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Data quality and observability · November 2026 Edition

Soda Core vs Ataccama ONE

Zero of fourteen models named Soda Core first on the direct prompt; zero named Ataccama ONE. Soda Core was named by eight of the fourteen models and Ataccama ONE by eleven and Soda Core carries 10 labels and Ataccama ONE 15, so the shares are not directly comparable.

Soda Core

criticized challenger

Named in one category this edition.

Ataccama ONE

accepted challenger

Named in three categories this edition.

First-choice share6%4%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate30%7%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#4#8A position in a field of 14; printed, not drawn.
Labels1015A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Soda Core 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 Soda Core · Metaplane vs Ataccama ONE · Monte Carlo vs Soda Core

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.
Soda CoreFirst choices, of fourteen modelsAtaccama ONE
Direct00
Paraphrase021 against Ataccama ONE
Comparative01
Budget-constrained301 against Soda Core
Scale-constrained00
Negative002 against Soda Core
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 Soda Core and Ataccama ONE 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
Soda Core Ataccama ONE first choice named as an alternative argued againstblank: not namedEach cell is one answer, Soda Core on the left and Ataccama ONE on the right.

The direct prompt

The plain question, one answer per model, grouped by where Soda Core and Ataccama ONE stood in it.

Neither was named

14 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
Gemini 3.5 FlashMetaplane alternatives: Bigeye, Elementary Data, Monte Carlo
Perplexity SonarMetaplane alternatives: Elementary, Monte Carlo, Soda
Grok 4.20Metaplane alternatives: Bigeye, Datadog Observability, Soda
Mistral SmallDatadog Data Observability, New Relic alternatives: IBM Instana
DeepSeek V4 FlashMonte Carlo alternatives: Metaplane, Sifflet
Llama 4 Maverickno first choice
Qwen 3.7 FlashMonte Carlo alternatives: Bigeye, Datadog Data Observability, Soda
Kimi K2Metaplane alternatives: Bigeye, Soda
GLM 4.7 FlashXMonte Carlo alternatives: Bigeye, Sifflet
MiniMax M2.5Monte Carlo alternatives: Acceldata, DQLabs, Metaplane, Soda
GPT-6 LunaSoda alternatives: Datadog Data Observability, Monte Carlo
Muse Glimmer 30BBigeye alternatives: Acceldata, DQLabs, Metaplane, Soda

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
Soda Core leads by two points.
Soda Core2%#– of 11
Ataccama ONE0%#– of 11
The full small business standing →
Mid-marketThe figures above
Soda Core leads by two points.
Soda Core6%#4 of 14
Ataccama ONE4%#8 of 14
The full mid-market standing →
Enterprise
The order flips: Ataccama ONE leads at enterprise.
Ataccama ONE2%#7 of 15
Soda Core0%#– of 15
The full enterprise standing →

What the models said about Soda Core

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

“Open-source data quality engines (like Great Expectations or Soda) ... self-hosting and maintaining them at scale requires significant engineering bandwidth.” Gemini 3.5 Flash · negative prompt · soft negative
“It's fast and flexible, but limited to the command line. There's no UI, alerting, or centralized tracking.” Muse Glimmer 30B · negative prompt · soft negative
“Caution: Per-table pricing can escalate quickly with many tables” Kimi K2 · budget prompt · soft negative
“For most small to mid-sized companies with limited budgets, Soda Core or Elementary would be excellent starting points” GLM 4.7 FlashX · budget prompt · first choice
“Best ultra-budget option: Great Expectations or Soda Core for basic data quality checks” GPT-5.4 mini · budget prompt · first choice
“Start with GX Core or Soda Core for data quality testing” MiniMax M2.5 · budget 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

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.