IT AI Index

Index › Products › DataObservability.ai · October 2026 Edition

1 category · Named, not ranked

DataObservability.ai

3Judge labels

1First choices

0Negative labels

2 / 14Models named it

1Category

October 2026 Edition. Every number here is derived from the raw labels under vendor table v2026-10.8, every buyer segment counted.

Standing

2 labels, too few to rank

A product needs 10 labels in a category before a share or quadrant is stated. DataObservability.ai was named 2 times in Data observability, where Metaplane led with 31%. The labels and the evidence are below, counted exactly.

By buyer segmentRead the same way at every buyer size.

Small business48 of 101

under the label threshold

Mid-market16 of 110

under the label threshold

Enterprisenot named

No model named it for this buyer.

In data observability · each standing computed within its segment · bars are 0 to 100 · the accent bar is the product's own best reading

Standing by category

Every category where a model named DataObservability.ai for a mid-market B2B company. Share is first choices across the direct, paraphrase, budget and scale prompts; rank is within every product named in that category.

Category	Function	Share	Rank	Negative rate	Labels	Quadrant	Since September 2026
Data quality and observability	Data platform	2%	16 of 110	0%	2	under 10 labels · led by Metaplane at 31%

Movement

This is the first edition on this tier, so no move can be computed for DataObservability.ai yet. From the next edition this section shows, per buyer segment and per category, whether its share moved by more than the measured noise floor.

By model

How each model treated DataObservability.ai across every prompt where it was named for a mid-market B2B company. Fourteen models, six prompts per category.

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Model	First choice	Alternative	Labels
Claude Haiku 4.5	0	0	0
GPT-5.4 mini	0	0	0
Gemini 3.5 Flash	1	0	1
Perplexity Sonar	0	0	0
Grok 4.1 Fast	0	0	0
Mistral Small	0	0	0
DeepSeek V4 Flash	0	0	0
Llama 4 Maverick	0	0	0
Qwen 3.7 Flash	0	0	0
Kimi K2	0	1	1
GLM 4.7 FlashX	0	0	0
MiniMax M2.5	0	0	0
GPT-6 Luna	0	0	0
Muse Glimmer 30B	0	0	0

By framing

Which of the six questions produced the naming. By model says how often; this says asked what. The first-choice count on the right carries the marks of the models that produced it.

FramingLabels by classFirst choices

Direct0 labelsNone

Paraphrase0 labelsNone

Comparative0 labelsNone

Budget-constrained2 labels1

Scale-constrained0 labelsNone

Negative1 labelNone

First choiceAlternativeMentionNegative3 labels in all, every segment counted; 1 of the 1 first choices count toward share, since the comparative and negative framings do not. The bar is one segment per label class, to scale within the framing.

What the models said for it

Verbatim evidence the judge attached to positive labels.

“Use Metaplane (free tier/low tier) or DataObservability.ai.” Gemini 3.5 Flash · Data observability · budget prompt · first choice

“DataObservability.ai ⭐ Most Transparent Pricing” Kimi K2 · Data observability · budget prompt · alternative

And against it

Verbatim evidence attached to negative labels. A warning on a product with few labels is a warning; on a product with many, it is one voice among them.

No model argued against it.

Named alongside

The products named in the same answers as DataObservability.ai, over the 3 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and DataObservability.ai was named but was not.

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ProductSame answerTook the first choice insteadHead to head

Metaplane3 of 31Not among the top eight

Elementary2 of 32Not among the top eight

Soda Core2 of 31Not among the top eight

Anomalo2 of 30Not among the top eight

Bigeye2 of 30Not among the top eight

Great Expectations2 of 30Not among the top eight

Monte Carlo2 of 30Not among the top eight

dbt tests1 of 31Not among the top eight

AnomalyArmor1 of 30Not among the top eight

Datadog1 of 30Not among the top eight

A head-to-head page exists where both products are among a category's top eight. The other rows are the same fact without a page behind them, so they link to the product instead.

What carried it into the answer

The sites and pages cited by the answers that named DataObservability.ai. A fact about retrieval, not a lever on the model.

Citations exist only for the models that return a source list, five of the fourteen in this edition, so these counts come from 3 of the 3 answers that named DataObservability.ai and are not a share of its labels.

Domains cited

atlan.com3

dataobservability.aiYour site3

acceldata.io2

decube.io2

synq.io2

airbyte.com1

appsglobal.co1

bestdataobservability.tools1

blog.anomalyarmor.ai1

checkthat.ai1

Fourteen of the seventeen domain citations in answers naming DataObservability.ai came from somebody else's page.

Pages cited

atlan.com/know/data-observability-tools3

dataobservability.ai/data-observability-pricing2

dataobservability.ai/pricing2

decube.io/post/open-source-data-observability2

synq.io/blog/the-10-best-data-observability-tools-in-20252

appsglobal.co/articles/which-data-observability-software-is-best-for-small-businesses1

bestdataobservability.tools/1

acceldata.io/blog/data-observability-tool-pricing-complete-breakdown-cost-guide1

Pages are listed as the models cited them.

What it publishes

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Addresses on dataobservability.ai

155

subdomains included

Content

101

counted in the table below

Documentation

Product · Integration

9 · 0

Kind	Pages	Last 90 days	Latest	Categories named
Blog	58	27	2026-09-01	Observability, Data warehouses
Comparison	41	12	2026-10-01	Data catalogs
Glossary or explainer	2	0	2026-06-01

Most recent

2026-10-01Deequ alternatives comparison
2026-10-01Acceldata vs datadog pricing comparison
2026-10-01Atlan vs collibra pricing comparison
2026-09-01Metaplane pricing blog
2026-09-01Elementary data pricing blog
2026-09-01Alation vs collibra pricing comparison

How it is countedHide how it is counted

Every page dataobservability.ai exposes, subdomains included. Kind is read from the address and title. The last 90 days, the latest date and the twelve months count pages by when they were published, from the site's feeds, a date in the address, or the page's own publication date, read from up to a hundred of its most recently changed pages; a page that says only when it last changed is counted in its kind but not in when, so the recent counts are a floor, and a kind none of whose pages gives a publication date reads undated. Read October 5, 2026.

Follow DataObservability.ai

An email the morning each edition publishes: where this product moved, where it held, and by how much against the noise floor. One address, confirmed by a click; a stop link in every email.

Already following? Everything you follow, with a stop for each.

The company

DataObservability.ai is its own company.

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In its own words

Stated by the vendor, not checked

Positioning: A self-serve data observability platform that monitors freshness, volume, schema, and anomalies across your warehouse, maps end-to-end data lineage, and tracks incidents, so data teams catch broken data before stakeholders do.
dataobservability.ai
For: For data teams on Snowflake, BigQuery, Databricks and Redshift
dataobservability.ai
Price stated: Starter is 59.50 dollars per month, Team is 179.50 dollars per month, Scale is 479.50 dollars per month and Enterprise is 999 dollars per month, billed yearly dataobservability.ai
Starting price: 59.50 dataobservability.ai
Free plan or trial: 14-day trial, no credit card required dataobservability.ai
Certifications: SSOaudit log
Hosting: in-VPC deployment
Not stated on the pages read: Integrations, customers

What DataObservability.ai's own pages state, read October 6, 2026: dataobservability.ai, dataobservability.ai/pricing, dataobservability.ai/solutions/dbt. A claimed page can correct any of them.

Is this your product?

Claim this page

Claiming is free and changes nothing in the data. A claimed page shows a verified contact who is told when each edition publishes and when DataObservability.ai's standing changes by more than the noise floor; the right to propose corrections to the vendor table, meaning names the judge wrote that should or should not read as DataObservability.ai, applied by version and listed in the change log; and a one-line description supplied by the vendor and marked as such.

What a new claim receivesHide what a new claim receives

A new claim receives the current edition's vendor brief for DataObservability.ai by email, built from the raw record of the edition. It shows:

where DataObservability.ai is named, by buyer and by framing, and which cells hold its first choices;
the claims the models make when they name it, ranked, with the strongest and the weakest quoted;
its vocabulary against the segment leader's, and the pages the models cited;
who was chosen in the answers that did not name DataObservability.ai, and every reason the record gives;
a battlecard for each top rival: the head-to-head split, why they win, and the reservation quoted against them;
one page of published figures cleared to show a buyer.