Which vector databases and RAG infrastructure vendors AI assistants name, Oct 2026
How ChatGPT, Perplexity and Google AI Mode answer 3 buyer questions on vector databases and RAG infrastructure: 15 answers, 19 Oct 2026, India.
What this page is. AEOLedger sells answer engine optimization services, so we have an interest in these results. 15 answers (3 questions × 5 runs), collected 19 October 2026 from one location, read by hand. Vendors are named only when an assistant named them; being listed here is a count, not an endorsement or a ranking. The data is downloadable as CSV.
Summary
- Read per question (5 answers each). Vector databases: Qdrant, Pinecone, Weaviate, Milvus and pgvector were named in 5 of 5; Qdrant was named first in all three ChatGPT runs. Only Google AI Mode added more (OpenSearch and Elasticsearch).
- PDF and table parsing: Docling, LlamaParse, Unstructured, Reducto and Azure AI Document Intelligence were named in 5 of 5.
- Managed RAG: Vectara, AWS Bedrock Knowledge Bases, Google Vertex AI and Azure AI Search were named in 5 of 5; Ragie in 3 of 5.
- 13 further vendors were named in 1 or 2 of 15 answers each.
- 1 further vendor named in 3 to 9 of 15 answers is not shown (see limits).
The 3 buyer questions
Written in buyer wording, without any vendor’s name. Each question was asked in a fresh session every run (runs per question: ChatGPT 3, Perplexity 1, Google AI Mode 1), so 5 answers per question and 15 in all.
| # | Type | Question |
|---|---|---|
| C4-1 | shortlist | “What is the best vector database for a production RAG application in 2026?” |
| C4-2 | shortlist | “Which tools parse complex PDFs and tables into clean chunks for RAG?” |
| C4-3 | requirements | “Which managed RAG platforms or retrieval APIs are best for building production RAG apps without running infrastructure?” |
Who gets named
Named in X of N: answers whose text recommends or lists the vendor.
Apps, 19 October 2026, Bengaluru, India. ChatGPT (logged out, fresh chat, 3 runs per question); Perplexity (signed in to one account, fresh thread, 1 run per question); Google AI Mode (signed in, personalisation off, 1 run per question). N = 15 answers per vendor.
| Vendor | Answers | Named | By assistant |
|---|---|---|---|
| Qdrant | 8 of 15 | ChatGPT 6/9, Perplexity 1/3, Google AI Mode 1/3 | |
| Pinecone | 7 of 15 | ChatGPT 5/9, Perplexity 1/3, Google AI Mode 1/3 | |
| Weaviate | 7 of 15 | ChatGPT 5/9, Perplexity 1/3, Google AI Mode 1/3 | |
| AWS Bedrock Knowledge Bases | 5 of 15 | ChatGPT 3/9, Perplexity 1/3, Google AI Mode 1/3 | |
| Azure AI Document Intelligence | 5 of 15 | ChatGPT 3/9, Perplexity 1/3, Google AI Mode 1/3 | |
| Azure AI Search | 5 of 15 | ChatGPT 3/9, Perplexity 1/3, Google AI Mode 1/3 | |
| Docling | 5 of 15 | ChatGPT 3/9, Perplexity 1/3, Google AI Mode 1/3 | |
| Google Vertex AI (Search / RAG Engine) | 5 of 15 | ChatGPT 3/9, Perplexity 1/3, Google AI Mode 1/3 | |
| LlamaParse | 5 of 15 | ChatGPT 3/9, Perplexity 1/3, Google AI Mode 1/3 | |
| Milvus | 5 of 15 | ChatGPT 3/9, Perplexity 1/3, Google AI Mode 1/3 | |
| Reducto | 5 of 15 | ChatGPT 3/9, Perplexity 1/3, Google AI Mode 1/3 | |
| Unstructured | 5 of 15 | ChatGPT 3/9, Perplexity 1/3, Google AI Mode 1/3 | |
| Vectara | 5 of 15 | ChatGPT 3/9, Perplexity 1/3, Google AI Mode 1/3 | |
| pgvector | 5 of 15 | ChatGPT 3/9, Perplexity 1/3, Google AI Mode 1/3 | |
| Marker | 3 of 15 | ChatGPT 2/9, Perplexity 0/3, Google AI Mode 1/3 | |
| Ragie | 3 of 15 | ChatGPT 1/9, Perplexity 1/3, Google AI Mode 1/3 |
What gets cited
Domains the assistants linked as sources, counted once per answer.
| Domain | Type | Cited in |
|---|---|---|
| github.com | cited site | 8 of 15 |
| pinecone.io | cited site | 7 of 15 |
| qdrant.tech | cited site | 7 of 15 |
| weaviate.io | cited site | 6 of 15 |
| learn.microsoft.com | cited site | 6 of 15 |
| docs.pinecone.io | cited site | 5 of 15 |
| milvus.io | cited site | 4 of 15 |
| reducto.ai | cited site | 4 of 15 |
| developers.llamaindex.ai | cited site | 4 of 15 |
| azure.microsoft.com | cited site | 4 of 15 |
Method
- Fieldwork 19 Oct 2026, Bengaluru, India, consumer apps. 3 buyer questions written without any vendor name. ChatGPT was asked each question 3 times (logged out, fresh chat); Perplexity and Google AI Mode once each. 15 answers, every one read in full.
- A vendor counts as named when the answer recommends or compares it, including a row in a comparison table. Vendors mentioned only as an acquisition note, as 'the big names to avoid', or in follow-up suggestions are not counted. Product lines are counted under their company.
The full protocol is on the methodology page.
Limits
- 15 answers per category, 5 per question: enough to say 'named in every answer' or 'in none', not to order vendors in the middle (95% intervals overlap).
- One location (India), no VPN. Perplexity ran in one signed-in account and Google AI Mode signed in with personalisation off; logged-out results may differ.
- Companies we track are not named on this page. As anonymised vendors they appear as 'a RAG infrastructure vendor' only when named in 10 or more of 15 answers, shown as '10+ of 15' without a per-assistant split, and are otherwise left out; their sites are left out of the sources list.
Data
Download the data (CSV): one row per vendor and per cited domain, with the count, N, assistant split, date and location. Licensed CC BY 4.0: reuse it with a link to this page.
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How to cite and changes
AEOLedger. “Which vector databases and RAG infrastructure vendors AI assistants name, Oct 2026.” AI Shortlist Index v1.0, 22 October 2026. https://aeoledger.com/research/ai-shortlist/rag-infrastructure/
- v1.0, 22 October 2026: first published.
Spotted an error? Write to hello@aeoledger.com. Corrections are dated here.