1 category · RankedBigeye
Standing by category
| Category | Function | Share | Rank | Negative rate | Labels | Quadrant | Since September 2026 |
|---|---|---|---|---|---|---|---|
| Data quality and observability | Data platform | 3% | 7 of 110 | 23% | 35 | accepted challenger |
Movement
By model
How each model treated Bigeye across every prompt where it was named for a mid-market B2B company. Fourteen models, six prompts per category.ShowHide
By model
| Model | First choice | Alternative | Mention | Negative | Labels |
|---|---|---|---|---|---|
| Claude Haiku 4.5 | 0 | 2 | 0 | 0 | 2 |
| GPT-5.4 mini | 1 | 1 | 0 | 0 | 2 |
| Gemini 3.5 Flash | 0 | 0 | 0 | 1 | 1 |
| Perplexity Sonar | 0 | 1 | 1 | 1 | 3 |
| Grok 4.1 Fast | 0 | 0 | 2 | 1 | 3 |
| Mistral Small | 0 | 0 | 1 | 1 | 2 |
| DeepSeek V4 Flash | 0 | 2 | 1 | 0 | 3 |
| Llama 4 Maverick | 0 | 1 | 1 | 0 | 2 |
| Qwen 3.7 Flash | 0 | 0 | 1 | 1 | 2 |
| Kimi K2 | 0 | 2 | 2 | 1 | 5 |
| GLM 4.7 FlashX | 0 | 2 | 0 | 0 | 2 |
| MiniMax M2.5 | 0 | 2 | 0 | 0 | 2 |
| GPT-6 Luna | 0 | 0 | 1 | 0 | 1 |
| Muse Glimmer 30B | 2 | 1 | 0 | 2 | 5 |
By framing
What the models said for it
Verbatim evidence the judge attached to positive labels.
“Bigeye is positioned as the Enterprise AI Trust Platform combining data observability, end-to-end lineage and agentic AI governance. It is recommended for large enterprises 500+ employees” Muse Glimmer 30B · Data observability · scale prompt · first choice
“Bigeye is the platform most guides explicitly call out for mid-market ease of use and quick implementation” Muse Glimmer 30B · Data observability · direct prompt · first choice
“If you want production observability with automated anomaly detection: Monte Carlo or Bigeye” GPT-5.4 mini · Data observability · comparative prompt · first choice
“Enterprise AI trust and data observability platform for improving data quality, lineage visibility, sensitivity discovery, governance, and AI policy enforcement.” Muse Glimmer 30B · Data observability · comparative 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.
“Why be cautious: integration gaps for non-standard stacks, workspace management overhead, and enterprise-only quote pricing.” Muse Glimmer 30B · Data observability · negative prompt · soft negative
“users note tuning needs; consolidation (e.g., via acquisitions like Datadog/Metaplane) may disrupt roadmaps” Grok 4.1 Fast · Data observability · negative prompt · soft negative
“Monte Carlo, Anomalo, Bigeye: While popular, these ML-based platforms are best for specific use cases” Mistral Small · Data observability · negative prompt · soft negative
“contrasted with usage-based enterprise pricing like Monte Carlo / Bigeye at $25K–$100K+/year” Muse Glimmer 30B · Data observability · budget prompt · soft negative
Named alongside
The products named in the same answers as Bigeye, over the 105 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Bigeye was named but was not.ShowHide
Named alongside
What carried it into the answer
Citations exist only for the models that return a source list, five of the fourteen in this edition, so these counts come from 99 of the 105 answers that named Bigeye and are not a share of its labels.
Domains cited
364 of the 364 domain citations in answers naming Bigeye came from somebody else's page.
Pages cited
Pages are listed as the models cited them.
Search and answers
bigeye.com ranks 5 on Google for the category's searches. In the answers, Bigeye takes 3% of first choices and Metaplane takes 31%.ShowHide
Search and answers
In search
Google, US estimates- Searches for its name, Google
- 33,100 a month (“bigeye”)
- AI search demand for its name, est.
- 618 a month
- Organic visits to its site
- about 9,473 a month
- Searches its site ranks for
- 674 · 31 in the top three
- Sites linking to it
- 3,197
- Paid Google search
- 3 searches its ads show for, about 2,358 clicks and $18,069 a month (estimate)
In answers
This edition- First choices
- 3 across its categories
- Named in
- 105 answers
- Its own site cited
- in 99 of the answers that named it
Search figures are US estimates from DataForSEO, read October 5, 2026; AI search demand is its modeled, directional estimate, not a count of queries to any assistant. The answers are this edition's. Two measurements side by side: neither is read as the cause of the other.
What it publishes
ShowHide
What it publishes
ShowHide| Kind | Pages | Last 90 days | 2025-11 to 2026-10 | Latest | Categories named |
|---|---|---|---|---|---|
| Blog | 259 | 0 | 2026-03-24 | Observability, ETL | |
| Glossary or explainer | 190 | undated | ETL, Data catalogs | ||
| Webinar or virtual event | 34 | 2 | 2026-10-01 | ||
| Conference or event | 28 | 1 | 2026-09-23 | ||
| Comparison | 15 | undated | ETL, Observability | ||
| Case study | 11 | 0 | 2026-01-27 | ||
| News or press | 2 | undated | |||
| Podcast or video | 1 | undated |
Most recent
- 2026-10-01Operationalize your data program with bigeye share webinar or virtual event
- 2026-09-23Ai leaders dinner big data london 2026 conference or event
- 2026-08-19Reducing ai agent costs at enterprise scale webinar or virtual event
- 2026-06-17Data ai leaders dinner databricks conference or event
- 2026-06-02Data ai leaders dinner snowflake summit conference or event
- 2026-04-22Data ai leaders dinner data decoded conference or event
Events in person
- September 23, 2026AI Leaders Dinner at Big Data LondonIdalia At Pillar Hall, 5 Olympia Way London W14 0EY, London, United Kingdom · hosted
As the event pages on bigeye.com state them, read October 5, 2026.
How it is countedHide how it is counted
Every page bigeye.com 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. An event counts as online when its address or title says so (webinar, on demand, virtual or online summit); a conference, summit, trade show, expo or roadshow that does not say so is counted as a conference or event, which on a vendor's site is mostly in person. Read October 5, 2026.
Names read as Bigeye
Follow Bigeye
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
Bigeye is its own company.ShowHide
The company
In its own words
Stated by the vendor, not checked- Positioning
- Bigeye is the data and AI trust platform for large enterprises.
- For
- large enterprises
- Certifications
- EU AI ActISO 42001SOC 2 Type IIISO 27001:2022ISO/IEC 27001 certificationSOC 2ISO 27001
- Hosting
- AWS
- Customers named
- NOVFreedom MortgageZoomUdacityJustAnswerSimpleRoseImpiraCrux
- Not stated on the pages read
- Price, starting price, free plan or trial, integrations
What Bigeye's own pages state, read October 5, 2026: bigeye.com, trust.bigeye.com, bigeye.com/platform/security, bigeye.com/customer-stories, bigeye.com/partners. A claimed page can correct any of them.
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 Bigeye'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 Bigeye, 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 Bigeye by email, built from the raw record of the edition. It shows:
- where Bigeye 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 Bigeye, 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.