Data quality and observability · November 2026 Edition
Bigeye vs Ataccama ONE
One of fourteen models named Bigeye first on the direct prompt; zero named Ataccama ONE. Bigeye was named by thirteen of the fourteen models and Ataccama ONE by eleven and Bigeye carries 40 labels and Ataccama ONE 15, so the shares are not directly comparable.
Named in one category this edition.
Named in three categories this edition.
First-choice share4%4%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate28%7%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#6#8A position in a field of 14; printed, not drawn.
Labels4015A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Bigeye reading right to left. Rank and label count are printed, not drawn.Monte Carlo was named alongside these two in nine of the fourteen direct answers. Metaplane vs Bigeye · Metaplane vs Ataccama ONE · Monte Carlo vs Bigeye
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 data quality and observability page.
By framing
How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
BigeyeFirst choices, of fourteen modelsAtaccama ONE
Direct10
Paraphrase021 against Ataccama ONE
Comparative21
Budget-constrained005 against Bigeye
Scale-constrained10
Negative006 against Bigeye
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 Bigeye and Ataccama ONE stood in it.
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| 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 | | |
Bigeye Ataccama ONE first choice named as an alternative argued againstblank: not namedEach cell is one answer, Bigeye on the left and Ataccama ONE on the right.The direct prompt
The plain question, one answer per model, grouped by where Bigeye and Ataccama ONE stood in it.
Bigeye first, Ataccama ONE not the choice
1 of 14 modelsAtaccama ONE was named in the answer but not as the choice, or not at all.Muse Glimmer 30BBigeye alternatives: Acceldata, DQLabs, Metaplane, Soda
Neither was the first choice, one was named
5 of 14 modelsThe answer put something else first and named one of the two as an alternative.Gemini 3.5 FlashMetaplane alternatives: Bigeye, Elementary Data, Monte Carlo
Grok 4.20Metaplane alternatives: Bigeye, Datadog Observability, Soda
Qwen 3.7 FlashMonte Carlo alternatives: Bigeye, Datadog Data Observability, Soda
Kimi K2Metaplane alternatives: Bigeye, Soda
GLM 4.7 FlashXMonte Carlo alternatives: Bigeye, Sifflet
Neither was named
8 of 14 modelsThe answer made no first choice from these two in this category.Claude Haiku 4.5Metaplane, New Relic alternatives: Monte Carlo, Motadata ObserveOps
GPT-5.4 miniMetaplane alternatives: Monte Carlo
Perplexity SonarMetaplane alternatives: Elementary, Monte Carlo, Soda
Mistral SmallDatadog Data Observability, New Relic alternatives: IBM Instana
DeepSeek V4 FlashMonte Carlo alternatives: Metaplane, Sifflet
Llama 4 Maverickno first choice
MiniMax M2.5Monte Carlo alternatives: Acceldata, DQLabs, Metaplane, Soda
GPT-6 LunaSoda alternatives: Datadog Data Observability, Monte Carlo
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
Mid-marketThe figures above
Level: the same share of first choices.
Bigeye4%#6 of 14Ataccama ONE4%#8 of 14Enterprise
Level: the same share of first choices.
Bigeye2%#6 of 15Ataccama ONE2%#7 of 15What the models said about Bigeye
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Avoid if on a Tight Budget ... Bigeye (~$45K+/year)” DeepSeek V4 Flash · budget prompt · hard negative
“can be a good fit, but it is positioned as an enterprise-grade platform; its docs and vendor materials emphasize enterprise use cases more than budget-first adoption” GPT-5.4 mini · budget prompt · soft negative
“Emerging or niche tools (e.g., older mentions of Bigeye, Acceldata, Databand): These have fewer public complaints but mixed adoption.” Grok 4.20 · negative prompt · soft negative
“Bigeye is the most cited mid-market-friendly option with transparent per-table pricing and auto metric generation.” Muse Glimmer 30B · direct prompt · first choice
“Want cross-stack incident detection and lineage? Start with Monte Carlo and Bigeye.” GPT-6 Luna · comparative prompt · first choice
“Bigeye or Acceldata for hybrid support and pipeline‑layer visibility.” GLM 4.7 FlashX · comparative prompt · first choice
What the models said about Ataccama ONE
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of five in this category shown.
“Consider only when the business needs enterprise-grade master-data management, governance, complex entity matching, or compliance controls” Perplexity Sonar · paraphrase prompt · soft negative
“Data Quality | Informatica, Ataccama | Talend, Oracle, Precisely” GLM 4.7 FlashX · comparative prompt · first choice
“I recommend starting with Ataccama ONE or Talend Data Fabric.” Grok 4.20 · paraphrase prompt · first choice
“🏆 Top Recommendation: Ataccama ONE” Kimi K2 · paraphrase prompt · first choice
Also compared
Metaplane vs BigeyeMetaplane vs Ataccama ONEMonte Carlo vs BigeyeMonte Carlo vs Ataccama ONESoda vs BigeyeSoda vs Ataccama ONESoda Core vs BigeyeSoda Core vs Ataccama ONEGreat Expectations vs BigeyeGreat Expectations vs Ataccama ONEBigeye vs ElementaryElementary vs Ataccama ONEAll twenty-eight head-to-head pages and the full data observability standing
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.