# Vector databases: which vendors AI models recommend (September 2026)

Source: Memetik Index, https://www.memetik.ai/index/vector-databases
Field: AI infra. Edition: 2026-09. Run: 2026-09-02.
Method: 5 fixed buyer prompts sent once each to 10 current models (GPT-5.6 Sol, GPT-5.6 Terra, GPT-5.6 Luna, Claude Opus 5, Claude Sonnet 5, Claude Fable 5, Gemini 3.6 Flash, Gemini 3.5 Flash, Sonar Pro, Sonar Reasoning Pro) via their APIs with web search enabled. 50 answers. Answer share = fraction of answers naming the vendor. Named first = fraction where it was the first tracked vendor named.

**pgvector leads** with 98% answer share (named in 49 of 50 answers) and was named first in 32%. Qdrant follows at 98%. 16 vendors were named at least once.

## Answer share

| # | Vendor | Answer share | Named first | Mentions | Models |
|---|---|---|---|---|---|
| 1 | pgvector | 98% | 32% | 49/50 | GPT-5.6 Sol, ChatGPT, GPT-5.6 Luna, Claude Opus 5, Claude, Claude Fable 5, Gemini, Gemini 3.5 Flash, Perplexity, Sonar Reasoning Pro |
| 2 | Qdrant | 98% | 24% | 49/50 | GPT-5.6 Sol, ChatGPT, GPT-5.6 Luna, Claude Opus 5, Claude, Claude Fable 5, Gemini, Gemini 3.5 Flash, Perplexity, Sonar Reasoning Pro |
| 3 | Pinecone | 94% | 38% | 47/50 | GPT-5.6 Sol, ChatGPT, GPT-5.6 Luna, Claude Opus 5, Claude, Claude Fable 5, Gemini, Gemini 3.5 Flash, Perplexity, Sonar Reasoning Pro |
| 4 | Weaviate | 90% | 0% | 45/50 | GPT-5.6 Sol, ChatGPT, GPT-5.6 Luna, Claude Opus 5, Claude, Claude Fable 5, Gemini, Gemini 3.5 Flash, Perplexity, Sonar Reasoning Pro |
| 5 | Milvus | 90% | 0% | 45/50 | GPT-5.6 Sol, ChatGPT, GPT-5.6 Luna, Claude Opus 5, Claude, Claude Fable 5, Gemini, Gemini 3.5 Flash, Perplexity, Sonar Reasoning Pro |
| 6 | Chroma | 48% | 4% | 24/50 | ChatGPT, GPT-5.6 Luna, Claude Opus 5, Claude, Claude Fable 5, Gemini, Gemini 3.5 Flash, Perplexity, Sonar Reasoning Pro |
| 7 | Elasticsearch | 46% | 0% | 23/50 | GPT-5.6 Sol, ChatGPT, GPT-5.6 Luna, Claude Opus 5, Claude, Claude Fable 5, Gemini, Gemini 3.5 Flash, Perplexity, Sonar Reasoning Pro |
| 8 | OpenSearch | 36% | 0% | 18/50 | GPT-5.6 Sol, ChatGPT, GPT-5.6 Luna, Claude Opus 5, Claude, Gemini, Gemini 3.5 Flash, Sonar Reasoning Pro |
| 9 | LanceDB | 20% | 0% | 10/50 | GPT-5.6 Luna, Claude Opus 5, Claude Fable 5, Gemini, Gemini 3.5 Flash |
| 10 | Redis | 18% | 0% | 9/50 | GPT-5.6 Luna, Claude Opus 5, Claude, Claude Fable 5, Gemini, Gemini 3.5 Flash |
| 11 | Turbopuffer | 14% | 0% | 7/50 | Claude Opus 5, Claude, Gemini, Gemini 3.5 Flash |
| 12 | Vespa | 10% | 0% | 5/50 | Claude Opus 5, Claude Fable 5, Gemini, Sonar Reasoning Pro |
| 13 | Supabase | 6% | 0% | 3/50 | GPT-5.6 Luna, Gemini |
| 14 | Neon | 6% | 0% | 3/50 | GPT-5.6 Luna, Gemini |
| 15 | MongoDB | 6% | 0% | 3/50 | Claude, Sonar Reasoning Pro |
| 16 | FAISS | 2% | 0% | 1/50 | ChatGPT |

## By model

- **GPT-5.6 Sol** (OpenAI, gpt-5.6-sol, 5 answers): pgvector 100%, Qdrant 100%, Pinecone 80%, Weaviate 80%, Milvus 80%
- **GPT-5.6 Terra** (OpenAI, gpt-5.6-terra, 5 answers): pgvector 100%, Qdrant 100%, Pinecone 80%, Milvus 80%, Weaviate 60%
- **GPT-5.6 Luna** (OpenAI, gpt-5.6-luna, 5 answers): pgvector 100%, Qdrant 100%, Pinecone 100%, Milvus 100%, Weaviate 80%
- **Claude Opus 5** (Anthropic, claude-opus-5, 5 answers): pgvector 100%, Qdrant 100%, Pinecone 100%, Weaviate 100%, Milvus 100%
- **Claude Sonnet 5** (Anthropic, claude-sonnet-5, 5 answers): pgvector 100%, Qdrant 100%, Pinecone 100%, Weaviate 100%, Milvus 100%
- **Claude Fable 5** (Anthropic, claude-fable-5, 5 answers): pgvector 100%, Qdrant 100%, Pinecone 100%, Weaviate 100%, Milvus 100%
- **Gemini 3.6 Flash** (Google, gemini-3.6-flash, 5 answers): pgvector 100%, Qdrant 100%, Pinecone 100%, Weaviate 100%, Milvus 100%
- **Gemini 3.5 Flash** (Google, gemini-3.5-flash, 5 answers): pgvector 100%, Qdrant 100%, Pinecone 100%, Weaviate 100%, Milvus 100%
- **Sonar Pro** (Perplexity, sonar-pro, 5 answers): pgvector 100%, Qdrant 100%, Pinecone 100%, Weaviate 100%, Milvus 80%
- **Sonar Reasoning Pro** (Perplexity, sonar-reasoning-pro, 5 answers): pgvector 80%, Qdrant 80%, Pinecone 80%, Weaviate 80%, Milvus 60%

## Sources the models cited

- firecrawl.dev: 176
- medium.com: 102
- dev.to: 73
- iternal.ai: 70
- alphacorp.ai: 51
- youtube.com: 42
- braintrust.dev: 39
- qdrant.tech: 33
- redis.io: 30
- pingcap.com: 22
- ranksquire.com: 18
- zenml.io: 15
- encore.dev: 15
- karthikeyanrathinam.medium.com: 14
- reddit.com: 14

## Prompts

1. What is the best vector database for a RAG application? Name specific products.
2. Which vector database would you recommend to a RAG application in 2026?
3. Compare the top vector database options right now.
4. I'm a RAG application and I need a vector database. What should I use and why?
5. Best vector database for a RAG application for a startup building AI search?

## Tracked but never named

None.

Raw answers: https://www.memetik.ai/index/vector-databases (every answer published in full). Chart image: https://www.memetik.ai/charts/vector-databases.png. Licence: CC BY 4.0, credit "Memetik Index".