Looker
Standing by category
| Category | Function | Share | Rank | Negative rate | Labels | Quadrant | Since September 2026 |
|---|---|---|---|---|---|---|---|
| Embedded analytics | Data platform | 0% | 67 of 72 | 43% | 35 | criticized challenger | |
| Data catalogs and governance | Data platform | 0% | 73 of 83 | 100% | 1 | under 10 labels · led by Atlan at 44% | |
| Telemetry pipelines | IT operations and endpoint | 0% | 64 of 124 | 0% | 1 | under 10 labels · led by OpenTelemetry Collector at 37% |
Movement
By model
How each model treated Looker 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 | 0 | 0 | 0 | 0 |
| GPT-5.4 mini | 0 | 2 | 1 | 0 | 3 |
| Gemini 3.5 Flash | 0 | 0 | 0 | 5 | 5 |
| Perplexity Sonar | 0 | 2 | 0 | 1 | 3 |
| Grok 4.1 Fast | 0 | 0 | 1 | 1 | 2 |
| Mistral Small | 0 | 0 | 2 | 0 | 2 |
| DeepSeek V4 Flash | 0 | 2 | 0 | 2 | 4 |
| Llama 4 Maverick | 0 | 1 | 0 | 0 | 1 |
| Qwen 3.7 Flash | 1 | 1 | 0 | 1 | 3 |
| Kimi K2 | 0 | 1 | 1 | 3 | 5 |
| GLM 4.7 FlashX | 0 | 2 | 0 | 2 | 4 |
| MiniMax M2.5 | 0 | 1 | 1 | 0 | 2 |
| GPT-6 Luna | 0 | 1 | 0 | 1 | 2 |
| Muse Glimmer 30B | 0 | 1 | 0 | 0 | 1 |
By framing
What the models said for it
Verbatim evidence the judge attached to positive labels.
“Choose GoodData/Looker if you are building a software product (SaaS) and need the analytics to look like part of the app itself.” Qwen 3.7 Flash · Embedded analytics · comparative prompt · first choice
“Looker is a better fit if your product needs semantic modeling, consistent metrics, and tighter governance” Perplexity Sonar · Embedded analytics · direct prompt · alternative
“Looker is the premium choice... Requires a dedicated developer... Higher barrier to entry than Sisense.” Qwen 3.7 Flash · Embedded analytics · direct prompt · alternative
“Looker | Excellent semantic layer (LookML), governance | Requires technical expertise; steep ramp” DeepSeek V4 Flash · Embedded analytics · scale 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.
“Avoid: Looker and Tableau unless you're already deeply invested in those ecosystems” Kimi K2 · Embedded analytics · paraphrase prompt · hard negative
“Avoid: Looker, Domo, ThoughtSpot, and GoodData if budget is your primary constraint” DeepSeek V4 Flash · Embedded analytics · budget prompt · hard negative
“Legacy BI tools repackaged as "embedded" (Tableau, Power BI, Looker, Qlik)” DeepSeek V4 Flash · Embedded analytics · negative prompt · hard negative
“Avoid: Enterprise platforms like Tableau, Looker, or Sisense” Kimi K2 · Embedded analytics · budget prompt · hard negative
Named alongside
The products named in the same answers as Looker, over the 109 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Looker 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 102 of the 109 answers that named Looker and are not a share of its labels.
Domains cited
No domain is on file for Looker, so its own site is not marked.
Pages cited
Pages are listed as the models cited them.
Search and answers
Where Looker stands in Google search beside where it stands in the models' answers.ShowHide
Search and answers
In search
Google, US estimates- Searches for its name, Google
- 18,100 a month (“looker”)
- AI search demand for its name, est.
- 2,051 a month
- Its own site
- No site of its own on file, so no site figures
In answers
This edition- First choices
- 6 across its categories
- Named in
- 109 answers
- Its own site cited
- No site on file to match
Search figures are US estimates from DataForSEO, read September 28, 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.
Names read as Looker
Follow Looker
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.
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 Looker'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 Looker, 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 Looker by email, built from the raw record of the edition. It shows:
- where Looker 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 Looker, 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.