| Category | Function | Share | Rank | Negative rate | Labels | Quadrant | Since September 2026 |
|---|---|---|---|---|---|---|---|
| SaaS metrics and analytics | Planning and analysis | 2% | 14 of 78 | 45% | 11 | criticized challenger | ▲+2Since September 2026: 0% → 2%, +2 points. Inside the 10-point floor: within noise. Read over the models both editions asked. |
| Financial reporting and dashboards | Planning and analysis | 0% | 19 of 105 | 6% | 17 | accepted challenger | =heldSince September 2026: 0% → 0%, ±0 points. Inside the 10-point floor: within noise. Read over the models both editions asked. |
| Category | September 2026 | Now | Change | Reading | Rank |
|---|---|---|---|---|---|
| SaaS metrics | 0% | 2% | ▲+2Since September 2026: 0% → 2%, +2 points. Inside the 10-point floor: within noise. Read over the models both editions asked. | Within noise | Rank 15 → 14 of 78 |
| Financial reporting | 0% | 0% | =heldSince September 2026: 0% → 0%, ±0 points. Inside the 10-point floor: within noise. Read over the models both editions asked. | Within noise | Rank 20 → 19 of 105 |
Shares here are read over the models both editions asked, so they can differ by a point or two from the standing above, which counts every model in this edition.
The floor is 10 points of share, measured: how far the models move a leader on their own when the same questions are asked twice with nothing changed. A larger change is movement; a smaller one is noise, and both are shown. Movement is read over the twelve models both editions asked; GPT-6 Luna, Muse Glimmer 30B joined this edition and are in the standing but not yet in the comparison. How the floor is measured · The editions
| Model | First choice | Alternative | Mention | Negative | Labels |
|---|---|---|---|---|---|
| Claude Haiku 4.5 | 0 | 1 | 1 | 1 | 3 |
| GPT-5.4 mini | 1 | 1 | 0 | 0 | 2 |
| Gemini 3.5 Flash | 0 | 1 | 1 | 2 | 4 |
| Perplexity Sonar | 0 | 1 | 0 | 0 | 1 |
| Grok 4.1 Fast | 0 | 1 | 0 | 0 | 1 |
| Mistral Small | 0 | 1 | 1 | 1 | 3 |
| DeepSeek V4 Flash | 0 | 0 | 2 | 0 | 2 |
| Llama 4 Maverick | 0 | 0 | 0 | 0 | 0 |
| Qwen 3.7 Flash | 0 | 1 | 0 | 1 | 2 |
| Kimi K2 | 0 | 0 | 0 | 0 | 0 |
| GLM 4.7 FlashX | 0 | 0 | 3 | 0 | 3 |
| MiniMax M2.5 | 0 | 0 | 4 | 1 | 5 |
| GPT-6 Luna | 0 | 1 | 0 | 0 | 1 |
| Muse Glimmer 30B | 0 | 1 | 0 | 0 | 1 |
Verbatim evidence the judge attached to positive labels.
“If you want one default recommendation, I'd usually pick Looker for a mid-market B2B company” GPT-5.4 mini · SaaS metrics · direct prompt · first choice
“Looker works well for Google Cloud organizations that want code-first governance through LookML.” Claude Haiku 4.5 · Financial reporting · comparative prompt · alternative
“Governance & semantic consistency: Looker for metric definition in code across an organization.” Muse Glimmer 30B · Financial reporting · comparative prompt · alternative
“Choose Looker if your biggest challenge is conflicting data definitions across departments” Qwen 3.7 Flash · Financial reporting · comparative prompt · alternative
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.
“too heavy and expensive for early-stage SaaS companies” Mistral Small · SaaS metrics · negative prompt · hard negative
“you might not yet have the massive data engineering resources required to build custom BI dashboards (like Looker or Tableau)” Gemini 3.5 Flash · SaaS metrics · direct prompt · soft negative
“Modern BI Tools (e.g., Looker, PowerBI, Tableau): Better if you have a dedicated data team... requires technical resources.” Qwen 3.7 Flash · SaaS metrics · scale prompt · soft negative
“Struggles with customer-facing analytics and has premium pricing that can be prohibitive” Claude Haiku 4.5 · SaaS metrics · negative prompt · soft negative
Citations exist only for the models that return a source list, five of the fourteen in this edition, so these counts come from 88 of the 98 answers that named Looker and are not a share of its labels.
171 of the 171 domain citations in answers naming Looker came from somebody else's page.
Pages are listed as the models cited them.
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.
| Kind | Pages | Last 90 days | 2025-10 to 2026-09 | Latest | Categories named |
|---|---|---|---|---|---|
| Blog | 37 | 14 | 2026-09-28 | ||
| Case study | 19 | undated | |||
| Conference or event | 4 | undated | |||
| Report or ebook | 2 | undated | |||
| Glossary or explainer | 2 | undated | |||
| Webinar or virtual event | 1 | undated | |||
| Template or tool | 1 | undated | |||
| News or press | 1 | undated |
Every page cloud.google.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 September 29, 2026.
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.
What Looker's own pages state, read September 28, 2026: cloud.google.com. A claimed page can correct any of them.
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.
A new claim receives the current edition's vendor brief for Looker by email, built from the raw record of the edition. It shows: