Pinecone vs pgvector vs Qdrant: which is named first in AI answers?

Pinecone is named first most often in this sample, appearing first in 19 of 50 recorded answers, while pgvector and Qdrant are each named in more answers overall. Our Vector databases research compares mention frequency and first position for all three across recorded buyer questions. Across the whole category, Pinecone led 3 of the 5 recorded questions, pgvector led 2, and Qdrant led 1. Ties stay ties. Below are the counts by vendor, by question, and by model family, along with what this dated sample can and can’t tell you.

Which of Pinecone, pgvector and Qdrant is named first most often?

Pinecone. It was named in 47 of 50 answers and first in 19. pgvector was named in 49 of 50 answers and first in 16. Qdrant was named in 49 of 50 answers and first in 12. The mention order and the first-position order are not the same. Pinecone appears in the fewest answers of the three and still takes the most opening slots.

Vendor	Named answers	Answer share	Named first answers	Named first share
Pinecone	47/50	94%	19/50	38%
pgvector	49/50	98%	16/50	32%
Qdrant	49/50	98%	12/50	24%

If you’re assessing software visibility in AI answers, the two columns answer two different questions.

What do answer share and named first measure?

Answer share measures whether a tracked vendor appears at all. Named first records the earliest appearance among all 16 tracked vendors, including products outside the selected three. A mention counts when the vendor’s name or a known alias appears in the answer text.

The category tracks 16 products and all 16 were named. This article selects Pinecone, pgvector and Qdrant for comparison. First-position counts for the three sum to 47 of 50 answers. Other tracked products occupy 2 first positions. 1 recorded answer contains no matched tracked vendor, so it has no named-first vendor.

How does the named-first order change across the five recorded questions?

It changes a lot. Pinecone took 8 of 10 first positions on question 3 and 1 of 10 on question 2. Qdrant took 5 of 10 on question 5 and 0 of 10 on question 3.

Recorded question	Pinecone named first	pgvector named first	Qdrant named first	All-category first-position leader
1. What is the best vector database for a RAG application? Name specific products.	4/10 (40%)	3/10 (30%)	2/10 (20%)	Pinecone
2. Which vector database would you recommend to a RAG application in 2026?	1/10 (10%)	5/10 (50%)	3/10 (30%)	pgvector
3. Compare the top vector database options right now.	8/10 (80%)	2/10 (20%)	0/10 (0%)	Pinecone
4. I’m a RAG application and I need a vector database. What should I use and why?	4/10 (40%)	4/10 (40%)	2/10 (20%)	Pinecone, pgvector
5. Best vector database for a RAG application for a startup building AI search?	2/10 (20%)	2/10 (20%)	5/10 (50%)	Qdrant

Question 4 ends in a tie at 4/10 each for Pinecone and pgvector, and it’s recorded as a tie. A single question would have given you a different winner each time. That’s the reason to inspect all 5 and the limits of one dated sample.

Does the named-first leader change with the model family asked?

Yes, and the swing is wide. In the 15 OpenAI answers, Pinecone was named first 3 times, pgvector 3 times, Qdrant 9 times. In the 15 Anthropic answers, Pinecone was named first 4 times, pgvector 9 times, Qdrant 0 times.

Provider family	Recorded answers	Pinecone named first	pgvector named first	Qdrant named first
OpenAI	15	3/15 (20%)	3/15 (20%)	9/15 (60%)
Anthropic	15	4/15 (26.7%)	9/15 (60%)	0/15 (0%)
Google	10	5/10 (50%)	4/10 (40%)	1/10 (10%)
Perplexity	10	7/10 (70%)	0/10 (0%)	2/10 (20%)

Each sampled model carries equal weight. OpenAI contributes 15 answers, Anthropic 15, Google 10, Perplexity 10. Perplexity never named pgvector first in its 10 answers. Prompts stay fixed between editions, so the next edition measures the same 5 questions against the same models.

How far can one dated sample of 50 answers take you?

Far enough to describe what these models said in September 2026, and no further. The category contains 50 recorded answers from 10 models across 5 fixed English-language buyer questions. We report the dated sample and the exact questions so you can judge whether a visibility comparison supports your conclusion.

One response was collected per question-model combination. The results are a descriptive snapshot. Repeated controlled trials would be needed to estimate repeatability or isolate wording effects. Alias matching is string-based. API models differ from consumer chat products. Prompts are English and not localised. The full method and every raw answer sit behind the figures above.

How should you use these visibility counts when choosing a vector database?

Use them to understand where your product sits in AI answers, not to pick a database. First position records where a product name appears in the text. Product quality, purchase suitability and recommendation strength are separate questions, and these counts don’t touch them.

We help software founders and growth teams understand their products’ measured visibility in AI answers. The decision this data serves is how to read an AI-visibility comparison. A purchase decision needs separate evidence about your requirements and the products themselves. More editions are in the research, and you can subscribe for the next one.

Frequently asked questions

Is Pinecone named ahead of Qdrant in this research?

On first position, yes. Pinecone was named first in 19 of 50 answers (38%), Qdrant in 12 of 50 (24%). On answer share the order flips: Qdrant appears in 49 of 50 answers (98%), Pinecone in 47 of 50 (94%).

Vendor	Named answers	Answer share	Named first answers	Named first share
Pinecone	47/50	94%	19/50	38%
pgvector	49/50	98%	16/50	32%
Qdrant	49/50	98%	12/50	24%

How do pgvector and Qdrant differ in these recorded answers?

They’re level on answer share and split by model family. Both were named in 49 of 50 answers (98%), and pgvector took 16 first positions to Qdrant’s 12. The split sits in who’s asking: Anthropic answers named pgvector first 9 of 15 times and Qdrant first 0 times, while OpenAI answers named Qdrant first 9 of 15 times.

Provider family	Recorded answers	Pinecone named first	pgvector named first	Qdrant named first
OpenAI	15	3/15 (20%)	3/15 (20%)	9/15 (60%)
Anthropic	15	4/15 (26.7%)	9/15 (60%)	0/15 (0%)
Google	10	5/10 (50%)	4/10 (40%)	1/10 (10%)
Perplexity	10	7/10 (70%)	0/10 (0%)	2/10 (20%)

How do Pinecone and pgvector compare on first position?

Pinecone leads 19 to 16 across the 50 answers, and the lead comes from one question. Pinecone was named in 47 of 50 answers and first in 19. pgvector was named in 49 of 50 answers and first in 16. The mention order and the first-position order are not the same. On question 2, pgvector took 5 of 10 first positions to Pinecone’s 1. On question 3, Pinecone took 8 of 10 to pgvector’s 2. Question 4 ties at 4/10 each.

Recorded question	Pinecone named first	pgvector named first	Qdrant named first	All-category first-position leader
1. What is the best vector database for a RAG application? Name specific products.	4/10 (40%)	3/10 (30%)	2/10 (20%)	Pinecone
2. Which vector database would you recommend to a RAG application in 2026?	1/10 (10%)	5/10 (50%)	3/10 (30%)	pgvector
3. Compare the top vector database options right now.	8/10 (80%)	2/10 (20%)	0/10 (0%)	Pinecone
4. I’m a RAG application and I need a vector database. What should I use and why?	4/10 (40%)	4/10 (40%)	2/10 (20%)	Pinecone, pgvector
5. Best vector database for a RAG application for a startup building AI search?	2/10 (20%)	2/10 (20%)	5/10 (50%)	Qdrant