Index › IT › Products › Firestore vector search · November 2026 Edition
Vector databases · Named, not ranked

Firestore vector search

–Too few to rank
1/14Models named it
0First choices
1Warnings
November 2026 Edition · all company sizes counted.
Ranked inVector databases #40 mid-market
What AI models recommend

Named, too few times to rank.

A product needs 10 labels in a category before a share is stated. Firestore vector search has 1.

Why models recommend it

No positive label carried a quote.

What gives them pause

“Firestore vector search if its limits conflict with your app. Its documented constraints include a 2,048-dimension maximum, a 1,000-document query limit”GPT-6 Luna · Negative question · warning

Also on the shortlist

The products named in the same answers as Firestore vector search, over the 1 answer that named it, and how often each was the first choice instead.

ProductNamed togetherPicked instead
10View →
10View →

A side-by-side page exists where both products are among a category's top eight.

How this is measured

Fourteen AI models from 12 labs are asked the same buying questions six ways, for three company sizes. Standings refresh every edition.

Gane sells AI visibility software. No company can pay to change a result.

Read the method →

Sources returned with these answers

The sites and pages the answers that named Firestore vector search came back with. A fact about retrieval, not a cause of the answer.

Fourteen of the fourteen models returned a source list in this edition: five return the pages they cited and nine the search results they consulted. These counts come from 1 of the 1 answers that named Firestore vector search and are not a share of its labels.

Domains cited

docs.aws.amazon.com1
docs.trychroma.com1
firebase.google.com1
github.com1
mongodb.com1

No domain is on file for Firestore vector search, so its own site is not marked.

Pages cited

Pages are listed as the models cited them.

By model

How each model answered on Firestore vector search across every prompt where it was named for a mid-market B2B company. Fourteen models, six prompts per category.
ModelFirst choiceAlternativeMentionWarningLabels
Claude Haiku 4.500000
GPT-5.4 mini00000
Gemini 3.5 Flash00000
Perplexity Sonar00000
Grok 4.2000000
Mistral Small00000
DeepSeek V4 Flash00000
Llama 4 Maverick00000
Qwen 3.7 Flash00000
Kimi K200000
GLM 4.7 FlashX00000
MiniMax M2.500000
GPT-6 Luna00011
Muse Glimmer 30B00000