NoSQL databases for small business buyers
Asked as “NoSQL database”, and as “document or key-value database service”, on behalf of a small B2B company. 62 first choices recorded across the direct, paraphrase, budget and scale prompts, fourteen models each.
Standing · first-choice share
69%
Clear leader
69MongoDB11Amazon DynamoDB06Google Cloud Firestore13others
69% of first choices, clear leader.
Since September 2026▼−11Since September 2026: 77% → 66%, −11 points. Past the 11-point floor: movement. Read over the models both editions asked.MongoDB held the lead, −11 points on 77%, past the 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
MongoDB69%Clear leader▼−11Since September 2026: 77% → 66%, −11 points. Past the 11-point floor: movement. Read over the models both editions asked.This pageMid-marketMongoDB74%Clear leader▲+14Since September 2026: 59% → 73%, +14 points. Past the 11-point floor: movement. Read over the models both editions asked.Standing →EnterpriseMongoDB56%Clear leader=heldSince September 2026: 53% → 53%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.Standing →
MongoDB69%Clear leader▼−11Since September 2026: 77% → 66%, −11 points. Past the 11-point floor: movement. Read over the models both editions asked.This pageMid-marketMongoDB74%Clear leader▲+14Since September 2026: 59% → 73%, +14 points. Past the 11-point floor: movement. Read over the models both editions asked.Standing →EnterpriseMongoDB56%Clear leader=heldSince September 2026: 53% → 53%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.Standing →The standing
Share is the count of first choices across the direct, paraphrase, budget and scale prompts, over all fourteen models, for a small B2B company. Ordered by share.
ProductFirst-choice shareNegative rateLabelsQuadrantSince September 2026
01MongoDB69%12%82endorsed leader▼−11Since September 2026: 77% → 66%, −11 points. Past the 11-point floor: movement. Read over the models both editions asked.77% → 66%
MongoDB69%12%82endorsed leader▼−11Since September 2026: 77% → 66%, −11 points. Past the 11-point floor: movement. Read over the models both editions asked.77% → 66%02
Amazon DynamoDB11%18%60accepted challenger▲+11Since September 2026: 2% → 12%, +11 points. Inside the 11-point floor: within noise. Read over the models both editions asked.2% → 12%
Amazon DynamoDB11%18%60accepted challenger▲+11Since September 2026: 2% → 12%, +11 points. Inside the 11-point floor: within noise. Read over the models both editions asked.2% → 12%03
Google Cloud Firestore Lab in the set6%3%32accepted challenger▼−1Since September 2026: 8% → 7%, −1 point. Inside the 11-point floor: within noise. Read over the models both editions asked.8% → 7%
Google Cloud Firestore Lab in the set6%3%32accepted challenger▼−1Since September 2026: 8% → 7%, −1 point. Inside the 11-point floor: within noise. Read over the models both editions asked.8% → 7%04
PostgreSQL5%0%25accepted challenger▲+2Since September 2026: 4% → 5%, +2 points. Inside the 11-point floor: within noise. Read over the models both editions asked.4% → 5%
PostgreSQL5%0%25accepted challenger▲+2Since September 2026: 4% → 5%, +2 points. Inside the 11-point floor: within noise. Read over the models both editions asked.4% → 5%05
Couchbase2%23%31accepted challenger=heldSince September 2026: 2% → 2%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.2% → 2%
Couchbase2%23%31accepted challenger=heldSince September 2026: 2% → 2%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.2% → 2%06
Apache CouchDB2%15%13accepted challenger▲+2Since September 2026: 0% → 2%, +2 points. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 2%
Apache CouchDB2%15%13accepted challenger▲+2Since September 2026: 0% → 2%, +2 points. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 2%07
Azure Cosmos DB2%27%11criticized challenger▲+2Since September 2026: 0% → 2%, +2 points. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 2%
Azure Cosmos DB2%27%11criticized challenger▲+2Since September 2026: 0% → 2%, +2 points. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 2%Show the three products at 0%, ordered by negative rate
10
Apache Cassandra0%58%36criticized challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 0%
Apache Cassandra0%58%36criticized challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 0%09
Neo4j0%33%15criticized challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 0%
Neo4j0%33%15criticized challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 0%08
Redis0%16%51accepted challenger▼−2Since September 2026: 2% → 0%, −2 points. Inside the 11-point floor: within noise. Read over the models both editions asked.2% → 0%
Redis0%16%51accepted 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 Firestore 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 four 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
MongoDB vs ScyllaDBMongoDB vs PostgreSQLMongoDB vs Google Cloud FirestoreAmazon DynamoDB vs CouchbaseAmazon DynamoDB vs RedisAmazon DynamoDB vs ScyllaDBAmazon DynamoDB vs PostgreSQLAmazon DynamoDB vs Google Cloud FirestoreCouchbase vs RedisCouchbase vs ScyllaDBCouchbase vs PostgreSQLCouchbase vs Google Cloud FirestoreRedis vs ScyllaDBRedis vs PostgreSQLRedis vs Google Cloud FirestoreScyllaDB vs PostgreSQLScyllaDB vs Google Cloud FirestorePostgreSQL vs Google Cloud Firestore
One product takes 69% of first choices here, so the chart would put nine 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. Three products carry a negative rate above 25% in this category.
What they warned about
Ten of fourteen models held their first choice under the paraphrase. Gemini 3.5 Flash, Mistral Small, Llama 4 Maverick and Qwen 3.7 Flash 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.
Apache Cassandra58%
21 of 36 labels negative · 12 of 14 models · 6 hard negative
“Overly complex to cluster and maintain. Built for petabyte-scale data and massive throughput—useless if you have fewer than millions of records.” Qwen 3.7 Flash, negative prompt
MongoDB12%
10 of 82 labels negative · 9 of 14 models · 2 hard negative
“High Caution: MongoDB (Due to Licensing)... Vendor lock-in risk with escalating costs” Kimi K2, negative prompt
Amazon DynamoDB18%
11 of 60 labels negative · 9 of 14 models · 1 hard negative
“**Avoid paid enterprise platforms** like Cosmos DB, DynamoDB, or Enterprise editions” Perplexity Sonar, budget prompt
Apache HBase88%
7 of 8 labels negative · 6 of 14 models · 6 hard negative
“Requires a full Hadoop ecosystem (HDFS, Zookeeper, RegionServers), which is overkill for most small‑business workloads” MiniMax M2.5, 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
67 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. 834 links across 212 sites, every framing counted. Ranked by the number of answers carrying the site or page. 30 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 · Amazon27 answers · 35 citations · 12 models
26 answers · 27 citations · 10 models
vendor site · SourceForge25 answers · 31 citations · 9 models
vendor site · FitGap25 answers · 30 citations · 10 models
vendor site · Guideflow25 answers · 25 citations · 11 models
vendor site · MongoDB23 answers · 37 citations · 11 models
vendor site · G217 answers · 31 citations · 10 models
vendor site · Couchbase15 answers · 32 citations · 10 models
vendor site · Aerospike15 answers · 15 citations · 7 models
15 answers · 15 citations · 8 models
14 answers · 16 citations · 7 models
13 answers · 15 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 · 11 models
24 answers · 10 models
17 answers · 11 models
16 answers · 7 models
13 answers · 6 models
12 answers · 7 models
12 answers · 7 models
11 answers · 9 models
11 answers · 6 models
11 answers · 6 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 nosql database”, “nosql database”, “nosql databases”. 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 | Best Google position |
|---|---|---|---|---|---|---|---|---|
| MongoDB mongodb.com | 69% | 192 | 74,000 | 9,630 | 342,632 | $8,625 | 120 | 1 |
| Amazon aws.amazon.com Amazon DynamoDB 11% | 11% | 155 | 151,000,000 | 1,380,939 | 9,238,321 | $20,476 | 5 | |
| Google Cloud google.dev Google Cloud Firestore 6% | 6% | 38 | 165,000 | 7,596 | 881,346 | 109 | ||
| PostgreSQL postgresql.org | 5% | 82 | 74,000 | 14,143 | 465,585 | |||
| Couchbase couchbase.com | 2% | 94 | 4,400 | 221 | 62,564 | $34 | 26 | |
| Apache CouchDB apache.org | 2% | 28 | 1,600 | 4 | 1,144,025 | 0 | ||
| Azure parent site Azure Cosmos DB 2% | 2% | 66 | 301,000 | 47,904 | ||||
| Firebase firebase.google.com Firebase Realtime Database 2%, Firebase 0% | 2% | 6 | 90,500 | 3,437 | 91,247 company | |||
| PostgreSQL with JSONB columns | 2% | |||||||
| Redis redis.io | 0% | 137 | 27,100 | 6,980 | 112,424 | 18 |
Google's top ten for the three searches hold 14 sites; 3 of them are among the sites the models cited here (aerospike.com, aws.amazon.com, mongodb.com). The first result for “best nosql database” is reddit.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 NoSQL database for a small B2B company?”Paraphrase“Which document or key-value database service would you recommend to a small business that sells to other businesses?”Comparative“What are the top NoSQL databases for a small team and how do they differ?”Budget-constrained“What is the best NoSQL database for a small company with a tight budget?”Scale-constrained“We are a 30 person company evaluating a NoSQL database. What should we look at?”Negative“Which NoSQL databases should a small business avoid or be cautious about?”
Claude Haiku 4.5Google Cloud Firestore, MongoDBGoogle Cloud Firestore, MongoDBHeldMongoDBGoogle Cloud Firestore, MongoDBagainst: Apache Cassandrano first choicenothing named
Two alternatives
Amazon DynamoDB, PostgreSQLOne alternative
RedisThree alternatives
Amazon DynamoDB, Google Cloud Firestore, RedisTwo alternatives
Amazon DynamoDB, Apache CouchDBGPT-5.4 miniMongoDBMongoDBHeldMongoDBagainst: Apache CassandraApache CouchDB, MongoDBagainst: Redisno first choiceagainst: Apache Cassandra
Two alternatives
Amazon DynamoDB, Google Cloud FirestoreOne alternative
Amazon DynamoDBTwo alternatives
Amazon DynamoDB, RedisGemini 3.5 FlashAmazon DynamoDB, MongoDBMongoDBChangedagainst: RedisGoogle Cloud Firestore, MongoDBAmazon DynamoDB, MongoDBagainst: Apache CouchDB, MongoDBAmazon DynamoDB, MongoDBagainst: Apache Cassandra, ScyllaDBagainst: Amazon DynamoDB, Apache Cassandra, Apache HBase, MongoDB, Neo4j, Redis, ScyllaDB
Two alternatives
Google Firebase Firestore, PostgreSQLTwo alternatives
Amazon DynamoDB, Google Cloud FirestoreTwo alternatives
Amazon DynamoDB, RedisTwo alternatives
Google Cloud Firestore, PostgreSQL with `JSONB`Six alternatives
Firebase, Google Cloud Firestore, PostgreSQL, Redis, Supabase, UpstashPerplexity SonarMongoDBMongoDBHeldMongoDBagainst: CouchbaseMongoDBagainst: Amazon DynamoDB, Azure Cosmos DBno first choiceagainst: MongoDB
Three alternatives
Amazon DynamoDB, Azure Cosmos DB, RedisThree alternatives
Amazon DynamoDB, Google Cloud Firestore, RedisOne alternative
RavenDBGrok 4.1 FastMongoDBMongoDBHeldagainst: RedisMongoDBMongoDBagainst: Apache CassandraAmazon DynamoDB, MongoDBagainst: Apache Cassandra, Apache HBase, Couchbase, Redis clusters, Self-managed MongoDB
Three alternatives
Amazon DynamoDB, Apache Cassandra, RedisOne alternative
Amazon DynamoDBFour alternatives
Amazon DynamoDB, Apache Cassandra, Couchbase, RedisTwo alternatives
Apache CouchDB, RedisFive alternatives
Couchbase, DataStax Astra DB, Google Cloud Firestore, Redis, ScyllaDBMistral SmallAmazon DynamoDBAmazon DynamoDB, MongoDBChangedCouchbase, MongoDBagainst: Apache CassandraFirebase Realtime Database, MongoDBMongoDB, PostgreSQLagainst: Apache Cassandra, Apache CouchDB, Neo4j, Riak KV
Three alternatives
Apache Cassandra, MongoDB, RedisFour alternatives
Azure Cosmos DB, Couchbase, Google Cloud Firestore, RedisTwo alternatives
Amazon DynamoDB, RedisTwo alternatives
Apache Cassandra, RedisFour alternatives
Amazon DynamoDB, Apache Cassandra, Firebase, RedisDeepSeek V4 FlashMongoDBagainst: CouchbaseMongoDBHeldMongoDBagainst: Apache CassandraMongoDBAmazon DynamoDB, Google Cloud Firestore, MongoDBagainst: Apache Cassandra, ScyllaDBagainst: Amazon DynamoDB, Apache Cassandra, Apache HBase, Couchbase, Google Cloud Bigtable, MongoDB
Two alternatives
Amazon DynamoDB, RavenDBTwo alternatives
Amazon DynamoDB, Google Cloud FirestoreThree alternatives
Amazon DynamoDB, Google Cloud Firestore, RedisThree alternatives
Apache Cassandra, Google Cloud Firestore, ScyllaDBLlama 4 Maverickno first choiceCouchbaseChangedno first choiceAzure Cosmos DBno first choiceagainst: MongoDB, Oracle Database, Redis, ScyllaDB, Velneo
Two alternatives
Amazon DocumentDB, MongoDBTwo alternatives
Apache Cassandra, RavenDBQwen 3.7 FlashPostgreSQLMongoDBChangedMongoDBMongoDBPostgreSQL with JSONB columnsagainst: Apache Cassandra, Apache HBaseagainst: Amazon DynamoDB, Apache Cassandra, Apache HBase, Apache Spark Streaming DBs, Elasticsearch, IBM InfoSphere, MongoDB, Neo4j, Oracle Coherence, Oracle Spatial, Redis Cluster, ScyllaDB
Two alternatives
Amazon DynamoDB, MongoDBOne alternative
Amazon DynamoDBFive alternatives
Amazon DynamoDB, Couchbase, Google Cloud Firestore, PostgreSQL, RedisThree alternatives
Firebase, PostgreSQL, RedisTwo alternatives
Amazon DynamoDB, MongoDBKimi K2MongoDBMongoDBHeldagainst: Amazon DynamoDBMongoDBMongoDBMongoDB, PostgreSQLagainst: Amazon DynamoDB, Apache Cassandraagainst: Apache Cassandra, Couchbase, EnterpriseDB, MongoDB
Two alternatives
Amazon DynamoDB, PostgreSQLOne alternative
Google Cloud FirestoreFour alternatives
Amazon DynamoDB, Couchbase, Firebase, RedisTwo alternatives
Google Cloud Firestore, PostgreSQLGLM 4.7 FlashXMongoDBMongoDBHeldMongoDBagainst: Amazon DynamoDBMongoDBno first choiceagainst: Amazon DynamoDB, Apache Cassandra, Apache HBase, Neo4j, ScyllaDB
Two alternatives
Amazon DynamoDB, CouchbaseThree alternatives
Couchbase, Google Cloud Firestore, RedisTwo alternatives
Couchbase, RedisThree alternatives
Apache Cassandra, Redis, SQLiteMiniMax M2.5MongoDBagainst: RedisMongoDBHeldMongoDBMongoDBMongoDBagainst: Amazon DynamoDB, Apache Cassandra, Apache HBase, Azure Cosmos DB, Couchbase, Elasticsearch, Google Cloud Datastore, IBM Cloudant, MongoDB, Neo4j, Oracle NoSQL Database, Redis
Two alternatives
Amazon DynamoDB, CouchbaseSix alternatives
Amazon DynamoDB, Apache CouchDB, Couchbase, Firebase, Google Cloud Firestore, RedisTwo alternatives
ArangoDB, restdb.ioTwo alternatives
Amazon DynamoDB, CouchbaseGPT-6 LunaMongoDBMongoDBHeldMongoDBMongoDBno first choiceagainst: Amazon DynamoDB, Apache Cassandra, Google Cloud Firestore, MongoDB, Redis
Two alternatives
Amazon DynamoDB, PostgreSQLTwo alternatives
Amazon DynamoDB, PostgreSQLThree alternatives
Amazon DynamoDB, Google Cloud Firestore, RedisTwo alternatives
Amazon DynamoDB, Google Cloud FirestoreMuse Glimmer 30BMongoDBMongoDBHeldMongoDBagainst: Apache CassandraMongoDBagainst: Amazon DynamoDBno first choiceagainst: Apache Cassandra, Azure Cosmos DB, Couchbase, MongoDB
One alternative
Google Cloud FirestoreOne alternative
Amazon DynamoDBFive alternatives
Amazon DynamoDB, Couchbase, Firebase, Google Cloud Firestore, RedisTwo alternatives
Apache Cassandra, Apache CouchDBBold 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 11:22 | no | 0 | 5 s |
| Direct recommendation | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 11:48 | yes | 3 | 4 s |
| Direct recommendation | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 08:56 | yes | 7 | 26 s |
| Direct recommendation | Perplexity Sonar | sonar | 2026-10-01 10:31 | yes | 20 | 3 s |
| Direct recommendation | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 12:00 | yes | 15 | 7 s |
| Direct recommendation | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 10:47 | yes | 5 | 3 s |
| Direct recommendation | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 13:51 | yes | 22 | 33 s |
| Direct recommendation | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 10:57 | yes | 5 | 2 s |
| Direct recommendation | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 09:24 | no | 0 | 29 s |
| Direct recommendation | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 08:32 | yes | 14 | 17 s |
| Direct recommendation | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 11:05 | yes | 25 | 29 s |
| Direct recommendation | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 11:47 | no | 0 | 48 s |
| Direct recommendation | GPT-6 Luna | gpt-6-luna | 2026-10-01 08:25 | yes | 2 | 11 s |
| Direct recommendation | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 09:05 | yes | 23 | 33 s |
| Paraphrase | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 10:24 | no | 0 | 4 s |
| Paraphrase | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 10:38 | yes | 3 | 4 s |
| Paraphrase | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 11:01 | yes | 7 | 23 s |
| Paraphrase | Perplexity Sonar | sonar | 2026-10-01 08:57 | yes | 23 | 5 s |
| Paraphrase | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 10:25 | yes | 20 | 6 s |
| Paraphrase | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 10:25 | no | 0 | 5 s |
| Paraphrase | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 11:11 | yes | 20 | 34 s |
| Paraphrase | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 09:42 | yes | 5 | 4 s |
| Paraphrase | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 12:15 | yes | 10 | 38 s |
| Paraphrase | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 13:42 | yes | 14 | 19 s |
| Paraphrase | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 07:42 | yes | 14 | 11 s |
| Paraphrase | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 10:23 | yes | 5 | 27 s |
| Paraphrase | GPT-6 Luna | gpt-6-luna | 2026-10-01 08:32 | yes | 3 | 10 s |
| Paraphrase | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 08:31 | yes | 15 | 26 s |
| Comparative | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 12:29 | yes | 18 | 9 s |
| Comparative | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 11:04 | yes | 4 | 9 s |
| Comparative | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 11:32 | yes | 12 | 23 s |
| Comparative | Perplexity Sonar | sonar | 2026-10-01 12:23 | yes | 18 | 4 s |
| Comparative | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 10:15 | yes | 15 | 5 s |
| Comparative | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 08:16 | yes | 10 | 8 s |
| Comparative | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 09:33 | yes | 24 | 37 s |
| Comparative | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 10:37 | yes | 5 | 1 s |
| Comparative | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 13:12 | no | 0 | 27 s |
| Comparative | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 07:50 | yes | 12 | 32 s |
| Comparative | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 07:45 | yes | 25 | 31 s |
| Comparative | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 08:36 | yes | 15 | 30 s |
| Comparative | GPT-6 Luna | gpt-6-luna | 2026-10-01 13:49 | yes | 4 | 19 s |
| Comparative | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 09:47 | yes | 14 | 24 s |
| Budget constrained | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 08:56 | no | 0 | 4 s |
| Budget constrained | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 08:05 | yes | 3 | 5 s |
| Budget constrained | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 13:12 | yes | 8 | 24 s |
| Budget constrained | Perplexity Sonar | sonar | 2026-10-01 12:58 | yes | 17 | 4 s |
| Budget constrained | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 08:58 | yes | 13 | 6 s |
| Budget constrained | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 09:38 | yes | 5 | 5 s |
| Budget constrained | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 13:28 | yes | 19 | 15 s |
| Budget constrained | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 10:25 | yes | 5 | 2 s |
| Budget constrained | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 13:40 | no | 0 | 38 s |
| Budget constrained | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 11:57 | yes | 14 | 17 s |
| Budget constrained | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 13:35 | yes | 15 | 40 s |
| Budget constrained | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 08:04 | yes | 5 | 26 s |
| Budget constrained | GPT-6 Luna | gpt-6-luna | 2026-10-01 12:02 | yes | 4 | 11 s |
| Budget constrained | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 08:09 | yes | 15 | 16 s |
| Scale constrained | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 07:43 | no | 0 | 5 s |
| Scale constrained | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 09:00 | no | 0 | 7 s |
| Scale constrained | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 10:42 | no | 0 | 14 s |
| Scale constrained | Perplexity Sonar | sonar | 2026-10-01 12:57 | yes | 19 | 5 s |
| Scale constrained | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 08:16 | no | 0 | 6 s |
| Scale constrained | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 09:12 | no | 0 | 6 s |
| Scale constrained | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 08:20 | yes | 17 | 35 s |
| Scale constrained | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 07:58 | yes | 5 | 2 s |
| Scale constrained | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 13:19 | no | 0 | 30 s |
| Scale constrained | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 12:01 | yes | 18 | 21 s |
| Scale constrained | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 09:10 | no | 0 | 14 s |
| Scale constrained | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 07:51 | yes | 13 | 32 s |
| Scale constrained | GPT-6 Luna | gpt-6-luna | 2026-10-01 09:06 | yes | 3 | 14 s |
| Scale constrained | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 09:42 | no | 0 | 2 s |
| Negative framing | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 08:21 | yes | 9 | 7 s |
| Negative framing | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 10:15 | yes | 4 | 5 s |
| Negative framing | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 13:11 | yes | 6 | 26 s |
| Negative framing | Perplexity Sonar | sonar | 2026-10-01 07:36 | yes | 19 | 3 s |
| Negative framing | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 09:46 | yes | 22 | 8 s |
| Negative framing | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 11:13 | yes | 5 | 5 s |
| Negative framing | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 12:23 | yes | 24 | 39 s |
| Negative framing | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 12:19 | yes | 5 | 3 s |
| Negative framing | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 11:07 | yes | 10 | 25 s |
| Negative framing | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 08:04 | yes | 24 | 23 s |
| Negative framing | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 11:07 | yes | 25 | 22 s |
| Negative framing | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 13:47 | no | 0 | 36 s |
| Negative framing | GPT-6 Luna | gpt-6-luna | 2026-10-01 09:10 | yes | 4 | 18 s |
| Negative framing | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 08:45 | yes | 23 | 40 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
DataStax Astra read as DataStax Astra DB
MongoDB Atlas read as MongoDB
MongoDB Atlas (M0 Free Tier) read as MongoDB
MongoDB Atlas (managed) read as MongoDB
Oracle NoSQL read as Oracle NoSQL Database
Riak read as Riak KV
Unresolved, counted raw
AWS Keyspaces
Apache Spark Streaming DBs
Azure DocumentDB
Google Firebase Firestore
NuoDB
Oracle Coherence
Oracle Spatial
PostgreSQL with JSONB columns
PostgreSQL with `JSONB`
Realtime Database
Redis Cluster (advanced setups)
Redis clusters
Self-managed MongoDB
Studio 3T
Velneo
Discontinued, still offered
No shut-down product was recommended here.