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NoSQL databases · November 2026 Edition

PostgreSQL vs Apache CouchDB

Zero of fourteen models named PostgreSQL first on the direct prompt; zero named Apache CouchDB. PostgreSQL was named by eight of the fourteen models and Apache CouchDB by seven and PostgreSQL carries 13 labels and Apache CouchDB 12, so the shares are not directly comparable.

PostgreSQL

accepted challenger

Named in three categories this edition.

Apache CouchDB

criticized challenger

Named in one category this edition.

First-choice share4%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate0%42%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#3#7A position in a field of 14; printed, not drawn.
Labels1312A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, PostgreSQL reading right to left. Rank and label count are printed, not drawn.MongoDB was named alongside these two in fourteen of the fourteen direct answers. MongoDB vs PostgreSQL · MongoDB vs Apache CouchDB · Amazon DynamoDB vs PostgreSQL

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 nosql 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.
PostgreSQLFirst choices, of fourteen modelsApache CouchDB
Direct00
Paraphrase00
Comparative00
Budget-constrained21
Scale-constrained00
Negative005 against Apache CouchDB
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.

Every model, every framing

The eighty-four answers behind the chart above, one cell each: where PostgreSQL and Apache CouchDB 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
PostgreSQL Apache CouchDB first choice named as an alternative argued againstblank: not namedEach cell is one answer, PostgreSQL on the left and Apache CouchDB on the right.

The direct prompt

The plain question, one answer per model, grouped by where PostgreSQL and Apache CouchDB stood in it.

Neither was the first choice, one was named

1 of 14 modelsThe answer put something else first and named one of the two as an alternative.
Qwen 3.7 FlashMongoDB alternatives: Amazon DynamoDB, PostgreSQL

Neither was named

13 of 14 modelsThe answer made no first choice from these two in this category.
Claude Haiku 4.5Amazon DynamoDB, MongoDB alternatives: Firebase, Redis
GPT-5.4 miniAmazon DynamoDB, MongoDB alternatives: Azure Cosmos DB
Gemini 3.5 FlashMongoDB alternatives: Amazon DynamoDB, Azure Cosmos DB, Couchbase, Neo4j, Redis
Perplexity SonarMongoDB alternatives: Amazon DynamoDB, Apache Cassandra, Azure Cosmos DB, Couchbase, Neo4j, ScyllaDB
Grok 4.20MongoDB alternatives: Amazon DynamoDB, Apache Cassandra, Couchbase, Elasticsearch
Mistral SmallMongoDB alternatives: Amazon DynamoDB
DeepSeek V4 FlashMongoDB alternatives: Amazon DynamoDB, Apache Cassandra, Couchbase, DataStax
Llama 4 MaverickMongoDB
Kimi K2MongoDB alternatives: Amazon DynamoDB, Apache Cassandra, Couchbase, Redis
GLM 4.7 FlashXMongoDB alternatives: Amazon DynamoDB, Apache Cassandra, Azure Cosmos DB, Couchbase
MiniMax M2.5MongoDB alternatives: Apache Cassandra, Elasticsearch, Neo4j
GPT-6 LunaMongoDB alternatives: Amazon DynamoDB, Couchbase
Muse Glimmer 30BMongoDB alternatives: Amazon DynamoDB

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
PostgreSQL leads by two points.
PostgreSQL2%#4 of 12
Apache CouchDB0%#7 of 12
The full small business standing →
Mid-marketThe figures above
PostgreSQL leads by two points.
PostgreSQL4%#3 of 14
Apache CouchDB2%#7 of 14
The full mid-market standing →
Enterprise
PostgreSQL leads by two points.
PostgreSQL2%#– of 14
Apache CouchDB0%#– of 14
The full enterprise standing →

What the models said about PostgreSQL

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

“Use PostgreSQL + JSONB if you're early-stage and want maximum flexibility with minimal cost.” GPT-5.4 mini · budget prompt · first choice
“The Best "All-in-One" Alternative: PostgreSQL (with JSONB)” Gemini 3.5 Flash · budget prompt · first choice
“TL;DR: Default to Postgres. Reach for NoSQL only when you can state the specific access pattern or scale wall that forces it.” Muse Glimmer 30B · negative prompt · alternative

What the models said about Apache CouchDB

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

“Status: Declining — PayloadDB and CouchDB have seen declining contributor counts and activity.” DeepSeek V4 Flash · negative prompt · hard negative
“showing essentially zero current recommendation momentum in recent AI-model surveys” Muse Glimmer 30B · negative prompt · hard negative
“CouchDB's replication is powerful for offline-first but has a smaller ecosystem.” Grok 4.20 · negative prompt · soft negative
“Apache Cassandra or CouchDB for wide-column / key-value” Muse Glimmer 30B · budget prompt · first choice
“Open-source and completely free” Claude Haiku 4.5 · budget prompt · alternative
“Best for Offline-First & Sync” DeepSeek V4 Flash · budget prompt · alternative
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.