Data quality and observability for enterprise buyers
Asked as “data observability platform”, and as “data quality tool”, on behalf of an enterprise B2B company. 49 first choices recorded across the direct, paraphrase, budget and scale prompts, fourteen models each. Added to the November 2026 Edition.
Standing · first-choice share
43%
Clear leader
43Monte Carlo14Informatica Data Quality08Acceldata35others
43% of first choices, clear leader.
Since October 2026▲+2Since October 2026: 41% → 43%, +2 points. Inside the 11-point floor: within noise.Monte Carlo held the lead, +2 points on 41%, inside the 11-point 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
Metaplane40%Contested▲+9Since October 2026: 31% → 40%, +9 points. Inside the 11-point floor: within noise.Standing →Mid-marketMetaplane23%Contested▼−8Since October 2026: 31% → 23%, −8 points. Inside the 11-point floor: within noise.Standing →Enterprise
Monte Carlo43%Clear leader▲+2Since October 2026: 41% → 43%, +2 points. Inside the 11-point floor: within noise.This page
Metaplane40%Contested▲+9Since October 2026: 31% → 40%, +9 points. Inside the 11-point floor: within noise.Standing →Mid-marketMetaplane23%Contested▼−8Since October 2026: 31% → 23%, −8 points. Inside the 11-point floor: within noise.Standing →EnterpriseMonte Carlo43%Clear leader▲+2Since October 2026: 41% → 43%, +2 points. Inside the 11-point floor: within noise.This pageThe standing
Share is the count of first choices across the direct, paraphrase, budget and scale prompts, over all fourteen models, for an enterprise B2B company. Ordered by share.
ProductFirst-choice shareNegative rateLabelsQuadrantSince October 2026
01Monte Carlo43%19%58endorsed leader▲+2Since October 2026: 41% → 43%, +2 points. Inside the 11-point floor: within noise.41% → 43%
Monte Carlo43%19%58endorsed leader▲+2Since October 2026: 41% → 43%, +2 points. Inside the 11-point floor: within noise.41% → 43%02
Informatica Data Quality14%0%11accepted challenger▲+6Since October 2026: 8% → 14%, +6 points. Inside the 11-point floor: within noise.8% → 14%
Informatica Data Quality14%0%11accepted challenger▲+6Since October 2026: 8% → 14%, +6 points. Inside the 11-point floor: within noise.8% → 14%03
Acceldata8%7%41accepted challenger▼−2Since October 2026: 10% → 8%, −2 points. Inside the 11-point floor: within noise.10% → 8%
Acceldata8%7%41accepted challenger▼−2Since October 2026: 10% → 8%, −2 points. Inside the 11-point floor: within noise.10% → 8%04
Informatica Intelligent Data Management Cloud8%13%23accepted challenger=heldSince October 2026: 8% → 8%, ±0 points. Inside the 11-point floor: within noise.8% → 8%
Informatica Intelligent Data Management Cloud8%13%23accepted challenger=heldSince October 2026: 8% → 8%, ±0 points. Inside the 11-point floor: within noise.8% → 8%05Datadog Data Observability4%47%19criticized challenger▲+2Since October 2026: 2% → 4%, +2 points. Inside the 11-point floor: within noise.2% → 4%
06
Bigeye2%20%40accepted challenger▲+2Since October 2026: 0% → 2%, +2 points. Inside the 11-point floor: within noise.0% → 2%
Bigeye2%20%40accepted challenger▲+2Since October 2026: 0% → 2%, +2 points. Inside the 11-point floor: within noise.0% → 2%07
Ataccama ONE2%9%22accepted challenger▼−2Since October 2026: 4% → 2%, −2 points. Inside the 11-point floor: within noise.4% → 2%
Ataccama ONE2%9%22accepted challenger▼−2Since October 2026: 4% → 2%, −2 points. Inside the 11-point floor: within noise.4% → 2%Show the eight products at 0%, ordered by negative rate
15Metaplane0%44%16criticized challenger▼−2Since October 2026: 2% → 0%, −2 points. Inside the 11-point floor: within noise.2% → 0%
Metaplane0%44%16criticized challenger▼−2Since October 2026: 2% → 0%, −2 points. Inside the 11-point floor: within noise.2% → 0%11
Anomalo0%30%30criticized challenger▼−2Since October 2026: 2% → 0%, −2 points. Inside the 11-point floor: within noise.2% → 0%
Anomalo0%30%30criticized challenger▼−2Since October 2026: 2% → 0%, −2 points. Inside the 11-point floor: within noise.2% → 0%14
Great Expectations0%28%18criticized challenger=heldSince October 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise.0% → 0%
Great Expectations0%28%18criticized challenger=heldSince October 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise.0% → 0%08
Soda0%18%33accepted challenger▼−4Since October 2026: 4% → 0%, −4 points. Inside the 11-point floor: within noise.4% → 0%
Soda0%18%33accepted challenger▼−4Since October 2026: 4% → 0%, −4 points. Inside the 11-point floor: within noise.4% → 0%13
Sifflet0%15%13accepted challenger▼−4Since October 2026: 4% → 0%, −4 points. Inside the 11-point floor: within noise.4% → 0%
Sifflet0%15%13accepted challenger▼−4Since October 2026: 4% → 0%, −4 points. Inside the 11-point floor: within noise.4% → 0%09
Collibra Data Quality & Observability0%12%17accepted challenger▼−2Since October 2026: 2% → 0%, −2 points. Inside the 11-point floor: within noise.2% → 0%
Collibra Data Quality & Observability0%12%17accepted challenger▼−2Since October 2026: 2% → 0%, −2 points. Inside the 11-point floor: within noise.2% → 0%10
DQLabs0%10%10accepted challenger▼−2Since October 2026: 2% → 0%, −2 points. Inside the 11-point floor: within noise.2% → 0%
DQLabs0%10%10accepted challenger▼−2Since October 2026: 2% → 0%, −2 points. Inside the 11-point floor: within noise.2% → 0%12
Datafold0%10%10accepted challenger=heldSince October 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise.0% → 0%
Datafold0%10%10accepted challenger=heldSince October 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise.0% → 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 can't be told apart from repeat variation, and both are shown. Each index measures its own floor from its own repeat, over its own categories, so the floor differs from one index to the next. Both editions asked the same fourteen models. 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.
For buyers
Head to headHead-to-head pages are published for mid-market buyers, so these open on mid-market figures, not enterprise.
All twenty-eight head-to-head pages: the top eight products, each against each
Metaplane vs Great ExpectationsMetaplane vs BigeyeMetaplane vs ElementaryMetaplane vs Ataccama ONEMonte Carlo vs SodaMonte Carlo vs Soda CoreMonte Carlo vs Great ExpectationsMonte Carlo vs BigeyeMonte Carlo vs ElementaryMonte Carlo vs Ataccama ONESoda vs Soda CoreSoda vs Great ExpectationsSoda vs BigeyeSoda vs ElementarySoda vs Ataccama ONESoda Core vs Great ExpectationsSoda Core vs BigeyeSoda Core vs ElementarySoda Core vs Ataccama ONEGreat Expectations vs BigeyeGreat Expectations vs ElementaryGreat Expectations vs Ataccama ONEBigeye vs ElementaryBigeye vs Ataccama ONEElementary vs Ataccama ONE
Recommended versus criticized
Every product with at least 10 labels here, on both axes. The 30% line names a quadrant, not the verdict above: that one needs more than 40%.
Criticized challengerCriticized default
Negative label rate →
01020304D05
06070809101112131415Accepted challengerEndorsed leader
0%First-choice share → · lines at 30% share and 25% negative60%
Key
01Monte Carlo43%
Monte Carlo43%02Informatica Data Quality14%
Informatica Data Quality14%03Acceldata8%
Acceldata8%04Informatica Intelligent Data Management Cloud8%
Informatica Intelligent Data Management Cloud8%05Datadog Data Observability4%
06Bigeye2%
Bigeye2%07Ataccama ONE2%
Ataccama ONE2%08Soda0%
Soda0%09Collibra Data Quality & Observability0%
Collibra Data Quality & Observability0%10DQLabs0%
DQLabs0%11Anomalo0%
Anomalo0%12Datafold0%
Datafold0%13Sifflet0%
Sifflet0%14Great Expectations0%
Great Expectations0%15Metaplane0%
Metaplane0%What they warned about
Two of fourteen models held their first choice under the paraphrase. 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 and Muse Glimmer 30B 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.
Anomalo30%
9 of 30 labels negative · 9 of 14 models
“all are meaningfully present in the market, but enterprise buyers should compare them on source coverage, governance, alert routing, and total implementation effort” GPT-5.4 mini, negative prompt
Monte Carlo19%
11 of 58 labels negative · 8 of 14 models · 5 hard negative
“Some AI models explicitly advise avoiding Monte Carlo if you need precise, actionable alerts or have limited resources.” Mistral Small, negative prompt
Datadog Data Observability
47%
9 of 19 labels negative · 8 of 14 models · 4 hard negative
“Datadog received a very high negative rate (approx. 48% in recent industry benchmarks)... general-purpose infrastructure monitoring tool rather than a specialized data observability solution” Qwen 3.7 Flash, negative prompt
Bigeye20%
8 of 40 labels negative · 7 of 14 models · 3 hard negative
“Pure-play alerting tools (early-stage Monte Carlo, Bigeye) unless you have a dedicated data reliability team.” DeepSeek V4 Flash, negative prompt
What they cite
Fourteen of the fourteen models returned a source list in this edition: five return the pages they cited and nine the search results they consulted.
Sites the answers cite
75 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. 962 links across 241 sites, every framing counted. Ranked by the number of answers carrying the site or page. 20 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.
34 answers · 81 citations · 11 models
vendor site · Acceldata28 answers · 48 citations · 13 models
vendor site · DQLabs23 answers · 36 citations · 10 models
vendor site · G221 answers · 31 citations · 11 models
18 answers · 19 citations · 10 models
vendor site · Monte Carlo16 answers · 26 citations · 8 models
14 answers · 20 citations · 8 models
vendor site · DataKitchen14 answers · 18 citations · 10 models
vendor site · Bigeye13 answers · 18 citations · 9 models
vendor site · Atlan11 answers · 15 citations · 5 models
vendor site · groundcover11 answers · 14 citations · 11 models
vendor site · Metaplane11 answers · 13 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 · 9 models
16 answers · 9 models
16 answers · 9 models
15 answers · 8 models
11 answers · 11 models
9 answers · 7 models
9 answers · 7 models
9 answers · 5 models
8 answers · 4 models
8 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 data observability platform”, “data observability platform”, “data quality and observability platforms”. 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 |
|---|---|---|---|---|---|---|---|---|
| Monte Carlo montecarlo.ai | 43% | 160 | 135,000 | 23,412 | 24,468 | |||
| Informatica parent site Informatica Data Quality 14% | 14% | 12 | 14,800 | 1,414 | ||||
| Acceldata acceldata.io | 8% | 76 | 880 | 23 | 60,654 | 3 | 5 | |
| Informatica Intelligent Data Management Cloud informatica.com | 8% | 366 | 390 | 1 | 73,321 | 120 | ||
| groundcover groundcover.com | 8% | 29 | 14,800 | 1,328 | 9,109 | $792 | 24 | |
| Prizm | 4% | |||||||
| Datadog parent site Datadog Data Observability 4% | 4% | 62 | 74,000 | 3,541 | ||||
| Bigeye bigeye.com | 2% | 111 | 33,100 | 618 | 9,473 | $18,069 | 4 | 5 |
| Ataccama ONE ataccama.com | 2% | 118 | 90 | 0 | 2,552 | 14 | ||
| Coralogix coralogix.com | 2% | 116 | 4,400 | 83 | 10,540 | $28 | 27 |
Google's top ten for the three searches hold 19 sites; 6 of them are among the sites the models cited here (acceldata.io, bigeye.com, datakitchen.io, dqlabs.ai, g2.com, metaplane.dev). The first result for “best data observability platform” 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 data observability platform for an enterprise B2B company?”Paraphrase“Which data quality tool would you recommend to a large B2B company with thousands of employees?”Comparative“What are the top enterprise-grade data quality and observability platforms and how do they differ?”Budget-constrained“What is the best data observability platform for a large company that needs predictable total cost across thousands of users?”Scale-constrained“We are a 5,000 person company with SSO, SOC 2 and procurement review requirements evaluating a data observability platform. What should we look at?”Negative“Which data quality and observability platforms should a large enterprise avoid or be cautious about?”
Claude Haiku 4.5no first choiceno first choiceHeldno first choicegroundcoverno first choiceagainst: Anomalo, Ataccama ONE, Collibra Data Quality & Observability, Informatica Intelligent Data Management Cloud, Talend
Four alternatives
Acceldata, Bigeye, Monte Carlo, SodaTwo alternatives
New Relic, OpenObserveGPT-5.4 miniMonte CarloInformatica Intelligent Data Management CloudChangedSodaDatadog Data Observabilityagainst: Sodano first choiceagainst: Acceldata, Anomalo, Bigeye, Informatica Intelligent Data Management Cloud, Monte Carlo, Soda, Telmai
Two alternatives
Acceldata, BigeyeOne alternative
SodaFive alternatives
Acceldata, Bigeye, GX Core, Great Expectations, Monte CarloTwo alternatives
Bigeye, Monte CarloGemini 3.5 FlashAcceldata, Monte CarloMonte CarloChangedMonte CarloAcceldatano first choiceagainst: Great Expectations, Soda Core
Three alternatives
Anomalo, IBM Databand, SodaThree alternatives
Collibra Data Quality, Informatica Cloud Data Governance and Quality, OpenpriseSeven alternatives
Acceldata, Anomalo, Ataccama ONE, Collibra Data Quality & Observability, Informatica Data Quality, Informatica Intelligent Data Management Cloud, SodaThree alternatives
Anomalo, Monte Carlo, SodaPerplexity SonarMonte CarloInformatica Intelligent Data Management CloudChangedMonte Carlogroundcoveragainst: Datadog Data Observability, Dynatraceno first choiceagainst: Acceldata, Coalesce Quality, DQLabs, Datadog Data Observability, Elementary, IBM Databand, Lightup, Metaplane, OvalEdge
Six alternatives
Acceldata, Atlan, Coalesce Quality, Elementary, IBM Databand, PrizmThree alternatives
Ataccama ONE, DemandTools, Monte CarloNine alternatives
Anomalo, Ataccama ONE, Atlan, Bigeye, Collibra Data Quality & Observability, Elementary, Great Expectations, Informatica Intelligent Data Management Cloud, SodaTwo alternatives
Grafana Cloud, New RelicGrok 4.20Monte Carloagainst: Dynatrace, Metaplane, New RelicInformatica Data Quality, Informatica Intelligent Data Management CloudChangedMonte Carloagainst: IBMAcceldataagainst: Collibra Data Quality & ObservabilityMonte Carloagainst: Datadog Data Observability, Dynatrace, Elastic, New Relicagainst: Acceldata, Anomalo, Bigeye, Monte Carlo, Soda
Four alternatives
Acceldata, Anomalo, Bigeye, SodaSix alternatives
Alation, Ataccama ONE, Collibra Data Quality & Observability, Precisely Data Integrity Suite, SAS Data Quality, TalendSix alternatives
Acceldata, Anomalo, Bigeye, Collibra Data Quality & Observability, Informatica Intelligent Data Management Cloud, SodaTwo alternatives
Monte Carlo, SodaThree alternatives
Acceldata, Bigeye, DQLabsMistral SmallMonte CarloInformatica Data QualityChangedMonte Carlo DataLast9, groundcoverno first choiceagainst: Anomalo, Bigeye, Datadog Data Observability, Monte Carlo
Four alternatives
Acceldata, DQLabs, Informatica Intelligent Data Management Cloud, MetaplaneThree alternatives
Ataccama ONE, SAS Data Quality, Talend Data QualitySix alternatives
Acceldata, Bigeye, DQLabs, Great Expectations, Informatica Intelligent Data Management Cloud, SodaOne alternative
CoralogixDeepSeek V4 FlashMonte CarloInformatica Data QualityChangedMonte Carloagainst: Great ExpectationsDatadog Data Observabilityagainst: Bigeye, Monte Carlo, SodaMonte Carloagainst: Great Expectationsagainst: AWS DataZone, Anomalo, Azure Purview, Bigeye, Google Dataplex, Great Expectations, IBM InfoSphere QualityStage, Informatica DQ, Monte Carlo, Sifflet, Soda, legacy SAS/QMF
Three alternatives
Acceldata, Bigeye, DQLabsTwo alternatives
Ataccama ONE, Collibra Data Quality & ObservabilitySix alternatives
Acceldata, Anomalo, Ataccama ONE, Collibra Data Quality & Observability, Informatica Intelligent Data Management Cloud, SodaTwo alternatives
Acceldata, SiffletFive alternatives
Bigeye, DQLabs, Databand, Datafold, dbtLlama 4 MaverickMonte CarloIntegrate.ioChangedno first choicePrizmno first choiceagainst: Anomalo, Bigeye, Datadog Data Observability, Metaplane
Three alternatives
Acceldata, Informatica Data Quality, Seemore DataTwo alternatives
DQE One, Data Quality NavigatorQwen 3.7 FlashMonte CarloInformatica Intelligent Data Management CloudChangedMonte CarloAcceldataagainst: Monte Carlono first choiceagainst: Anomalo, Datadog Data Observability, Informatica Intelligent Data Management Cloud, Metaplane, Talend
Five alternatives
Acceldata, DQ Labs, Datadog Data Observability, Dynatrace, SplunkThree alternatives
Microsoft Purview, Talend, TrifactaFive alternatives
Acceldata, Ataccama ONE, Databricks, Seemore Data, SodaTwo alternatives
Bigeye, DataKitchen TestGenKimi K2Monte CarloInformatica Data QualityChangedMonte Carloagainst: Precisely Data Integrity SuitePrizmagainst: Bigeye, Datadog Data Observability, Metaplane, Monte CarloMonte Carloagainst: Monte Carlo
Two alternatives
Acceldata, DQLabsOne alternative
Ataccama ONEEight alternatives
Acceldata, Anomalo, Ataccama ONE, Bigeye, Great Expectations, Informatica Data Quality, Metaplane, SodaTwo alternatives
Acceldata, SiffletFour alternatives
Anomalo, Bigeye, Datafold, IBM DatabandGLM 4.7 FlashXMonte CarloInformatica Data QualityChangedMonte Carlogroundcoveragainst: Anomalo, Monte Carlo, New Relicno first choicenothing named
Three alternatives
Acceldata, Anomalo, Great ExpectationsThree alternatives
Ataccama ONE, IBM Data Quality Solutions, Monte CarloEight alternatives
Acceldata, Anomalo, Ataccama ONE, Bigeye, Datafold, Great Expectations, Informatica Intelligent Data Management Cloud, SodaTwo alternatives
Datadog Data Observability, MetaplaneMiniMax M2.5Monte CarloInformatica Data QualityChangedMonte Carloagainst: Great ExpectationsCoralogixagainst: Datadog Data Observability, Monte Carlo, SplunkMonte Carloagainst: Monte Carlo
Two alternatives
Acceldata, DQLabsThree alternatives
Ataccama ONE, Collibra Data Quality & Observability, IBM InfoSphere QualityStageNine alternatives
Acceldata, Bigeye, Collibra Data Quality & Observability, DQLabs, IBM watsonx.data, Informatica Intelligent Data Management Cloud, Metaplane, Sifflet, SodaTwo alternatives
New Relic, OpenObserveThree alternatives
Acceldata, Informatica Intelligent Data Management Cloud, ValidioGPT-6 LunaMonte CarloMonte CarloHeldno first choiceMonte Carlono first choiceagainst: Ataccama ONE, Datafold, Great Expectations GX Cloud, Lightup, Metaplane, Pantomath, RightData, Sifflet, Snowflake, Soda, Telmai, Tencent Cloud
Three alternatives
Acceldata, Bigeye, Datadog Data ObservabilityTwo alternatives
Informatica Data Quality, SodaEight alternatives
Anomalo, Ataccama ONE, Bigeye, Collibra Data Quality & Observability, Informatica IDMC — Data Quality & Observability, Monte Carlo, Precisely Data Integrity Suite, SodaOne alternative
SodaMuse Glimmer 30BMonte CarloAtaccama ONE, Informatica Data QualityChangedCollibra Data Intelligence Cloud with Data Quality module, Informatica Intelligent Data Management Cloud IDMC – Intelligent Data Quality, Monte CarloBigeye, Monte Carloagainst: Chronosphere, New Relicno first choiceagainst: Anomalo, Bigeye, Datadog Data Observability, Metaplane
Three alternatives
Acceldata, Bigeye, DQLabsOne alternative
TalendSix alternatives
Acceldata, Ataccama ONE, Bigeye, Pentaho, Precisely Data Quality, Qlik Talend Data QualityBold 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.
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 | 2026-11-01 13:12 | no | 0 | 4 s |
| Direct recommendation | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-11-01 17:54 | yes | 2 | 5 s |
| Direct recommendation | Gemini 3.5 Flash | gemini-3.6-flash (asked for gemini-3.5-flash; the API answered as this version) | 2026-11-01 13:16 | yes | 8 | 24 s |
| Direct recommendation | Perplexity Sonar | sonar | 2026-11-01 09:49 | yes | 10 | 4 s |
| Direct recommendation | Grok 4.20 | spacexai/grok-4.20-non-reasoning via xai | 2026-11-01 16:32 | yes | 10 | 14 s |
| Direct recommendation | Mistral Small | mistral/mistral-small via mistral | 2026-11-01 08:48 | yes | 5 | 4 s |
| Direct recommendation | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-11-01 14:15 | yes | 24 | 32 s |
| Direct recommendation | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-11-01 18:11 | yes | 5 | 3 s |
| Direct recommendation | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-11-01 12:31 | yes | 5 | 21 s |
| Direct recommendation | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-11-01 09:35 | yes | 13 | 24 s |
| Direct recommendation | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-11-01 19:23 | yes | 17 | 43 s |
| Direct recommendation | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-11-01 13:40 | yes | 10 | 50 s |
| Direct recommendation | GPT-6 Luna | gpt-6-luna | 2026-11-01 15:11 | yes | 5 | 17 s |
| Direct recommendation | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-11-01 17:20 | yes | 12 | 21 s |
| Paraphrase | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-11-01 19:09 | no | 0 | 5 s |
| Paraphrase | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-11-01 08:41 | yes | 2 | 4 s |
| Paraphrase | Gemini 3.5 Flash | gemini-3.6-flash (asked for gemini-3.5-flash; the API answered as this version) | 2026-11-01 12:00 | no | 0 | 14 s |
| Paraphrase | Perplexity Sonar | sonar | 2026-11-01 08:55 | yes | 10 | 3 s |
| Paraphrase | Grok 4.20 | spacexai/grok-4.20-non-reasoning via xai | 2026-11-01 18:23 | yes | 5 | 8 s |
| Paraphrase | Mistral Small | mistral/mistral-small via mistral | 2026-11-01 11:52 | yes | 5 | 4 s |
| Paraphrase | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-11-01 16:35 | yes | 21 | 33 s |
| Paraphrase | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-11-01 16:01 | yes | 5 | 3 s |
| Paraphrase | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-11-01 09:13 | no | 0 | 37 s |
| Paraphrase | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-11-01 12:24 | yes | 18 | 47 s |
| Paraphrase | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-11-01 09:16 | yes | 15 | 21 s |
| Paraphrase | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-11-01 09:19 | yes | 5 | 21 s |
| Paraphrase | GPT-6 Luna | gpt-6-luna | 2026-11-01 14:54 | yes | 3 | 13 s |
| Paraphrase | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-11-01 08:49 | yes | 15 | 19 s |
| Comparative | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-11-01 09:39 | yes | 16 | 11 s |
| Comparative | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-11-01 10:39 | yes | 6 | 8 s |
| Comparative | Gemini 3.5 Flash | gemini-3.6-flash (asked for gemini-3.5-flash; the API answered as this version) | 2026-11-01 15:11 | yes | 16 | 36 s |
| Comparative | Perplexity Sonar | sonar | 2026-11-01 13:43 | yes | 10 | 7 s |
| Comparative | Grok 4.20 | spacexai/grok-4.20-non-reasoning via xai | 2026-11-01 16:22 | yes | 10 | 18 s |
| Comparative | Mistral Small | mistral/mistral-small via mistral | 2026-11-01 10:40 | yes | 18 | 15 s |
| Comparative | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-11-01 10:31 | yes | 23 | 43 s |
| Comparative | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-11-01 15:30 | yes | 5 | 2 s |
| Comparative | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-11-01 11:28 | yes | 5 | 27 s |
| Comparative | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-11-01 09:31 | yes | 23 | 44 s |
| Comparative | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-11-01 19:39 | yes | 25 | 80 s |
| Comparative | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-11-01 11:29 | yes | 20 | 58 s |
| Comparative | GPT-6 Luna | gpt-6-luna | 2026-11-01 10:11 | yes | 9 | 31 s |
| Comparative | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-11-01 17:45 | yes | 25 | 40 s |
| Budget constrained | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-11-01 17:50 | yes | 17 | 9 s |
| Budget constrained | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-11-01 12:22 | yes | 4 | 7 s |
| Budget constrained | Gemini 3.5 Flash | gemini-3.6-flash (asked for gemini-3.5-flash; the API answered as this version) | 2026-11-01 16:45 | yes | 12 | 24 s |
| Budget constrained | Perplexity Sonar | sonar | 2026-11-01 17:52 | yes | 10 | 4 s |
| Budget constrained | Grok 4.20 | spacexai/grok-4.20-non-reasoning via xai | 2026-11-01 18:28 | yes | 22 | 18 s |
| Budget constrained | Mistral Small | mistral/mistral-small via mistral | 2026-11-01 17:51 | yes | 5 | 4 s |
| Budget constrained | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-11-01 15:57 | yes | 24 | 50 s |
| Budget constrained | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-11-01 14:24 | yes | 5 | 3 s |
| Budget constrained | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-11-01 11:45 | yes | 18 | 63 s |
| Budget constrained | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-11-01 09:09 | yes | 20 | 25 s |
| Budget constrained | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-11-01 12:39 | yes | 23 | 260 s |
| Budget constrained | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-11-01 10:30 | yes | 18 | 63 s |
| Budget constrained | GPT-6 Luna | gpt-6-luna | 2026-11-01 15:06 | yes | 2 | 14 s |
| Budget constrained | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-11-01 10:09 | yes | 20 | 30 s |
| Scale constrained | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-11-01 16:59 | yes | 19 | 12 s |
| Scale constrained | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-11-01 09:00 | yes | 3 | 7 s |
| Scale constrained | Gemini 3.5 Flash | gemini-3.6-flash (asked for gemini-3.5-flash; the API answered as this version) | 2026-11-01 17:18 | no | 0 | 19 s |
| Scale constrained | Perplexity Sonar | sonar | 2026-11-01 12:11 | yes | 10 | 11 s |
| Scale constrained | Grok 4.20 | spacexai/grok-4.20-non-reasoning via xai | 2026-11-01 13:43 | yes | 14 | 16 s |
| Scale constrained | Mistral Small | mistral/mistral-small via mistral | 2026-11-01 15:52 | no | 0 | 9 s |
| Scale constrained | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-11-01 14:15 | yes | 24 | 50 s |
| Scale constrained | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-11-01 15:22 | yes | 5 | 4 s |
| Scale constrained | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-11-01 18:10 | no | 0 | 34 s |
| Scale constrained | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-11-01 14:43 | no | 0 | 61 s |
| Scale constrained | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-11-01 10:34 | yes | 19 | 17 s |
| Scale constrained | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-11-01 16:39 | yes | 5 | 25 s |
| Scale constrained | GPT-6 Luna | gpt-6-luna | 2026-11-01 13:24 | yes | 5 | 17 s |
| Scale constrained | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-11-01 19:26 | yes | 14 | 19 s |
| Negative framing | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-11-01 18:25 | yes | 37 | 16 s |
| Negative framing | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-11-01 12:30 | yes | 7 | 8 s |
| Negative framing | Gemini 3.5 Flash | gemini-3.6-flash (asked for gemini-3.5-flash; the API answered as this version) | 2026-11-01 12:20 | no | 0 | 16 s |
| Negative framing | Perplexity Sonar | sonar | 2026-11-01 17:53 | yes | 10 | 5 s |
| Negative framing | Grok 4.20 | spacexai/grok-4.20-non-reasoning via xai | 2026-11-01 17:16 | yes | 15 | 19 s |
| Negative framing | Mistral Small | mistral/mistral-small via mistral | 2026-11-01 11:11 | yes | 5 | 5 s |
| Negative framing | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-11-01 12:27 | yes | 25 | 41 s |
| Negative framing | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-11-01 18:12 | yes | 5 | 3 s |
| Negative framing | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-11-01 15:47 | yes | 5 | 27 s |
| Negative framing | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-11-01 18:57 | yes | 23 | 26 s |
| Negative framing | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-11-01 11:34 | yes | 21 | 123 s |
| Negative framing | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-11-01 19:28 | yes | 24 | 83 s |
| Negative framing | GPT-6 Luna | gpt-6-luna | 2026-11-01 13:50 | yes | 2 | 34 s |
| Negative framing | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-11-01 13:30 | yes | 24 | 32 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
Ataccama read as Ataccama ONE
Collibra read as Collibra Data Quality & Observability
Datadog read as Datadog Data Observability
Datadog (Data Observability module) read as Datadog Data Observability
IBM Data Observability by Databand read as IBM Databand
Informatica read as Informatica Intelligent Data Management Cloud
Informatica (CDGC) read as Informatica Intelligent Data Management Cloud
Informatica (Data Quality & Governance) read as Informatica Intelligent Data Management Cloud
Informatica (part of the Infa IDMC suite) read as Informatica Intelligent Data Management Cloud
Unresolved, counted raw
AWS DataZone
Apache DataFusion
Collibra Data Intelligence Cloud with Data Quality module
Data Quality Navigator
Elementry
Experian B2B Data Quality
Great Expectations GX Cloud
IBM Data Quality Solutions
Informatica Cloud Data Governance and Quality (CDGC)
Informatica IDMC — Data Quality & Observability
Informatica Intelligent Data Management Cloud IDMC – Intelligent Data Quality
Microsoft Purview Data Quality
Precisely Data Quality
Qlik Talend Data Quality
SAP Data Quality
SAP Data Quality Management
Validity DemandTools
ZoomInfo RingLead
legacy SAS/QMF
Discontinued, still offered
No shut-down product was recommended here.