Index › Data platform › September 2026 Edition

NoSQL databases

Asked as “NoSQL database”, and as “document or key-value database service”, on behalf of a mid-market B2B company. 54 first choices recorded across the direct, paraphrase, budget and scale prompts, twelve models each.

Standing

MongoDB

Clear leader

59% of first choices, clear leader.

ShowinginAll 67 categories →

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

MongoDB77%Clear leaderStanding →Mid-market

MongoDB59%Clear leaderThis page Enterprise

MongoDB53%Clear leaderStanding →

01The standing

Share is the count of first choices across the direct, paraphrase, budget and scale prompts, over all twelve models, for a mid-market B2B company. Ordered by share.

ProductFirst-choice shareNegative rateLabelsQuadrant

MongoDB13%69endorsed leader

Amazon DynamoDB14%51accepted challenger

Couchbase8%38accepted challenger

Redis22%54accepted challenger

Azure Cosmos DB4%23accepted challenger

PostgreSQL0%11accepted challenger

Neo4j8%25accepted challenger

Show the five products at 0%, ordered by negative rate

Apache HBase31%13criticized challenger

Apache Cassandra30%43criticized challenger

ScyllaDB27%22criticized challenger

Apache CouchDB25%12criticized challenger

Google Cloud Firestore0%10accepted challenger

Bars are the share of first choices, 0 to 100Every product with at least 10 labels here. Every product name links to its vendor page.

Head to head

MongoDB vs Amazon DynamoDB MongoDB vs Couchbase Amazon DynamoDB vs Couchbase

One product takes 59% of first choices here, so the chart would put eleven markers in one corner and one at the far edge. The two measurements it plots are columns in the standing above: share, and the negative label rate. Four products carry a negative rate above 25% in this category. Jump to the standing

02What they warned about

Five of twelve models held their first choice under the paraphrase. Claude Haiku 4.5, Gemini 3.5 Flash, 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.

MongoDB

13%

9 of 69 labels negative · 8 of 12 models · 1 hard negative

“Risk Level: High MongoDB has been the target of massive automated botnets in the past due to its default insecure settings.” GLM 4.7 FlashX, negative prompt

Redis

22%

12 of 54 labels negative · 8 of 12 models · 1 hard negative

“Risk Level: Critical ... **Do not expose Redis to the internet.**” GLM 4.7 FlashX, negative prompt

Apache Cassandra

30%

13 of 43 labels negative · 8 of 12 models

“it's *extremely* easy to misuse... If you don't have a genuine write-heavy, massive-scale, multi-datacenter requirement, Cassandra is overkill.” DeepSeek V4 Flash, negative prompt

Amazon DynamoDB

14%

7 of 51 labels negative · 7 of 12 models · 2 hard negative

“**Avoid:** Proprietary/managed-only like DynamoDB (pay-per-use adds up) unless on free tier.” Grok 4.1 Fast, budget prompt

03What they cite

Citations exist only for the models that return a source list: twelve of the twelve in this edition, and all six flagship models on the expanded tier.

Sites the answers cite

53 of 72 answers in this category came back with a source list, from 12 of 12 models: citations where the model returns them, or the search results it consulted. 5 of those lists are Google grounding redirects that name no site and are left out of the counts. 700 links across 220 sites, every framing counted. Ranked by the number of answers carrying the site or page.

aerospike.com

vendor site · Aerospike26 answers · 33 citations · 9 models

mongodb.com

vendor site · MongoDB19 answers · 29 citations · 8 models

sourceforge.net

vendor site · SourceForge17 answers · 20 citations · 8 models

db-engines.com

12 answers · 22 citations · 8 models

g2.com

12 answers · 17 citations · 9 models

infoworld.com

12 answers · 15 citations · 5 models

getapp.com

12 answers · 12 citations · 8 models

guideflow.com

vendor site · Guideflow12 answers · 12 citations · 7 models

couchbase.com

vendor site · Couchbase11 answers · 20 citations · 7 models

scylladb.com

vendor site · ScyllaDB11 answers · 12 citations · 7 models

medium.com

10 answers · 19 citations · 6 models

peerspot.com

10 answers · 10 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.

aerospike.com/blog/ten-best-nosql-cloud-databases

24 answers · 9 models

guideflow.com/blog/nosql-database-software

12 answers · 7 models

thectoclub.com/tools/best-nosql-database

10 answers · 7 models

6sense.com/tech/nosql-databases

9 answers · 8 models

getapp.com/it-management-software/nosql-databases/org/mid-size-business

9 answers · 8 models

peerspot.com/categories/nosql-databases

9 answers · 6 models

pingcap.com/article/top-10-nosql-databases-to-consider-2024

9 answers · 6 models

alex-klaus.com/reasons-not-to-nosql

8 answers · 8 models

sourceforge.net/software/nosql-database/for-mid-size-business

8 answers · 8 models

couchbase.com/blog/nosql-database-evaluation-guide

7 answers · 6 models

04How 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.

ModelDirect“What is the best NoSQL database for a mid-market B2B company?”Paraphrase“Which document or key-value database service would you recommend to a mid-sized B2B company?”Comparative“What are the top NoSQL databases and how do they differ?”Budget-constrained“What is the best NoSQL database for a company with a limited budget?”Scale-constrained“We are a 500 person company evaluating a NoSQL database. What should we look at?”Negative“Which NoSQL databases should I avoid or be cautious about?”

Claude Haiku 4.5MongoDB, PostgreSQL

Two alternatives

Amazon DynamoDB, Redis

MongoDBChanged

Three alternatives

Amazon DynamoDB, Firebase, Redis

MongoDB

One alternative

Amazon DynamoDB

no first choiceno first choiceagainst: Apache Cassandra, Apache HBase, MongoDB, OrientDB

GPT-5.4 miniMongoDB

Three alternatives

Amazon DynamoDB, Apache Cassandra, Redis

MongoDBHeld

Two alternatives

Amazon DynamoDB, Redis

MongoDB

Five alternatives

Amazon DynamoDB, Apache Cassandra, Elasticsearch / OpenSearch, Neo4j, Redis

MongoDB, PostgreSQL

One alternative

Redis

against: Apache Cassandrano first choiceagainst: Amazon DynamoDB, Redis

Gemini 3.5 FlashMongoDB

Three alternatives

Amazon DynamoDB, Azure Cosmos DB, Neo4j

against: Apache Cassandra, Redis, ScyllaDB, ValkeyMongoDB, ValkeyChanged

Two alternatives

Amazon DynamoDB, Amazon ElastiCache

against: RedisMongoDB

Four alternatives

Amazon DynamoDB, Apache Cassandra, Neo4j, Redis

Amazon DynamoDB

Five alternatives

Google Cloud Firestore, MongoDB, PostgreSQL, ScyllaDB, Valkey

against: Apache Cassandra, MongoDB, Redisno first choiceagainst: Amazon DynamoDB, Apache Cassandra, Apache HBase, Firebase Firestore / Google Cloud Datastore, MongoDB, Neo4j, Redis, RethinkDB, Riak KV, ScyllaDB

Perplexity SonarMongoDB

Four alternatives

Amazon DynamoDB, Azure Cosmos DB, Couchbase, Redis

MongoDBHeld

Three alternatives

Amazon DocumentDB, Amazon DynamoDB, Azure Cosmos DB

MongoDB

Four alternatives

Amazon DynamoDB, Apache Cassandra, Neo4j, Redis

Couchbase

Three alternatives

Google Cloud Firestore, IBM Cloudant, Redis

against: Amazon DynamoDBno first choicenothing named

Grok 4.1 FastMongoDB

Two alternatives

Amazon DynamoDB, Apache Cassandra

Amazon DynamoDB, MongoDBChanged

Three alternatives

Azure Cosmos DB, Couchbase, Redis

no first choiceMongoDB

Three alternatives

Apache Cassandra, Apache CouchDB, Redis

against: Amazon DynamoDBAmazon DynamoDB, MongoDB

Three alternatives

Apache Cassandra, Couchbase, Redis

against: Actian NoSQL, Apache Cassandra, Apache CouchDB, BigchainDB, BoltDB, Hypertable, MemcacheDB, MongoDB, Oracle NoSQL Database, Redis, Riak, Voldemort

Mistral SmallAzure Cosmos DB, MongoDB

One alternative

Redis

Amazon DynamoDB, MongoDBChanged

Two alternatives

Azure Cosmos DB, Redis

no first choiceMongoDB, Redis

Three alternatives

Apache Cassandra, ArangoDB, ScyllaDB

no first choice

Eight alternatives

Amazon DynamoDB, Apache Cassandra, Couchbase, InfluxDB, MongoDB, Neo4j, Redis, ScyllaDB

against: Amazon DynamoDB, Apache Cassandra, Apache HBase, Couchbase, MongoDB, Redis, Riak

DeepSeek V4 FlashMongoDB

Two alternatives

Amazon DynamoDB, PostgreSQL

against: Apache Cassandra, Redis, ScyllaDBMongoDBHeld

Three alternatives

Amazon DocumentDB, Amazon DynamoDB, Redis

against: Aerospike, CouchbaseApache Cassandra, MongoDB, Neo4j, Redis

Three alternatives

Amazon DynamoDB, Azure Cosmos DB, Elasticsearch

MongoDB

Nine alternatives

Apache Cassandra, Azure Cosmos DB, Couchbase, FerretDB, Google Cloud Firestore, KeyDB, Percona Server for MongoDB, ScyllaDB, Valkey

against: Amazon DynamoDB, Redisno first choiceagainst: Apache Cassandra, Apache CouchDB, Apache HBase, Memcached, MongoDB, Riak, ScyllaDB

Llama 4 Maverickno first choiceAmazon DynamoDBChanged

Three alternatives

Aerospike, Couchbase, MongoDB

no first choiceInfluxDB, Neo4j, RavenDBno first choicenothing named

Qwen 3.7 FlashMongoDB

Three alternatives

Amazon DynamoDB, InfluxDB, Neo4j

against: Apache Cassandra, ScyllaDBMongoDBHeld

Two alternatives

Amazon DynamoDB, Azure Cosmos DB

MongoDB

Three alternatives

Apache Cassandra, Neo4j, Redis

MongoDB

Three alternatives

Amazon DynamoDB, Google Firebase / Firestore, Supabase

no first choice

Three alternatives

MongoDB, Neo4j, Redis

against: Amazon DynamoDB, Apache Cassandra, Elasticsearchagainst: Apache Cassandra, Milvus, MongoDB, Neo4j, Pinecone, Redis, RethinkDB, ScyllaDB

Kimi K2MongoDB

Two alternatives

Amazon DynamoDB, Couchbase

MongoDBHeld

Four alternatives

Amazon DynamoDB, Azure Cosmos DB, Couchbase, Redis

MongoDB

Four alternatives

Amazon DynamoDB, Apache Cassandra, Couchbase, Redis

Firebase

Three alternatives

Apache Cassandra, Apache CouchDB, MongoDB

MongoDB

Four alternatives

Amazon DynamoDB, Apache Cassandra, Couchbase, Redis

against: Azure Cosmos DBagainst: Amazon QLDB, AngstromDB, AnnaDB, Apache Cassandra, Fauna, MongoDB, Redis, RedisGraph

GLM 4.7 FlashXCouchbase

Two alternatives

Amazon DynamoDB, MongoDB

MongoDB, RedisChanged

Four alternatives

Aerospike, Azure Cosmos DB, Couchbase, Google Cloud Memorystore for Redis

no first choiceCouchbase, MongoDB

Five alternatives

Amazon DynamoDB, Apache Cassandra, Azure Cosmos DB, Google Cloud Firestore, Redis

no first choiceagainst: Apache CouchDB, Couchbase, Elasticsearch, MongoDB, Redis

MiniMax M2.5Couchbase, MongoDB

One alternative

Amazon DynamoDB

MongoDBChanged

Four alternatives

Amazon DynamoDB, Azure Cosmos DB, Google Cloud Firestore, Redis

no first choiceMongoDB, Redis

Two alternatives

Apache Cassandra, Apache CouchDB

Amazon DynamoDB, Azure Cosmos DB, MongoDBagainst: AngstromDB, AnnDB, AnnaDB, AnuDB Serverless, Arakoon, BigchainDB, Blueflood, BoltDB, CurioDB

Bold is the first choiceAlternatives are counted; the count opens them.What the answer argued against

05The 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

Seventy-two 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`	2026-09-17 13:04	no	0	5 s
Direct recommendation	GPT-5.4 mini	`gpt-5.4-mini-2026-03-17`	2026-09-17 11:53	yes	4	5 s
Direct recommendation	Gemini 3.5 Flash	`gemini-3.5-flash`	2026-09-17 10:35	yes	11	21 s
Direct recommendation	Perplexity Sonar	`sonar`	2026-09-17 09:36	yes	17	8 s
Direct recommendation	Grok 4.1 Fast	`spacexai/grok-4.1-fast-non-reasoning via vertex`	2026-09-17 12:58	yes	19	11 s
Direct recommendation	Mistral Small	`mistral/mistral-small via mistral`	2026-09-17 10:00	yes	9	8 s
Direct recommendation	DeepSeek V4 Flash	`deepseek/deepseek-v4-flash via fireworks`	2026-09-17 14:09	yes	10	15 s
Direct recommendation	Llama 4 Maverick	`meta/llama-4-maverick via bedrock`	2026-09-17 11:32	yes	5	3 s
Direct recommendation	Qwen 3.7 Flash	`alibaba/qwen3.7-flash via alibaba`	2026-09-17 10:38	no	0	34 s
Direct recommendation	Kimi K2	`moonshotai/kimi-k2 via novita`	2026-09-17 09:24	yes	10	22 s
Direct recommendation	GLM 4.7 FlashX	`zai/glm-4.7-flashx via zai`	2026-09-17 12:38	yes	34	81 s
Direct recommendation	MiniMax M2.5	`minimax/minimax-m2.5 via minimax`	2026-09-17 11:18	yes	5	28 s
Paraphrase	Claude Haiku 4.5	`claude-haiku-4-5-20251001`	2026-09-17 09:49	no	0	4 s
Paraphrase	GPT-5.4 mini	`gpt-5.4-mini-2026-03-17`	2026-09-17 12:08	no	0	4 s
Paraphrase	Gemini 3.5 Flash	`gemini-3.5-flash`	2026-09-17 12:47	yes	6	23 s
Paraphrase	Perplexity Sonar	`sonar`	2026-09-17 12:04	yes	20	5 s
Paraphrase	Grok 4.1 Fast	`spacexai/grok-4.1-fast-non-reasoning via vertex`	2026-09-17 10:22	yes	19	9 s
Paraphrase	Mistral Small	`mistral/mistral-small via mistral`	2026-09-17 13:41	yes	5	6 s
Paraphrase	DeepSeek V4 Flash	`deepseek/deepseek-v4-flash via fireworks`	2026-09-17 11:58	yes	20	20 s
Paraphrase	Llama 4 Maverick	`meta/llama-4-maverick via bedrock`	2026-09-17 10:13	yes	5	3 s
Paraphrase	Qwen 3.7 Flash	`alibaba/qwen3.7-flash via alibaba`	2026-09-17 09:38	yes	10	61 s
Paraphrase	Kimi K2	`moonshotai/kimi-k2 via novita`	2026-09-17 12:08	yes	20	36 s
Paraphrase	GLM 4.7 FlashX	`zai/glm-4.7-flashx via zai`	2026-09-17 11:09	yes	25	59 s
Paraphrase	MiniMax M2.5	`minimax/minimax-m2.5 via minimax`	2026-09-17 12:49	no	0	57 s
Comparative	Claude Haiku 4.5	`claude-haiku-4-5-20251001`	2026-09-17 13:04	yes	9	9 s
Comparative	GPT-5.4 mini	`gpt-5.4-mini-2026-03-17`	2026-09-17 13:24	no	0	5 s
Comparative	Gemini 3.5 Flash	`gemini-3.5-flash`	2026-09-17 12:08	yes	13	27 s
Comparative	Perplexity Sonar	`sonar`	2026-09-17 13:26	yes	20	10 s
Comparative	Grok 4.1 Fast	`spacexai/grok-4.1-fast-non-reasoning via vertex`	2026-09-17 12:05	no	0	6 s
Comparative	Mistral Small	`mistral/mistral-small via mistral`	2026-09-17 12:33	yes	5	7 s
Comparative	DeepSeek V4 Flash	`deepseek/deepseek-v4-flash via fireworks`	2026-09-17 12:12	yes	20	20 s
Comparative	Llama 4 Maverick	`meta/llama-4-maverick via bedrock`	2026-09-17 11:08	yes	5	2 s
Comparative	Qwen 3.7 Flash	`alibaba/qwen3.7-flash via alibaba`	2026-09-17 10:10	no	0	41 s
Comparative	Kimi K2	`moonshotai/kimi-k2 via novita`	2026-09-17 11:19	yes	10	27 s
Comparative	GLM 4.7 FlashX	`zai/glm-4.7-flashx via zai`	2026-09-17 11:27	no	0	42 s
Comparative	MiniMax M2.5	`minimax/minimax-m2.5 via minimax`	2026-09-17 14:02	yes	8	28 s
Budget constrained	Claude Haiku 4.5	`claude-haiku-4-5-20251001`	2026-09-17 13:14	no	0	5 s
Budget constrained	GPT-5.4 mini	`gpt-5.4-mini-2026-03-17`	2026-09-17 13:34	no	0	3 s
Budget constrained	Gemini 3.5 Flash	`gemini-3.5-flash`	2026-09-17 13:53	yes	11	27 s
Budget constrained	Perplexity Sonar	`sonar`	2026-09-17 09:26	yes	20	6 s
Budget constrained	Grok 4.1 Fast	`spacexai/grok-4.1-fast-non-reasoning via vertex`	2026-09-17 13:49	yes	14	9 s
Budget constrained	Mistral Small	`mistral/mistral-small via mistral`	2026-09-17 10:31	yes	8	6 s
Budget constrained	DeepSeek V4 Flash	`deepseek/deepseek-v4-flash via fireworks`	2026-09-17 12:18	yes	20	15 s
Budget constrained	Llama 4 Maverick	`meta/llama-4-maverick via bedrock`	2026-09-17 12:45	yes	5	3 s
Budget constrained	Qwen 3.7 Flash	`alibaba/qwen3.7-flash via alibaba`	2026-09-17 10:27	no	0	46 s
Budget constrained	Kimi K2	`moonshotai/kimi-k2 via novita`	2026-09-17 13:28	yes	10	64 s
Budget constrained	GLM 4.7 FlashX	`zai/glm-4.7-flashx via zai`	2026-09-17 11:46	yes	10	79 s
Budget constrained	MiniMax M2.5	`minimax/minimax-m2.5 via minimax`	2026-09-17 11:30	no	0	10 s
Scale constrained	Claude Haiku 4.5	`claude-haiku-4-5-20251001`	2026-09-17 10:07	no	0	5 s
Scale constrained	GPT-5.4 mini	`gpt-5.4-mini-2026-03-17`	2026-09-17 12:07	no	0	8 s
Scale constrained	Gemini 3.5 Flash	`gemini-3.5-flash`	2026-09-17 09:42	no	0	16 s
Scale constrained	Perplexity Sonar	`sonar`	2026-09-17 09:29	yes	19	17 s
Scale constrained	Grok 4.1 Fast	`spacexai/grok-4.1-fast-non-reasoning via vertex`	2026-09-17 13:24	yes	18	12 s
Scale constrained	Mistral Small	`mistral/mistral-small via mistral`	2026-09-17 10:08	no	0	11 s
Scale constrained	DeepSeek V4 Flash	`deepseek/deepseek-v4-flash via fireworks`	2026-09-17 10:34	yes	26	29 s
Scale constrained	Llama 4 Maverick	`meta/llama-4-maverick via bedrock`	2026-09-17 13:23	yes	5	3 s
Scale constrained	Qwen 3.7 Flash	`alibaba/qwen3.7-flash via alibaba`	2026-09-17 09:46	no	0	40 s
Scale constrained	Kimi K2	`moonshotai/kimi-k2 via novita`	2026-09-17 11:55	yes	14	51 s
Scale constrained	GLM 4.7 FlashX	`zai/glm-4.7-flashx via zai`	2026-09-17 11:36	yes	14	78 s
Scale constrained	MiniMax M2.5	`minimax/minimax-m2.5 via minimax`	2026-09-17 10:26	no	0	22 s
Negative framing	Claude Haiku 4.5	`claude-haiku-4-5-20251001`	2026-09-17 14:15	yes	17	7 s
Negative framing	GPT-5.4 mini	`gpt-5.4-mini-2026-03-17`	2026-09-17 12:54	yes	3	5 s
Negative framing	Gemini 3.5 Flash	`gemini-3.5-flash`	2026-09-17 13:18	yes	19	29 s
Negative framing	Perplexity Sonar	`sonar`	2026-09-17 13:54	yes	20	7 s
Negative framing	Grok 4.1 Fast	`spacexai/grok-4.1-fast-non-reasoning via vertex`	2026-09-17 09:38	yes	38	17 s
Negative framing	Mistral Small	`mistral/mistral-small via mistral`	2026-09-17 11:54	yes	5	9 s
Negative framing	DeepSeek V4 Flash	`deepseek/deepseek-v4-flash via fireworks`	2026-09-17 10:03	yes	27	27 s
Negative framing	Llama 4 Maverick	`meta/llama-4-maverick via bedrock`	2026-09-17 12:04	yes	5	2 s
Negative framing	Qwen 3.7 Flash	`alibaba/qwen3.7-flash via alibaba`	2026-09-17 12:01	yes	20	64 s
Negative framing	Kimi K2	`moonshotai/kimi-k2 via novita`	2026-09-17 13:13	yes	30	63 s
Negative framing	GLM 4.7 FlashX	`zai/glm-4.7-flashx via zai`	2026-09-17 13:00	yes	23	59 s
Negative framing	MiniMax M2.5	`minimax/minimax-m2.5 via minimax`	2026-09-17 13:45	yes	15	42 s

Normalization in this category

Every judgment call made between the raw labels and the numbers above, listed so it is visible and reversible.

Category-scoped readings

MongoDB Atlas read as MongoDB

Oracle NoSQL read as Oracle NoSQL Database

Unresolved, counted raw

Actian NoSQL

Amazon QLDB

AnnDB

AnuDB Serverless

Arakoon

Blueflood

CurioDB

FerretDB

Firebase Firestore / Google Cloud Datastore

Google Cloud Memorystore

Google Cloud Memorystore for Redis

Hypertable

JanusGraph

LevelDB

MemcacheDB

Oracle Coherence

PouchDB

RedisGraph

RocksDB

etcd

Discontinued, still offered

No shut-down product was recommended here.

NoSQL databases

By buyer segment

01The standing

02What they warned about

03What they cite

Sites the answers cite

Pages the answers cite

04How they answered

05The record

Normalization in this category

Also in data platform