# Lakebase: how AI models rank it, November 2026

Gane Index, November 2026 Edition. Named in 1 judge label across 1 category by 1 of 14 models. Page: https://www.gane.ai/index/it/vendors/lakebase

## Standing by category

| Category | Share | Rank | Negative rate | Labels |
|---|---|---|---|---|
| [Vector databases](https://www.gane.ai/index/it/it-data/vector-databases/index.md) | 0% | 21 | 0% | 1 |

## What the models said for it

- "Teams running Databricks who need transactional Postgres alongside their analytics and AI stack will find Lakebase the strongest fit." (Muse Glimmer 30B, Vector databases)

## By model

| Model | First choice | Alternative | Mention | Warning | Labels |
|---|---|---|---|---|---|
| Claude Haiku 4.5 | 0 | 0 | 0 | 0 | 0 |
| GPT-5.4 mini | 0 | 0 | 0 | 0 | 0 |
| Gemini 3.5 Flash | 0 | 0 | 0 | 0 | 0 |
| Perplexity Sonar | 0 | 0 | 0 | 0 | 0 |
| Grok 4.20 | 0 | 0 | 0 | 0 | 0 |
| Mistral Small | 0 | 0 | 0 | 0 | 0 |
| DeepSeek V4 Flash | 0 | 0 | 0 | 0 | 0 |
| Llama 4 Maverick | 0 | 0 | 0 | 0 | 0 |
| Qwen 3.7 Flash | 0 | 0 | 0 | 0 | 0 |
| Kimi K2 | 0 | 0 | 0 | 0 | 0 |
| GLM 4.7 FlashX | 0 | 0 | 0 | 0 | 0 |
| MiniMax M2.5 | 0 | 0 | 0 | 0 | 0 |
| GPT-6 Luna | 0 | 0 | 0 | 0 | 0 |
| Muse Glimmer 30B | 0 | 1 | 0 | 0 | 1 |

## Sources returned with these answers

The sites and pages cited by the answers that named Lakebase, from 1 of the 1 answers that named it (only the models that return a source list).

### Domains cited

- daily.dev: 1
- databricks.com: 1
- dev.to: 1
- docs.aws.amazon.com: 1
- gane.ai/index: 1
- github.com: 1
- lastingdynamics.com: 1
- learn.microsoft.com: 1
- milvus.io: 1
- mongodb.com: 1

### Pages cited

- http://learn.microsoft.com/en-us/azure/cosmos-db/vector-database: 1
- https://daily.dev/posts/vector-database-comparison-2026-pinecone-vs-weaviate-vs-qdrant-vs-opensearch-ufform1ua: 1
- https://databricks.com/blog/ai-database-solutions: 1
- https://dev.to/ipsphil_digital/vector-databases-compared-pinecone-weaviate-qdrant-chroma-milvus-32: 1
- https://dev.to/locionic/vector-databases-for-production-rag-2026-pinecone-vs-qdrant-vs-milvus-vs-pgvector-4fim: 1
- https://docs.aws.amazon.com/he_il/opensearch-service/latest/developerguide/serverless-vector-search.md: 1
- https://docs.aws.amazon.com/pdfs/prescriptive-guidance/latest/choosing-an-aws-vector-database-for-rag-use-cases/choosing-an-aws-vector-database-for-rag-use-cases.pdf: 1
- https://docs.aws.amazon.com/prescriptive-guidance/latest/choosing-an-aws-vector-database-for-rag-use-cases/vector-db-options.html: 1

## Record

- Method: https://www.gane.ai/index/it/methodology
- License: CC BY 4.0. Cite as Gane Index, November 2026 Edition, www.gane.ai/index/it.