros

Cons

Pricing: a generous free tier is recorded, with no public prices in the captured answers. Best for: edge, local-first and multi-tenant applications where SQLite is the right engine.

8. Firebase

Pick Firebase when the product is mobile-first and real-time sync matters more than relational structure.

Measured: named 12 of 50 (Firebase 24%), first 0 of 50 (Firebase 0%), average position 4.08.

Firebase sits in the panel as the real-time mobile backend rather than a general database. The captured answers recommend its Realtime Database for mobile and web apps that need synchronised state, and place it beside Firestore and Cloud SQL for teams already inside Google Cloud. Its coverage is the narrowest of the products ranked above it, and the split is uneven. Claude and Gemini 3.5 Flash each named it in three of five answers, Perplexity and GPT-5.6 Sol never named it, and ChatGPT, GPT-5.6 Luna and Claude Fable 5 named it once each. The same answers point relational web products elsewhere. It was never named first.

Pros

Cons

Pricing: no public pricing recorded in the captured answers. Best for: mobile-first products where real-time sync is the point, inside the Google Cloud ecosystem.

9. CockroachDB

Pick CockroachDB only when the workload genuinely needs a distributed relational database with writes in more than one region.

Measured: named 10 of 50 (CockroachDB 20%), first 0 of 50 (CockroachDB 0%), average position 5.9.

CockroachDB is the distributed-relational answer, and the captured record frames it as a decision not to take early. GPT-5.6 Luna lists it for globally distributed relational systems, which is the case it exists for. The split is the sharpest in the edition. Claude Fable 5 named it in four of five answers, GPT-5.6 Sol in two, and ChatGPT, Claude, GPT-5.6 Luna and Claude Opus 5 in one each. The two Google models, Perplexity and Sonar Reasoning Pro never named it at all. Reading that spread is reading a product whose audience is specific rather than general. A startup without multi-region write requirements is pointed back to a single-region Postgres by the same answers.

Pros

Cons

Pricing: no public pricing recorded in the captured answers. Best for: global, strongly consistent relational workloads that genuinely span regions.

10. Northflank

Pick Northflank if the team wants deployment infrastructure and a managed database from one provider rather than a database alone.

Measured: named 7 of 50 (Northflank 14%), first 1 of 50 (Northflank 2%), average position 4.

Northflank appears in this category for a different reason from every other product: it is a platform that offers managed PostgreSQL, MySQL and MongoDB alongside application hosting and CI, and its own blog is the most cited source in the edition. Its named count, 7 of 50, is the smallest of any product that was ever named first. That one first place came from a Perplexity answer. The single most useful thing a buyer can take from this entry is the asymmetry. The most cited host in the corpus writes about databases for a living and is named by the models in a fraction of the answers, which is the finding developed further in the citation section below.

Pros

Cons

Pricing: no public pricing recorded in the captured answers. Best for: teams that want infrastructure and databases managed by one provider.

11. Crunchy Bridge

Pick Crunchy Bridge when Postgres is the core of the product and the team wants a specialist vendor across more than one cloud.

Measured: named 6 of 50 (Crunchy Bridge 12%), first 0 of 50 (Crunchy Bridge 0%), average position 4.

Crunchy Bridge is the Postgres specialist in the set. GPT-5.6 Luna names it the Postgres-first managed service to choose for a team that wants real Postgres with less operational burden and less cloud lock-in, offering managed PostgreSQL across AWS, Google Cloud and Azure with high availability and automated failover. Its coverage in the panel is thin and uneven. Claude Opus 5, Claude, Gemini and Gemini 3.5 Flash all named it, with Gemini 3.5 Flash naming it twice, while five of the ten models never named it. Its average position of 4 matches Northflank’s, which is a high placement for a product this far down the named count.

Pros

Cons

Pricing: no public pricing recorded in the captured answers. Best for: teams where Postgres is the product and multi-cloud portability matters.

12. DigitalOcean

Pick DigitalOcean if the application already runs on a smaller or simpler cloud and the team wants managed Postgres, MySQL, MongoDB and Redis from that provider.

Measured: named 6 of 50 (DigitalOcean 12%), first 0 of 50 (DigitalOcean 0%), average position 5.33.

DigitalOcean’s managed databases appear in the answers as the simple-cloud option: managed PostgreSQL, MySQL, MongoDB and Redis aimed at teams that want a developer-friendly platform without a hyperscaler’s configuration surface. Coverage is thin at 6 of 50 and it never leads an answer. The model split explains why the product is easy to miss. Claude Opus 5 and Sonar Reasoning Pro named it twice each and GPT-5.6 Luna and Claude Fable 5 once each, while both Google models, Claude and Perplexity never named it. A startup already on DigitalOcean has an obvious answer in these counts, and one that is not has no reason to look.

Pros

Cons

Pricing: no public pricing recorded in the captured answers. Best for: teams already hosting on DigitalOcean or a comparable simpler cloud.

13. Upstash

Pick Upstash when the database’s job is caching, sessions or rate limiting beside the main database.

Measured: named 6 of 50 (Upstash 12%), first 0 of 50 (Upstash 0%), average position 7.

Upstash is the only key-value product in the set, and the answers place it in one role: serverless Redis for caching, sessions and rate limiting, with pay-per-request pricing that suits spiky traffic. Every mention comes from two model families. Claude named it twice and Claude Fable 5 three times, Gemini named it once, and no other model named it at all. When it does appear, it sits low in the answer, which is what a supporting product looks like in a category question about primary databases. Upstash earns its place on this page as a companion to one of the databases above, not as a replacement for it.

Pros

Cons

Pricing: pay-per-request, recorded in the answers. Best for: caching, sessions and rate limiting beside a primary database.

14. Cloudflare D1

Pick Cloudflare D1 for read-heavy, geographically distributed applications where SQLite is enough.

Measured: named 4 of 50 (Cloudflare D1 8%), first 0 of 50 (Cloudflare D1 0%), average position 8.

Cloudflare D1 is the second edge product in the set and the more tightly bound one: SQLite at the edge inside Cloudflare’s platform. The captured answers recommend it for lightweight, globally distributed apps and for read-heavy workloads, and rank it beside Turso as the pair of choices for that job. Three models account for all four mentions. Claude Opus 5 named it twice, Claude and GPT-5.6 Sol once each, and the caution Claude Opus 5 recorded about the age of both edge products applies here as well. Average position 8 records where it lands when it does appear.

Pros

Cons

Pricing: no public pricing recorded in the captured answers. Best for: read-heavy, globally distributed applications built on Cloudflare’s platform.

15. Prisma

Pick nothing here yet: Prisma was named as tooling in every captured answer, not as a database to run.

Measured: named 3 of 50 (Prisma 6%), first 0 of 50 (Prisma 0%), average position 4.

Prisma is the entry that most needs reading before acting. The alias list for this category includes Prisma and Prisma Postgres, and three answers named it. All three were OpenAI models, and all three named it as an ORM or a migration tool rather than as the managed database to buy. One lists it as a good fit alongside Django and Rails, one lists it among mainstream ORMs for a deployment plan, and one lists it among migration tools. The counted mentions are real. What they measure is a name appearing in the answer, and here the name belongs to the layer above the database.

Pros

Cons

Pricing: no public pricing recorded in the captured answers. Best for: nothing recorded as a database choice, because all three mentions are tooling around one.

16. Railway

Pick Railway if the team is small, wants deployment and Postgres from one simple platform, and values speed of setup over operational control.

Measured: named 3 of 50 (Railway 6%), first 0 of 50 (Railway 0%), average position 5.67.

Railway appears in the panel as a platform that includes a database rather than a database vendor: fast deployment, a developer-friendly experience, and Postgres available inside the same product as hosting. GPT-5.6 Luna places it in the bracket for very small teams prioritising simplicity, and attaches the caution to “Evaluate operational controls carefully before making it your long-term critical database”. Its blog is one of the vendor-owned hosts the models cite, which is a second reason its name appears here. Three models named it once each: Claude, Claude Opus 5 and GPT-5.6 Luna. No model named it twice.

Pros

Cons

Pricing: no public pricing recorded in the captured answers. Best for: very small teams that want deployment and Postgres from one simple platform.

17. Xata

Pick nothing on this evidence: one model named Xata once, inside a list of Postgres platforms.

Measured: named 1 of 50 (Xata 2%), first 0 of 50 (Xata 0%), average position 9.

Xata is the thinnest record in the edition. It was named once, by Claude, inside a sentence listing the platforms built on Postgres, and that is the whole of its evidence. A single mention is enough for the panel to publish it with a percentage rather than as a never-named product, and it is not enough for a buyer to act on. The honest reading is that the models do not currently reach for Xata when a startup asks for a managed database, whatever the product does. Its average position of 9 is the weakest placement on the page, which means that when it was named, it was named at the end of the list.

Pros

Cons

Pricing: no public pricing recorded in the captured answers. Best for: not established by this edition, because one mention is not a recommendation.

How the tools compare

The category is managed database services: products where a vendor runs the database and the startup buys it as a service. This edition tracked 18 products, the models named 17 of them, and every named product carries a row below. Two measures carry the ranking. Answer share is the share of the 50 recorded answers that named the product. Named first is the share of recorded answers where the product appeared before any other tracked product. Average position is where it sat inside the answers that named it. The full category record, with every recorded answer and the per-model split, sits at /index/startup-databases.

Vendor Named Share Named first First share Average position Pricing model
Supabase 49/50 98% 26/50 52% 2.06 Free tier then paid plans
AWS RDS 46/50 92% 12/50 24% 3.02 Paid, instance-based
Neon 45/50 90% 11/50 22% 2.4 Usage-based with a free tier
Google Cloud SQL 33/50 66% 0/50 0% 4.03 Paid, committed-use options recorded
PlanetScale 32/50 64% 0/50 0% 4.75 Paid, free tier recorded as ended
MongoDB 32/50 64% 0/50 0% 5.16 Free tier recorded
Turso 16/50 32% 0/50 0% 5.75 Free tier recorded
Firebase 12/50 24% 0/50 0% 4.08 No public pricing recorded
CockroachDB 10/50 20% 0/50 0% 5.9 No public pricing recorded
Northflank 7/50 14% 1/50 2% 4 No public pricing recorded
Crunchy Bridge 6/50 12% 0/50 0% 4 No public pricing recorded
DigitalOcean 6/50 12% 0/50 0% 5.33 No public pricing recorded
Upstash 6/50 12% 0/50 0% 7 Pay-per-request
Cloudflare D1 4/50 8% 0/50 0% 8 No public pricing recorded
Prisma 3/50 6% 0/50 0% 4 No public pricing recorded
Railway 3/50 6% 0/50 0% 5.67 No public pricing recorded
Xata 1/50 2% 0/50 0% 9 No public pricing recorded

Supabase leads both counts, named in 49 of 50 answers and named first in 26. The pattern below it matters more than the ranking. Named share stays high a long way down the list while first share collapses after the top three. AWS RDS is named in 46 of 50 answers and opens 12. Neon is named in 45 and opens 11. From fourth place down, Northflank’s single first place is the only one on the page. A buyer reading only the named column sees a wide field of viable products. A buyer reading the first column sees three, and everything below them is named as an alternative to one of those three. The gap is recorded directly: AWS RDS (92% named against 24% first), Neon (90% against 22%), Supabase (98% against 52%). The models handle the engine the same way. ChatGPT’s answer to the comparison prompt puts it simply: “PostgreSQL handles the core startup workload well: transactional data, flexible queries, JSON fields, full-text search, and a large ecosystem.”

Where the models disagree

Every model except ChatGPT leads with Supabase (100% of its own answers): GPT-5.6 Sol, GPT-5.6 Luna, Claude Opus 5, Claude, Claude Fable 5, Gemini, Gemini 3.5 Flash, Perplexity and Sonar Reasoning Pro. ChatGPT leads with AWS RDS (100%), and it is the only model whose leader is not Supabase. That single disagreement is why the edition reports that the models do not agree on a leader, and why a buyer should not read the top row of the table as unanimous.

Below the top, the splits are wider, and they follow lines a buyer can use. All three Anthropic models named PlanetScale in at least four of their five answers, while ChatGPT named it in two and the other two GPT models in one each. Google and Perplexity named CockroachDB in none of their answers, and Claude Fable 5 named it in four of five. Northflank was named by Anthropic, Google and Perplexity models and by no OpenAI model. Upstash was named only by an Anthropic or a Google model. Prisma’s three mentions came from OpenAI models alone. Firebase (60% of Claude answers) shows the opposite lean to Firebase (0%) in the Perplexity and GPT-5.6 Sol answers.

Cloud alignment shows up in the same way. Google Cloud SQL (86.7% of OpenAI answers) is named far more often by OpenAI models than by Google’s own, where Google Cloud SQL (40% of Google answers) is the reading. Neon (73.3% of OpenAI answers) is named less often by that family than by Perplexity, where Neon (100%) is the reading. MongoDB (80% of Anthropic answers) is named more often there than in OpenAI answers, where MongoDB (53.3%) is the reading. None of these products opens an answer.

What the citation record shows

The models do not read the open web evenly in this category. The most cited host behind the 50 answers is northflank.com, at 96 citations, and it belongs to a vendor tracked in the same set. Railway’s blog is next among the vendor-owned hosts at 21 citations, then aws.amazon.com at 16, supabase.com at 15 and cloud.google.com at 12. Northflank’s product is named in 7 of the 50 answers. Railway’s is named in 3.

Those two facts sit together badly if citation volume is read as a route to being named. Claude Opus 5 flagged the reason in its own answer to the comparison prompt: “most of what came back is vendor-authored content (Northflank blog posts ranking Northflank first, Selfhost.dev ranking Selfhost.dev), so treat the rankings as marketing and the raw numbers as the useful part”.

That is the finding this page can show and a cost table cannot. In this category the sources the models retrieve are largely written by sellers, and being the most retrieved seller is not the same as being the most named product. The captured enterprise guide from NetApp Instaclustr has the shape the model described, ranking its own managed service first in a list of options.[cite:s2] The captured DanubeData post argues the self-hosting case and closes by sending the reader to its own account signup page.[cite:s5]

How the sample was built

10 models x 5 fixed prompts = 50 recorded answers, one answer per model-and-question pair, in the 2026-09 edition. The ten models are GPT-5.6 Sol, ChatGPT, GPT-5.6 Luna, Claude Opus 5, Claude, Claude Fable 5, Gemini, Gemini 3.5 Flash, Perplexity and Sonar Reasoning Pro. OpenAI supplied 15 of the answers, Anthropic 15, Google 10 and Perplexity 10.

The five recorded questions were:

  1. “What is the best managed database for a startup? Name specific products.”
  2. “Which managed database would you recommend to a startup in 2026?”
  3. “Compare the top managed database options right now.”
  4. “I’m a startup and I need a managed database. What should I use and why?”
  5. “Best managed database for a Postgres-based SaaS?”

The category’s tracking list held 18 products. Convex was tracked and never named, so it carries no percentage and no row. How the panel runs, what is counted and how answers are recorded is documented at /method.

How this sits against the captured startup database guides

Four comparable pages were captured and read for this edition, and one could not be read.

The NetApp Instaclustr guide ranks managed open source databases for enterprise rather than for startups, and the first entry in its list is its own service.[cite:s2] Pipedrive’s guide covers small business database software, which on that page means business applications with a database behind them rather than managed database services.[cite:s3] Canto’s guide covers databases for product catalogues and finishes by recommending its own product content platform.[cite:s4] DanubeData’s post argues that startups should not self-host and prices the comparison against its own managed service.[cite:s5] The Reddit thread that ranks in the captured results page for this query could not be read, because the site refused the capture, so nothing from it is reported here.

Those pages rank products for a buyer to purchase, and the vendor-published ones among them rank their own service first or price the comparison against it. This page counts the names 50 recorded AI answers produced, reports where each name landed inside its answer, and shows which sources those answers cited. It tests no product and ranks none on quality.

What these counts cannot tell you

What should a buyer do with these counts?

Treat the counts as the shortlist and decide on the things the counts do not contain.

Use the named column to see which products the models will put in front of the next person who asks this question. Use the first column to see which three they lead with, and read every entry below those three as an alternative rather than a default. If the team is already committed to a cloud, take that cloud’s own managed database, because that is what the recorded answers do. If the team wants a database alone, the record points to Neon. If it wants auth, storage and APIs in the same product, it points to Supabase. If the data is genuinely document-shaped, it points to MongoDB. If the workload has to sit close to users, it points to the edge pair, with the recorded caution about both attached.

Then check what this edition cannot: current pricing, the support terms the team will actually need, migration cost off the chosen platform, and whether the product’s failure modes are ones the team can absorb. Nothing on this page replaces that check.

Frequently asked questions

Is MySQL still relevant in 2026?

The models still name MySQL-based managed services in this category. AWS RDS was named in 46 of 50 answers, and its captured competitor entry lists MySQL and MariaDB among the engines it manages alongside PostgreSQL.[cite:s2] Google Cloud SQL, named in 33 of 50 answers, is recorded as a fully managed service for MySQL, PostgreSQL and SQL Server. PlanetScale, named in 32 of 50 answers, is recorded as MySQL-compatible and built on Vitess. What this panel does not measure is whether MySQL or Postgres suits a particular workload, because none of the five prompts asked the models to compare engines.

Is MongoDB still relevant in 2026?

MongoDB is named, and consistently. It appeared in 32 of 50 answers (MongoDB 64%), and at least once in every model’s answers. It was never named first in any of them, which fits the recorded advice that it is the right answer for genuinely document-shaped data and a common mistake when the schema is merely uncertain. Whether the engine is the right one for a given product is a question this measurement cannot answer.

Which database is mostly used in industry?

This panel cannot answer that, because it counts naming rather than adoption, revenue or install base. The closest measured signal is that AWS RDS was the only product named in all five answers by one model, ChatGPT, and that OpenAI’s models named AWS RDS (100% of their answers). That is a statement about how one family of models answers a startup question, not about what industry runs.

Should a startup pick Postgres or a document database?

The recorded answers point to Postgres first and document storage second. PostgreSQL is described as handling relational data, JSON, full-text search and vector search in one engine, which defers the decision. The recorded caution about MongoDB is that teams often reach for documents because the schema might change and later wish they had used Postgres with JSON columns. That caution sits in MongoDB’s own entry above as a claim from the answers, not as a product defect.

Should a startup on AWS or Google Cloud pick its own cloud’s database?

The recorded answers pick each cloud’s own managed database by default. For AWS the answer is AWS RDS, named in 46 of 50 answers. For Google Cloud it is Google Cloud SQL, named in 33. The difference between the two is first place: AWS RDS opens 12 answers and Google Cloud SQL opens none, which suggests the models treat the AWS option as a lead recommendation and the Google option as a fit-for-your-cloud one. A team with no cloud commitment is pointed at the serverless Postgres options instead.

How this list is ordered

The order is the measurement, not an assessment of the products. Answer share is the share of recorded answers that named the tool. Named first is the share where it appeared before any other tracked tool. Both are counts from one dated edition and are published in full on the category page.

A tool appears here only if it was named in the edition and its record carries a sourced claim. A product that was never named is not listed, and no position is sold.

Where to check it